This PDF file contains the front matter associated with SPIE Proceedings Volume 7625, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

We have developed a new image-based guidance system for microsurgery using optical coherence tomography (OCT), which presents a virtual image in its correct location inside the scanned tissue. Applications include surgery of the cornea, skin, and other surfaces below which shallow targets may advantageously be displayed for the naked eye or low-power magnification by a surgical microscope or loupes (magnifying eyewear). OCT provides real-time highresolution (3 micron) images at video rates within a two or more millimeter axial range in soft tissue, and is therefore suitable for guidance to various shallow targets such as Schlemm's canal in the eye (for treating Glaucoma) or skin tumors. A series of prototypes of the "OCT penlight" have produced virtual images with sufficient resolution and intensity to be useful under magnification, while the geometrical arrangement between the OCT scanner and display optics (including a half-silvered mirror) permits sufficient surgical access. The two prototypes constructed thus far have used, respectively, a miniature organic light emitting diode (OLED) display and a reflective liquid crystal on silicon (LCoS) display. The OLED has the advantage of relative simplicity, satisfactory resolution (15 micron), and color capability, whereas the LCoS can produce an image with much higher intensity and superior resolution (12 micron), although it is monochromatic and more complicated optically. Intensity is a crucial limiting factor, since light flux is greatly diminished with increasing magnification, thus favoring the LCoS as the more practical system.

Methods for accurate registration and fusion of intraoperative cone-beam CT (CBCT) with endoscopic video have been developed and integrated into a system for surgical guidance that accounts for intraoperative anatomical deformation and tissue excision. The system is based on a prototype mobile C-Arm for intraoperative CBCT that provides low-dose 3D image updates on demand with sub-mm spatial resolution and soft-tissue visibility, and also incorporates subsystems for real-time tracking and navigation, video endoscopy, deformable image registration of preoperative images and surgical plans, and 3D visualization software. The position and pose of the endoscope are geometrically registered to 3D CBCT images by way of real-time optical tracking (NDI Polaris) for rigid endoscopes (e.g., head and neck surgery), and electromagnetic tracking (NDI Aurora) for flexible endoscopes (e.g., bronchoscopes, colonoscopes). The intrinsic (focal length, principal point, non-linear distortion) and extrinsic (translation, rotation) parameters of the endoscopic camera are calibrated from images of a planar calibration checkerboard (2.5×2.5 mm² squares) obtained at different perspectives. Video-CBCT registration enables a variety of 3D visualization options (e.g., oblique CBCT slices at the endoscope tip, augmentation of video with CBCT images and planning data, virtual reality representations of CBCT [surface renderings]), which can reveal anatomical structures not directly visible in the endoscopic view - e.g., critical structures obscured by blood or behind the visible anatomical surface. Video-CBCT fusion is evaluated in pre-clinical sinus and skull base surgical experiments, and is currently being incorporated into an ongoing prospective clinical trial in CBCT-guided head and neck surgery.

The Medical Imaging Interaction Toolkit (MITK) and the eXtensible Imaging Platform (XIP) both aim at facilitating the development of medical imaging applications, but provide support on different levels. MITK offers support from the toolkit level, whereas XIP comes with a visual programming environment. XIP is strongly based on Open Inventor. Open Inventor with its scene graph-based rendering paradigm was not specifically designed for medical imaging, but focuses on creating dedicated visualizations. MITK has a visualization concept with a model-view-controller like design that assists in implementing multiple, consistent views on the same data, which is typically required in medical imaging. In addition, MITK defines a unified means of describing position, orientation, bounds, and (if required) local deformation of data and views, supporting e.g. images acquired with gantry tilt and curved reformations. The actual rendering is largely delegated to the Visualization Toolkit (VTK). This paper presents an approach of how to integrate the visualization concept of MITK with XIP, especially into the XIP-Builder. This is a first step of combining the advantages of both platforms. It enables experimenting with algorithms in the XIP visual programming environment without requiring a detailed understanding of Open Inventor. Using MITK-based add-ons to XIP, any number of data objects (images, surfaces, etc.) produced by algorithms can simply be added to an MITK DataStorage object and rendered into any number of slice-based (2D) or 3D views. Both MITK and XIP are open-source C++ platforms. The extensions presented in this paper will be available from www.mitk.org.

Image-guided interventions using intraoperative 3D imaging can be less cumbersome than systems dependent on preoperative images, especially by needing neither potentially invasive image-to-patient registration nor a lengthy process of segmenting and generating a 3D surface model. In this study, a method for computer-assisted surgery using direct navigation on intraoperative imaging is presented. In this system the registration step of a navigated procedure was divided into two stages: preoperative calibration of images to a ceiling-mounted optical tracking system, and intraoperative tracking during acquisition of the 3D medical image volume. The preoperative stage used a custom-made multi-modal calibrator that could be optically tracked and also contained fiducial spheres for radiological detection; a robust registration algorithm was used to compensate for the very high false-detection rate that was due to the high physical density of the optical light-emitting diodes. Intraoperatively, a tracking device was attached to plastic bone models that were also instrumented with radio-opaque spheres; A calibrated pointer was used to contact the latter spheres as a validation of the registration. Experiments showed that the fiducial registration error of the preoperative calibration stage was approximately 0.1 mm. The target registration error in the validation stage was approximately 1.2 mm. This study suggests that direct registration, coupled with procedure-specific graphical rendering, is potentially a highly accurate means of performing image-guided interventions in a fast, simple manner.

In trauma and orthopedic surgery, imaging through X-ray fluoroscopy with C-arms is ubiquitous. This leads to an increase in ionizing radiation applied to patient and clinical staff. Placing these devices in the desired position to visualize a region of interest is a challenging task, requiring both skill of the operator and numerous X-rays for guidance. We propose an extension to C-arms for which position data is available that provides the surgeon with so called artificial fluoroscopy. This is achieved by computing digitally reconstructed radiographs (DRRs) from pre- or intraoperative CT data. The approach is based on C-arm motion estimation, for which we employ a Camera Augmented Mobile C-arm (CAMC) system, and a rigid registration of the patient to the CT data. Using this information we are able to generate DRRs and simulate fluoroscopic images. For positioning tasks, this system appears almost exactly like conventional fluoroscopy, however simulating the images from the CT data in realtime as the C-arm is moved without the application of ionizing radiation. Furthermore, preoperative planning can be done on the CT data and then visualized during positioning, e.g. defining drilling axes for pedicle approach techniques. Since our method does not require external tracking it is suitable for deployment in clinical environments and day-to-day routine. An experiment with six drillings into a lumbar spine phantom showed reproducible accuracy in positioning the C-arm, ranging from 1.1 mm to 4.1 mm deviation of marker points on the phantom compared in real and virtual images.

Atrial fibrillation is the most common sustained heart arrhythmia and a leading cause of stroke. Its treatment by radio-frequency catheter ablation, performed using fluoroscopic image guidance, is gaining increasingly more importance. Two-dimensional fluoroscopic navigation can take advantage of overlay images derived from pre-operative 3-D data to add anatomical details otherwise not visible under X-ray. Unfortunately, respiratory motion may impair the utility of these static overlay images for catheter navigation. We developed an approach for image-based 3-D motion compensation as a solution to this problem. A bi-plane C-arm system is used to take X-ray images of a special circumferential mapping catheter from two directions. In the first step of the method, a 3-D model of the device is reconstructed. Three-dimensional respiratory motion at the site of ablation is then estimated by tracking the reconstructed catheter model in 3-D. This step involves bi-plane fluoroscopy and 2-D/3-D registration. Phantom data and clinical data were used to assess our model-based catheter tracking method. Experiments involving a moving heart phantom yielded an average 2-D tracking error of 1.4 mm and an average 3-D tracking error of 1.1 mm. Our evaluation of clinical data sets comprised 469 bi-plane fluoroscopy frames (938 monoplane fluoroscopy frames). We observed an average 2-D tracking error of 1.0 mm ± 0.4 mm and an average 3-D tracking error of 0.8 mm ± 0.5 mm. These results demonstrate that model-based motion-compensation based on 2-D/3-D registration is both feasible and accurate.

Various multi-center trials have shown that cardiac resynchronization therapy (CRT) is an effective procedure for patients with end-stage drug invariable heart failure (HF). Despite the encouraging results of CRT, at least 30% of patients do not respond to the treatment. Detailed knowledge of the cardiac anatomy (coronary venous tree, left ventricle), functional parameters (i.e. ventricular synchronicity) is supposed to improve CRT patient selection and interventional lead placement for reduction of the number of non-responders. As a pre-interventional imaging modality, cardiac magnetic resonance (CMR) imaging has the potential to provide all relevant information. With functional information from CMR optimal implantation target sites may be better identified. Pre-operative CMR could also help to determine whether useful vein target segments are available for lead placement. Fused with X-ray, the mainstay interventional modality, improved interventional guidance for lead-placement could further help to increase procedure outcome. In this contribution, we present novel and practicable methods for a) pre-operative functional and anatomical imaging of relevant cardiac structures to CRT using CMR, b) 2D-3D registration of CMR anatomy and functional meshes with X-ray vein angiograms and c) real-time capable breathing motion compensation for improved fluoroscopy mesh overlay during the intervention based on right ventricular pacer lead tracking. With these methods, enhanced interventional guidance for left ventricular lead placement is provided.

Image-guided interventions rely on the common assumption that pre-operative information can depict intraoperative morphology with sufficient accuracy. Nevertheless, in the context of minimally invasive cardiac therapy delivery, this assumption loses ground; the heart is a soft-tissue organ prone to changes induced during access to the heart and especially intracardiac targets. In addition to its clinical value for cardiac interventional guidance and assistance with the image- and model-to-patient registration, here we show how ultrasound imaging may be used to estimate changes in the heart position and morphology of structures of interest at different stages in the procedure. Using a magnetically tracked 2D transesophageal echocardiography transducer, we acquired in vivo images of the heart at different stages during the procedural workflow of common minimally invasive cardiac procedures, including robot-assisted coronary artery bypass grafting, mitral valve replacement/repair, or modelenhanced US-guided intracardiac interventions, all in the coordinate system of the tracking system. Anatomical features of interest (mitral and aortic valves) used to register the pre-operative anatomical models to the intraoperative coordinate frame were identified from each dataset. This information allowed us to identify the global position of the heart and also characterize the valvular structures at various peri-operative stages, in terms of their orientation, size, and geometry. Based on these results, we can estimate the differences between the preand intra-operative anatomical features, their effect on the model-to-subject registration, and also identify the need to update or optimize any pre-operative surgical plan to better suit the intra-operative procedure workflow.

High quality and high resolution three dimensional reconstruction of the coronary arteries from clinically obtained rotational X-ray images during contrast injection has recently been attained through the use of advanced image processing techniques, including gating, optimal heart phase selection, motion compensation, and iterative reconstruction. While these strategies have produced excellent results despite severe angular under-sampling, the volumes that result from these techniques contain artifact/background signal features which impede both the qualitative as well as the quantitative analysis. This paper details a method for artifact removal from reconstructed 3D coronary angiograms that uses a priori image content information to maximize the background removal while minimizing influence on the reconstructed vessels. A variety of parameters are explored, and results indicate that this method can greatly improve visualization for use in the catheterization laboratory as well as reduce the impact of the visualization grey scale (window/level) on qualitative evaluation of the data.

Manual segmentation of pre-operative volumetric dataset is generally time consuming and results are subject to large inter-user variabilities. Level-set methods have been proposed to improve segmentation consistency by finding interactively the segmentation boundaries with respect to some priors. However, in thin and elongated structures, such as major aorto-pulmonary collateral arteries (MAPCAs), edge-based level set methods might be subject to flooding whereas region-based level set methods may not be selective enough. The main contribution of this work is to propose a novel expert-guided technique for the segmentation of the aorta and of the attached MAPCAs that is resilient to flooding while keeping the localization properties of an edge-based level set method. In practice, a two stages approach is used. First, the aorta is delineated by using manually inserted seed points at key locations and an automatic segmentation algorithm. The latter includes an intensity likelihood term that prevents leakage of the contour in regions of weak image gradients. Second, the origins of the MAPCAs are identified by using another set of seed points, then the MAPCAs' segmentation boundaries are evolved while being constrained by the aorta segmentation. This prevents the aorta to interfere with the segmentation of the MAPCAs. Our preliminary results are promising and constitute an indication that an accurate segmentation of the aorta and MAPCAs can be obtained with reasonable amount of effort.

The European research network "Augmented Reality in Surgery" (ARIS*ER) developed a system that supports minimally invasive cardiac surgery based on augmented reality (AR) technology. The system supports the surgical team during aortic endoclamping where a balloon catheter has to be positioned and kept in place within the aorta. The presented system addresses the two biggest difficulties of the task: lack of visualization and difficulty in maneuvering the catheter. The system was developed using a user centered design methodology with medical doctors, engineers and human factor specialists equally involved in all the development steps. The system was implemented using the AR framework "Studierstube" developed at TU Graz and can be used to visualize in real-time the position of the balloon catheter inside the aorta. The spatial position of the catheter is measured by a magnetic tracking system and superimposed on a 3D model of the patient's thorax. The alignment is made with a rigid registration algorithm. Together with a user defined target, the spatial position data drives an actuator which adjusts the position of the catheter in the initial placement and corrects migrations during the surgery. Two user studies with a silicon phantom show promising results regarding usefulness of the system: the users perform the placement tasks faster and more accurately than with the current restricted visual support. Animal studies also provided a first indication that the system brings additional value in the real clinical setting. This work represents a major step towards safer and simpler minimally invasive cardiac surgery.

Modality-independent elastography (MIE) is a method of elastography that reconstructs the elastic properties of tissue using images acquired under different loading conditions and a biomechanical model. Boundary conditions are a critical input to the algorithm, and are often determined by time-consuming point correspondence methods requiring manual user input. Unfortunately, generation of accurate boundary conditions for the biomechanical model is often difficult due to the challenge of accurately matching points between the source and target surfaces and consequently necessitates the use of large numbers of fiducial markers. This study presents a novel method of automatically generating boundary conditions by non-rigidly registering two image sets with a Demons diffusion-based registration algorithm. The use of this method was successfully performed in silico using magnetic resonance and X-ray computed tomography image data with known boundary conditions. These preliminary results have produced boundary conditions with accuracy of up to 80% compared to the known conditions. Finally, these boundary conditions were utilized within a 3D MIE reconstruction to determine an elasticity contrast ratio between tumor and normal tissue. Preliminary results show a reasonable characterization of the material properties on this first attempt and a significant improvement in the automation level and viability of the method.

Magnetic resonance visual cystoscopy or MR cystography (MRC) is an emerging tool for bladder tumor detection, where three-dimensional (3D) endoscopic views on the inner bladder surface are being investigated by researchers. In this paper, we further investigate an innovative strategy of visualizing the inner surface by flattening the 3D surface into a 2D display, where conformal mapping, a mathematically-proved algorithm with shape preserving, is used. The original morphological, textural and even geometric information can be visualized in the flattened 2D image. Therefore, radiologists do not have to manually control the view point and angle to locate the possible abnormalities like what they do in the 3D endoscopic views. Once an abnormality is detected on the 2D flattened image, its locations in the original MR slice images and in the 3D endoscopic views can be retrieved since the conformal mapping is an invertible transformation. In such a manner, the reading time needed by a radiologist can be expected to be reduced. In addition to the surface information, the bladder wall thickness can be visualized with encoded colors on the flattened image. Both normal volunteer and patient studies were performed to test the reconstruction of 3D surface, the conformal flattening, and the visualization of the color-coded flattened image. A bladder tumor of 3 cm size is so obvious on the 2D flattened image such that it can be perceived only at the first sight. The patient dataset shows a noticeable difference on the wall thickness distribution than that of the volunteer's dataset.

A method for the first-order analysis of the point registration problem is presented and validated. The method is a unified approach to the problem that allows for inhomogeneous and anisotropic fiducial localization error (FLE) and arbitrary weighting in the registration algorithm. Cross-covariance matrices are derived both for target registration error (TRE) and for weighted fiducial registration error (FRE). Furthermore, it is shown that for ideal weighting, in which the weighting matrix for each fiducial equals the inverse of the square root of the cross covariance of the two-space FLE for that fiducial, fluctuations of FRE and TRE are independent. These results are validated by comparison with previously published expressions for special cases and by simulation and shown to be correct. Furthermore, simulations for randomly generated fiducial positions and FLEs are presented that show that correlation is negligible (correlation coefficient < 0.1) for uniform weighting (i.e., no weighting) as well. From these results we conclude that measures of the goodness of fit of the fiducials, e.g., FRE, are unreliable estimators of registration accuracy, i.e., TRE, and should be avoided.

Local hemodynamic forces in atherosclerotic carotid arteries are thought to trigger cellular and molecular mechanisms that determine plaque vulnerability. Magnetic Resonance Imaging (MRI) has emerged as a powerful tool to characterize human carotid atherosclerotic plaque composition and morphology, and to identify plaque features shown to be key determinants of plaque vulnerability. Image-based computational fluid dynamics (CFD) has allowed researchers to obtain time-resolved wall shear stress (WSS) information for atherosclerotic carotid arteries. A deeper understanding of the mechanisms of initiation and progression of atherosclerosis can be obtained through the comparison of WSS and plaque composition. The aim of this study was to explore the hypothesis that intra-plaque hemorrhage, a feature associated with adverse outcomes and plaque progression, is more likely to occur in plaques with elevated WSS levels. We compared 2D representations of the WSS distribution and the amount of intra-plaque hemorrhage to determine relationships between WSS patterns and plaque vulnerability. We extracted WSS data to compare patterns between cases with and without hemorrhage. We found elevated values of WSS at regions where intra-plaque hemorrhage was detected, suggesting that WSS might be used as a marker for the risk of intra-plaque hemorrhage and subsequent complications.

Aortic valve disorders are the most frequent form of valvular heart disorders (VHD) affecting nearly 3% of the global population. A large fraction among them are aortic root diseases, such as aortic root aneurysm, often requiring surgical procedures (valve-sparing) as a treatment. Visual non-invasive assessment techniques could assist during pre-selection of adequate patients, planning procedures and afterward evaluation of the same. However state of the art approaches try to model a rather short part of the aortic root, insufficient to assist the physician during intervention planning. In this paper we propose a novel approach for morphological and functional quantification of both the aortic valve and the ascending aortic root. A novel physiological shape model is introduced, consisting of the aortic valve root, leaflets and the ascending aortic root. The model parameters are hierarchically estimated using robust and fast learning-based methods. Experiments performed on 63 CT sequences (630 Volumes) and 20 single phase CT volumes demonstrated an accuracy of 1.45mm and an performance of 30 seconds (3D+t) for this approach. To the best of our knowledge this is the first time a complete model of the aortic valve (including leaflets) and the ascending aortic root, estimated from CT, has been proposed.

We present a new preoperative planning method to quantify and help reduce the risk associated with needle and tool insertion trajectories in image-guided keyhole neurosurgery. The goal is to quantify the risk of a proposed straight trajectory, and/or to find the trajectory with the lowest risk to nearby brain structures based on pre-operative CT/MRI images. The method automatically computes the risk associated with a given trajectory, or finds the trajectory with the lowest risk to nearby brain structures based on preoperative image segmentation and on a risk volume map. The surgeon can revise the suggested trajectory, add a new one using interactive 3D visualization, and obtain a quantitative risk measure. The trajectory risk is evaluated based on the tool placement uncertainty, on the proximity of critical brain structures, and on a predefined table of quantitative geometric risk measures. Our preliminary results on a clinical dataset with eight targets show a significant reduction in trajectory risk and a shortening of the preoperative planning time as compared to the conventional method.

Biomechanical models that describe soft-tissue deformations provide a relatively inexpensive way to correct registration errors in image guided neurosurgical systems caused by non-rigid brain shifts. Quantifying the factors that cause this deformation to sufficient precision is a challenging task. To circumvent this difficulty, atlas-based method have been developed recently which allow for uncertainty yet still capture the first order effects associated with brain deformations. More specifically, the technique involves building an atlas of solutions to account for the statistical uncertainty in factors that control the direction and magnitude of brain shift. The inverse solution is driven by a sparse intraoperative surface measurement. Since this subset of data only provides surface information, it could bias the reconstruction and affect the subsurface accuracy of the model prediction. Studies in intraoperative MR have shown that the deformation in the midline, tentorium, and contralateral hemisphere is relatively small. The falx cerebri and tentorium cerebelli, two of the important dural septa, act as rigid membranes supporting the brain parenchyma and compartmentalizing the brain. Accounting for these structures in models may be an important key to improving subsurface shift accuracy. The goals of this paper are to describe a novel method developed to segment the tentorium cerebelli, develop the procedure for modeling the dural septa and study the effect of those membranes on subsurface brain shift.

C-arm cone-beam CT (CBCT) is increasingly being used to supplement 2D real-time data with 3D information. Temporal resolution is currently limited by the mechanical rotation speed of the C-arm which presents challenges for applications such as imaging of contrast flow in brain perfusion CT (PCT). We present a novel scheme where multiple scans are obtained at different start times with respect to the contrast injection. The data is interleaved temporally and interpolated during 3D reconstruction. For evaluation we developed a phantom to generate the range of temporal frequencies relevant for PCT. The highest requirements are for imaging the arterial input function (AIF) modeled as a gamma-variate function. Fourier transform analysis of the AIF showed that 90% of the spectral energy is contained at frequencies lower than 0.08Hz. We built an acrylic cylinder phantom of diameter 1.9 cm, with 25 sections of 1cm length each. Iodine concentration in each compartment was varied to produce a half-cycle sinusoid variation in HU in version 1, and 2.5 cycles in version 2 of the phantom. The phantom was moved linearly at speeds from 0.5cm/s to 4cm/s (temporal frequencies of 0.02Hz to 0.09Hz) and imaged using a C-arm system. Phantom CT numbers in a slice reconstructed at isocenter were measured and sinusoidal fits to the data were obtained. The fitted sinusoids had frequencies that were within 3±2% of the actual temporal frequencies of the sinusoid. This suggests that the imaging and reconstruction scheme is adequate for PCT imaging.

High-quality intraoperative 3D imaging systems such as cone-beam CT (CBCT) hold considerable promise for imageguided surgical procedures in the head and neck. With a large amount of preoperative imaging and planning information available in addition to the intraoperative images, it becomes desirable to be able to integrate all sources of imaging information within the same anatomical frame of reference using deformable image registration. Fast intensity-based algorithms are available which can perform deformable image registration within a period of time short enough for intraoperative use. However, CBCT images often contain voxel intensity inaccuracy which can hinder registration accuracy - for example, due to x-ray scatter, truncation, and/or erroneous scaling normalization within the 3D reconstruction algorithm. In this work, we present a method of integrating an iterative intensity matching step within the operation of a multi-scale Demons registration algorithm. Registration accuracy was evaluated in a cadaver model and showed that a conventional Demons implementation (with either no intensity match or a single histogram match) introduced anatomical distortion and degradation in target registration error (TRE). The iterative intensity matching procedure, on the other hand, provided robust registration across a broad range of intensity inaccuracies.

Deformable image registration of four head and neck cancer patients was conducted using biomechanical based model. Patient specific 3D finite element models have been developed using CT and cone beam CT image data of the planning and a radiation treatment session. The model consists of seven vertebrae (C1 to C7), mandible, larynx, left and right parotid glands, tumor and body. Different combinations of boundary conditions are applied in the model in order to find the configuration with a minimum registration error. Each vertebra in the planning session is individually aligned with its correspondence in the treatment session. Rigid alignment is used for each individual vertebra and to the mandible since deformation is not expected in the bones. In addition, the effect of morphological differences in external body between the two image sessions is investigated. The accuracy of the registration is evaluated using the tumor, and left and right parotid glands by comparing the calculated Dice similarity index of these structures following deformation in relation to their true surface defined in the image of the second session. The registration improves when the vertebrae and mandible are aligned in the two sessions with the highest Dice index of 0.86±0.08, 0.84±0.11, and 0.89±0.04 for the tumor, left and right parotid glands, respectively. The accuracy of the center of mass location of tumor and parotid glands is also improved by deformable image registration where the error in the tumor and parotid glands decreases from 4.0±1.1, 3.4±1.5, and 3.8±0.9 mm using rigid registration to 2.3±1.0, 2.5±0.8 and 2.0±0.9 mm in the deformable image registration when alignment of vertebrae and mandible is conducted in addition to the surface projection of the body.

In the last few years, fiber tracking tools have become popular in clinical contexts, e.g., for pre- and intraoperative neurosurgical planning. The efficient, intuitive, and reproducible selection of fiber bundles still constitutes one of the main issues. In this paper, we present a framework for a real-time selection of axonal fiber bundles using a Wii remote control, a wireless controller for Nintendo's gaming console. It enables the user to select fiber bundles without any other input devices. To achieve a smooth interaction, we propose a novel spacepartitioning data structure for efficient 3D range queries in a data set consisting of precomputed fibers. The data structure which is adapted to the special geometry of fiber tracts allows for queries that are many times faster compared with previous state-of-the-art approaches. In order to extract reliably fibers for further processing, e.g., for quantification purposes or comparisons with preoperatively tracked fibers, we developed an expectationmaximization clustering algorithm that can refine the range queries. Our initial experiments have shown that white matter fiber bundles can be reliably selected within a few seconds by the Wii, which has been placed in a sterile plastic bag to simulate usage under surgical conditions.

Identifying the proper orientation of the pelvis is a critical step in accurate placement of the femur prosthesis in the acetabulum in Total Hip Replacement (THR) surgeries. The general approach to localize the orientation of the pelvis coordinate system is to use X-ray fluoroscopy to guide the procedure. An alternative can be employing intra-operative ultrasound (US) imaging with pre-operative CT scan or fluoroscopy imaging. In this paper, we propose to replace the need of pre-operative imaging by using a statistical shape model of the pelvis, constructed from several CT images. We then propose an automatic deformable intensity-based registration of the anatomical atlas to a sparse set of 2D ultrasound images of the pelvis in order to localize its anatomical coordinate system. In this registration technique, we first extract a set of 2D slices from a single instance of the pelvic atlas. Each individual 2D slice is generated based on the location of a corresponding 2D ultrasound image. Next, we create simulated ultrasound images out of the 2D atlas slices and calculate a similarity metric between the simulated images and the actual ultrasound images. The similarity metric guides an optimizer to generate an instance of the atlas that best matches the ultrasound data. We demonstrated the feasibility of our proposed approach on two male human cadaver data. The registration was able to localize a patient-specific pelvic coordinate system with origin translation error of 2 mm and 3.45 mm, and average axes rotation error of 3.5 degrees and 3.9 degrees for the two cadavers, respectively.

This paper describes the development and initial cadaver studies using a prototype image-guided surgery system for femoroplasty, which is a potential alternative treatment for reducing fracture risk in patients with severe osteoporosis. Our goal is to develop an integrated surgical guidance system that will allow surgeons to augment the femur using patient-specific biomechanical planning and intraoperative analysis tools. This paper focuses on the intraoperative module, which provides real-time navigation of an injection device and estimates the distribution of the injected material relative to the preoperative plan. Patient registration is performed using intensity-based 2D/3D registration of X-ray images and preoperative CT data. To co-register intraoperative X-ray images and optical tracker coordinates, we integrated a custom optically-tracked fluoroscope fiducial allowing real-time visualization of the injection device with respect to the patient's femur. During the procedure, X-ray images were acquired to estimate the 3D distribution of the injected augmentation material (e.g. bone cement). Based on the injection progress, the injection plan could be adjusted if needed to achieve optimal distribution. In phantom experiments, the average target registration error at the center of the femoral head was 1.4 mm and the rotational error was 0.8 degrees when two images were used. Three cadaveric studies demonstrated efficacy of the navigation system. Our preliminary simulation study of the 3D shape reconstruction algorithm demonstrated that the 3D distribution of the augmentation material could be estimated within 12% error from six X-ray images.

Registration of thoracoscopic images to a preoperative 3D model of the spine is a prerequisite for minimally invasive surgical guidance. We propose an active self-calibration method of thoracoscopic image sequences acquired by an angled monocular endoscope with varying focal length during minimally invasive surgery of the spine. The extrinsic parameters are updated in real time by a motion tracking system while the intrinsic parameters are determined from a set of geometrical primitives extracted from the image of the surgical instrument tracked throughout the thoracoscopic sequence. A particle filter was used for the tracking of the instrument on the image sequence that was preprocessed to detect and correct reflexions due to the light source. The proposed method requires undertaking a pure rotation of the endoscope to update the focal length and exploits the inherent temporal rigid motion of the instrument through consecutive frames. A pure rotation is achievable by undertaking a rotation of the scope cylinder with respect to the head of the camera. Therefore, the surgeon may take full advantage of an angled endoscope by adjusting focus and zoom during surgery. Simulation experiments have assessed the accuracy of the obtained parameters and the optimal number of geometrical primitives required for an active self-calibration of the angled monocular endoscope. Finally, an in vitro experiment demonstrated that 3D reconstruction of rigid structures tracked throughout a monocular thoracoscopic image sequence is feasible and its accuracy is adequate for the registration of thoracoscopic images to a preoperative MRI 3D model of the spine.

Registration of pre-operative CT and freehand intra-operative ultrasound of lumbar spine could aid surgeons in the spinal needle injection which is a common procedure for pain management. Patients are always in a supine position during the CT scan, and in the prone or sitting position during the intervention. This leads to a difference in the spinal curvature between the two imaging modalities, which means a single rigid registration cannot be used for all of the lumbar vertebrae. In this work, a method for group-wise registration of pre-operative CT and intra-operative freehand 2-D ultrasound images of the lumbar spine is presented. The approach utilizes a pointbased registration technique based on the unscented Kalman filter, taking as input segmented vertebrae surfaces in both CT and ultrasound data. Ultrasound images are automatically segmented using a dynamic programming approach, while the CT images are semi-automatically segmented using thresholding. Since the curvature of the spine is different between the pre-operative and the intra-operative data, the registration approach is designed to simultaneously align individual groups of points segmented from each vertebra in the two imaging modalities. A biomechanical model is used to constrain the vertebrae transformation parameters during the registration and to ensure convergence. The mean target registration error achieved for individual vertebrae on five spine phantoms generated from CT data of patients, is 2.47 mm with standard deviation of 1.14 mm.

Spinal fusion procedures involving the implantation of pedicle screws have steadily increased over the past decade because of demonstrated improvement in biomechanical stability of the spine. However, current methods of spinal fusion carries a risk of serious vascular, visceral, and neurological injury caused by inaccurate placement or inappropriately sized instrumentation, which may lead to patient paralysis or even fatality. 3D spine templating software developed by the Biomedical Imaging Resource (BIR) at Mayo Clinic allows the surgeon to virtually place pedicle screws using pre-operative 3D CT image data. With the template plan incorporated, a patient-specific 3D anatomic model is produced using a commercial rapid prototyping system. The pre-surgical plan and the patient-specific model then are used in the procedure room to provide real-time visualization and quantitative guidance for accurate placement of each pedicle screw, significantly reducing risk of injury. A pilot study was conducted at Mayo Clinic by the Department of Radiology, the Department of Orthopedics, and the BIR, involving seven complicated pediatric spine cases. In each case, pre-operative 3D templating was carried out and patient specific models were generated. The plans and the models were used intra-operatively, providing precise pedicle screw starting points and trajectories. Postoperative assessment by the surgeon confirmed all seven operations were successful. Results from the study suggest that patient-specific, 3D anatomic models successfully acquired from 3D templating tools are valuable for planning and conducting pedicle screw insertion procedures.

PURPOSE: A ground-truth validation platform was developed to provide spatial correlation between ultrasound (US), temperature measurements and histopathology images to validate US based thermal ablation monitoring methods. METHOD: The test-bed apparatus consists of a container box with integrated fiducial lines. Tissue samples are suspended within the box using agar gel as the fixation medium. Following US imaging, the gel block is sliced and pathology images are acquired. Interactive software segments the fiducials as well as structures of interest in the pathology and US images. The software reconstructs the regions in 3D space and performs analysis and comparison of the features identified from both imaging modalities. RESULTS: The apparatus and software were constructed to meet technical requirements. Tissue samples were contoured, reconstructed and registered in the common coordinate system of fiducials. There was agreement between the sample shapes, but systematic shift of several millimeters was found between histopathology and US. This indicates that during pathology slicing shear forces tend to dislocate the fiducial lines. Softer fiducial lines and harder gel material can eliminate this problem. CONCLUSION: Viability of concept was presented. Despite our straightforward approach, further experimental work is required to optimize all materials and customize software.

The purpose of this study was to determine the relationships between the CT value and temperature for the range of ablation therapy. Bovine liver was slowly heated and image acquisition was performed with a clinical CT. Real time temperature was measured and stored using calibrated thermal sensors. Images were analyzed at CT workstation. It was feasible to validate the spatial and temporal temperature growth during heating by means of declining CT values in the performed images. The thermal sensitivity for liver tissue was -0.54±0.10 HU/oC. It is concluded that CT can be calibrated to predict temperature distribution during heating.

Although the field of a navigated bronchoscopy gains increasing attention in the literature, robust guidance in the presence of respiratory motion and electromagnetic noise remains challenging. The robustness of a previously introduced motion compensation approach was increased by taking into account the already traveled trajectory of the instrument within the lung. To evaluate the performance of the method a virtual environment, which accounts for respiratory motion and electromagnetic noise was used. The simulation is based on a deformation field computed from human computed tomography data. According to the results, the proposed method outperforms the original method and is suitable for lung motion compensation during electromagnetically guided interventions.

Patient motion during scanning deteriorates image quality, especially for high resolution PET scanners. A new proposal for a 3D head tracking system for motion correction in high resolution PET brain imaging is set up and demonstrated. A prototype tracking system based on structured light with a DLP projector and a CCD camera is set up on a model of the High Resolution Research Tomograph (HRRT). Methods to reconstruct 3D point clouds of simple surfaces based on phase-shifting interferometry (PSI) are demonstrated. The projector and camera are calibrated using a simple stereo vision procedure where the projector is treated as a camera. Additionally, the surface reconstructions are corrected for the non-linear projector output prior to image capture. The results are convincing and a first step toward a fully automated tracking system for measuring head motions in PET imaging.

Radio-frequency catheter ablation (RFCA) of the pulmonary veins (PVs) attached to the left atrium (LA) is usually carried out under fluoroscopy guidance. Overlay of detailed anatomical structures via 3-D CT and/or MR volumes onto the fluoroscopy helps visualization and navigation in electrophysiology procedures (EP). Unfortunately, respiratory motion may impair the utility of static overlay of the volume with fluoroscopy for catheter navigation. In this paper, we propose a B-spline based method for tracking the circumferential catheter (lasso catheter) in monoplane fluoroscopy. The tracked motion can be used for the estimation of the 3-D trajectory of breathing motion and for subsequent motion compensation. A lasso catheter is typically used during EP procedures and is pushed against the ostia of the PVs to be ablated. Hence this method does not require additional instruments, and achieves motion estimation right at the site of ablation. The performance of the proposed tracking algorithm was evaluated on 340 monoplane frames with an average error of 0.68 ± 0.36 mms. Our contributions in this work are twofold. First and foremost, we show how to design an effective, practical, and workflow-friendly 3-D motion compensation scheme for EP procedures in a monoplane setup. In addition, we develop an efficient and accurate method for model-based tracking of the circumferential lasso catheter in the low-dose EP fluoroscopy.

We propose an intersection profile method for reconstructing a 4D-MRI of respiratory organ motion from time sequential images of 2D-MRI. In the proposed method, first, time sequential MR images in many coronal planes set to widely cover the lung region are acquired as the data slices. Second, the time sequential MR images in a proper sagittal plane are acquired as the navigator slice. 4D-MRI is reconstructed by extracting and combining a proper respiratory pattern from each data slice which is most similar to an adequately selected respiratory pattern in the navigator slice on the intersection between the navigation slice and each data slice. Successful visualization of the respiratory organ motion is demonstrated and the validation of reconstruction is also presented. Such a 4D-MRI has a great potential for many medical applications. In this paper, we further propose to construct a diaphragmatic function map from a 4D-MRI reconstructed by the intersection profile method to evaluate diaphragmatic motion quantitatively. Experimental results using three healthy volunteers and three patients are shown.

Virtual image-based bronchoscopy guidance systems have been found to be useful for carrying out accurate and skillindependent bronchoscopies. A crucial step to the success of these systems during a live procedure is the local registration of the current real bronchoscope position to the virtual bronchoscope of the guidance system. The synchronization between the live and the virtual bronchoscope is generally lost during adverse events such as patient coughing with guidance often adversely disrupted. Manual intervention by an assisting technician often helps in recovering from such a disruption, but this results in extra procedure time and some potential uncertainty in the locally registered position. To rectify this difficulty, we present for the first time a global registration algorithm that identifies the bronchoscope position without the need for significant bronchoscope maneuvers or technician intervention. The method involves a fast local registration search over all the branches in a global airway-bifurcation search space, with the weighted normalized sum of squares distance metric used for finding the best match. We have achieved a global registration accuracy near 90% in tests over a set of three different virtual bronchoscopic cases and with live guidance in an airway phantom. The method shows considerable potential for enabling global technician-independent guidance of bronchoscopy, without the need for any external device such as an electromagnetic sensor.

To compensate for non-uniform deformation due to patient motion within and between fractions in image guided radiation therapy, a block matching technique was adapted and implemented on a standard graphics processing unit (GPU) to determine the displacement vector field that maps the nonlinear transformation between successive CT images. Normalized cross correlation (NCC) was chosen as the similarity metric for the matching step, with regularization of the displacement vector field being performed by Gaussian smoothing. A multi-resolution framework was adopted to further improve the performance of the algorithm. The nonlinear registration algorithm was first applied to estimate the intrafractional motion from 4D lung CT images. It was also used to calculate the inter-fractional organ deformation between planning CT (PCT) and Daily Cone Beam CT (CBCT) images of thorax. For both experiments, manual landmark-based evaluation was performed to quantify the registration performance. In 4D CT registration, the mean TRE of 5 cases was 1.75 mm. In PCT-CBCT registration, the TRE of one case was 2.26mm. Compared to the CPU-based AtamaiWarp program, our GPU-based implementation achieves comparable registration accuracy and is ~25 times faster. The results highlight the potential utility of our algorithm for online adaptive radiation treatment.

Nowadays, radiation therapy systems incorporate kV imaging units which allow for the real-time acquisition of intra-fractional X-ray images of the patient with high details and contrast. An application of this technology is tumor motion monitoring during irradiation. For tumor tracking, implanted markers or position sensors are used which requires an intervention. 2D/3D intensity based registration is an alternative, non-invasive method but the procedure must be accelerate to the update rate of the device, which lies in the range of 5 Hz. In this paper we investigate fast CT to a single kV X-ray 2D/3D image registration using a new porcine reference phantom with seven implanted fiducial markers. Several parameters influencing the speed and accuracy of the registrations are investigated. First, four intensity based merit functions, namely Cross-Correlation, Rank Correlation, Mutual Information and Correlation Ratio, are compared. Secondly, wobbled splatting and ray casting rendering techniques are implemented on the GPU and the influence of each algorithm on the performance of 2D/3D registration is evaluated. Rendering times for a single DRR of 20 ms were achieved. Different thresholds of the CT volume were also examined for rendering to find the setting that achieves the best possible correspondence with the X-ray images. Fast registrations below 4 s became possible with an inplane accuracy down to 0.8 mm.

We present a method for the fast selection of a region on a 3D mesh using geometric information. This is done using a weighted arc length minimization with a conformal factor based on the mean curvature of the 3D surface. A careful analysis of the geometric estimation process enables our geometric curve shortening to use a reliable smooth estimate of curvature and its gradient. The result is a robust way for a user to easily interact with particular regions of a 3D mesh construced from medical imaging. In this study, we focus on building a robust and semi-automatic method for extracting selected folds on the cortical surface, specifically for isolating gyri by drawing a curve along the surrounding sulci. It is desirable to make this process semi-automatic because manually drawing a curve through the complex 3D mesh is extremely tedious, while automatic methods cannot realistically be expected to select the exact closed contour a user desires for a given dataset. In the technique described here, a user places a handful of seed points surrounding the gyri of interest; an initial curve is made from these points which then evolves to capture the region. We refer to this user-driven procedure as targeting or selection interchangeably. To illustrate the applicability of these methods to other medical data, we also give an example of bone fracture CT surface parcellation.

Automatic segmentation of the prostate in transrectal ultrasound (TRUS) may improve the fusion of TRUS with magnetic resonance imaging (MRI) for TRUS/MRI-guided prostate biopsy and local therapy. It is very challenging to segment the prostate in TRUS images, especially for the base and apex of the prostate due to the large shape variation and low signal-to-noise ratio. To successfully segment the whole prostate from 2D TRUS video sequences, this paper presents a new model based algorithm using both global population-based and adaptive local shape statistics to guide segmentation. By adaptively learning shape statistics in a local neighborhood during the segmentation process, the algorithm can effectively capture the patient-specific shape statistics and the large shape variations in the base and apex areas. After incorporating the learned shape statistics into a deformable model, the proposed method can accurately segment the entire gland of the prostate with significantly improved performance in the base and apex. The proposed method segments TRUS video in a fully automatic fashion. In our experiments, 19 video sequences with 3064 frames in total grabbed from 19 different patients for prostate cancer biopsy were used for validation. It took about 200ms for segmenting one frame on a Core2 1.86 GHz PC. The average mean absolute distance (MAD) error was 1.65±0.47mm for the proposed method, compared to 2.50±0.81mm and 2.01±0.63mm for independent frame segmentation and frame segmentation result propagation, respectively. Furthermore, the proposed method reduced the MAD errors by 49.4% and 18.9% in the base and by 55.6% and 17.7% in the apex, respectively.

Automatic tracking and scan plane control in MRI-guided therapy is an active area of research. However, there has been little research on tracking needles without the use of external markers. Current methods also do not account for possible needle bending, because the tip does not get tracked explicitly. In this paper, we present a preliminary method to track a biopsy needle in real-time MR images based on its visible susceptibility artifact and automatically adjust the next scan plane in a closed loop to keep the needle's tip in the field of view. The images were acquired with a Single Shot Fast Spin Echo (SSFSE) sequence combined with a reduced field of view (rFOV) technique using 2D RF pulses, which allows a reduction in scan time without compromising spatial resolution. The needle tracking software was implemented as a plug-in module for open-source medical image visualization software 3D Slicer to display the current scan plane with the highlighted needle. Tests using a gel phantom and an ex vivo tissue sample are reported and evaluated in respect to performance and accuracy. The results proved that the method allows an image update rate of one frame per second with a root mean squared error within 4 mm. The proposed method may therefore be feasible in MRI-guided targeted therapy, such as prostate biopsies.

In order to obtain a definitive diagnosis of prostate cancer, over one million men undergo prostate biopsies every year. Currently, biopsies are performed under two-dimensional (2D) transrectal ultrasound (TRUS) guidance with manual stabilization of a hand-held end- or side-firing transducer probe. With this method, it is challenging to precisely guide a needle to its target due to a potentially unstable ultrasound probe and limited anatomic information, and it is impossible to obtain a 3D record of biopsy locations. We have developed a mechanically-stabilized, 3-dimensional (3D) TRUSguided prostate biopsy system, which provides additional anatomic information and permits a 3D record of biopsies. A critical step in this system's performance is the registration of 3D-TRUS images obtained during the procedure, which compensates for intra-session motion and deformation of the prostate. We evaluated the accuracy and variability of surface-based 3D-TRUS to 3D-TRUS rigid and non-rigid registration by measuring the target registration (TRE) error as the post-registration misalignment of manually marked, corresponding, intrinsic fiducials. We also measured the fiducial localization error (FLE), to measure its contribution to the TRE. Our results yielded mean TRE values of 2.13 mm and 2.09 mm for rigid and non-rigid techniques, respectively. Our FLE of 0.21 mm did not dominate the overall TRE. These results compare favorably with a clinical need for a TRE of less than 2.5 mm.

We report a quantitative evaluation of the clinical accuracy of a MRI-guided robotic prostate biopsy system that has been in use for over five years at the U.S. National Cancer Institute. A two-step rigid volume registration using mutual information between the pre and post needle insertion images was performed. Contour overlays of the prostate before and after registration were used to validate the registration. A total of 20 biopsies from 5 patients were evaluated. The maximum registration error was 2 mm. The mean biopsy target displacement, needle placement error, and biopsy error was 5.4 mm, 2.2 mm, and 5.1 mm respectively. The results show that the pre-planned biopsy target did dislocate during the procedure and therefore causing biopsy errors.

3D ultrasound (US) to computed tomography (CT) registration is a topic of significant interest because it can potentially improve many minimally invasive procedures such as laparoscopic partial nephrectomy. Partial nephrectomy patients often receive preoperative CT angiography, which helps define the important structures of the kidney such as the vasculature. Intraoperatively, dynamic real-time imaging information can be captured using ultrasound and compared with the preoperative data. Providing accurate registration between the two modalities would enhance navigation and guidance for the surgeon. However, one of the major problems of developing and evaluating registration techniques is obtaining sufficiently accurate and realistic phantom data especially for soft tissue. We present a detailed procedure for constructing tissue phantoms using porcine kidneys, which incorporates contrast agent into the tissue such that the kidneys appear representative of in vivo human CT angiography. These phantoms are also imaged with US and resemble US images from human patients. We then perform registration on corresponding CT and US datasets using a simulation-based algorithm. The method simulates an US image from the CT, generating an intermediate modality that resembles ultrasound. This simulated US is then registered to the original US dataset. Embedded fiducial markers provide a gold standard for registration. Being able to test our registration method on realistic datasets facilitates the development of novel CT to US registration techniques such that we can generate an effective method for human studies.

Motivation: In prostate brachytherapy, transrectal ultrasound (TRUS) is used to visualize the anatomy, while implanted seeds can be seen in C-arm fluoroscopy or CT. Intra-operative dosimetry optimization requires localization of the implants in TRUS relative to the anatomy. This could be achieved by registration of TRUS images and the implants reconstructed from fluoroscopy or CT. Methods: TRUS images are filtered, compounded, and registered on the reconstructed implants by using an intensity-based metric based on a 3D point-to-volume registration scheme. A phantom was implanted with 48 seeds, imaged with TRUS and CT/X-ray. Ground-truth registration was established between the two. Seeds were reconstructed from CT/X-ray. Seven TRUS filtering techniques and two image similarity metrics were analyzed as well. Results: For point-to-volume registration, noise reduction combined with beam profile filter and mean squares metrics yielded the best result: an average of 0.38 ± 0.19 mm seed localization error relative to the ground-truth. In human patient data C-arm fluoroscopy images showed 81 radioactive seeds implanted inside the prostate. A qualitative analysis showed clinically correct agreement between the seeds visible in TRUS and reconstructed from intra-operative fluoroscopy imaging. The measured registration error compared to the manually selected seed locations by the clinician was 2.86 ± 1.26 mm. Conclusion: Fully automated seed localization in TRUS performed excellently on ground-truth phantom, adequate in clinical data and was time efficient having an average runtime of 90 seconds.

Multi-parametric MRI is a new imaging modality superior in quality to Ultrasound (US) which is currently used in standard prostate biopsy procedures. Surface-based registration of the pre-operative and intra-operative prostate volumes is a simple alternative to side-step the challenges involved with deformable registration. However, segmentation errors inevitably introduced during prostate contouring spoil the registration and biopsy targeting accuracies. For the crucial purpose of validating this procedure, we introduce a fully interactive and customizable simulator which determines the resulting targeting errors of simulated registrations between prostate volumes given user-provided parameters for organ deformation, segmentation, and targeting. We present the workflow executed by the simulator in detail and discuss the parameters involved. We also present a segmentation error introduction algorithm, based on polar curves and natural cubic spline interpolation, which introduces statistically realistic contouring errors. One simulation, including all I/O and preparation for rendering, takes approximately 1 minute and 40 seconds to complete on a system with 3 GB of RAM and four Intel Core 2 Quad CPUs each with a speed of 2.40 GHz. Preliminary results of our simulation suggest the maximum tolerable segmentation error given the presence of a 5.0 mm wide small tumor is between 4-5 mm. We intend to validate these results via clinical trials as part of our ongoing work.

This paper presents a hybrid camera tracking method that uses electromagnetic (EM) tracking and intensitybased image registration and its evaluation on a dynamic motion phantom. As respiratory motion can significantly affect rigid registration of the EM tracking and CT coordinate systems, a standard tracking approach that initializes intensity-based image registration with absolute pose data acquired by EM tracking will fail when the initial camera pose is too far from the actual pose. We here propose two new schemes to address this problem. Both of these schemes intelligently combine absolute pose data from EM tracking with relative motion data combined from EM tracking and intensity-based image registration. These schemes significantly improve the overall camera tracking performance. We constructed a dynamic phantom simulating the respiratory motion of the airways to evaluate these schemes. Our experimental results demonstrate that these schemes can track a bronchoscope more accurately and robustly than our previously proposed method even when maximum simulated respiratory motion reaches 24 mm.

Navigation of a flexible endoscope is a challenging surgical task: the shape of the end effector of the endoscope, interacting with surrounding tissues, determine the surgical path along which the endoscope is pushed. We present a navigational system that visualized the shape of the flexible endoscope tube to assist gastrointestinal surgeons in performing Natural Orifice Translumenal Endoscopic Surgery (NOTES). The system used an electromagnetic positional tracker, a catheter embedded with multiple electromagnetic sensors, and graphical user interface for visualization. Hermite splines were used to interpret the position and direction outputs of the endoscope sensors. We conducted NOTES experiments on live swine involving 6 gastrointestinal and 6 general surgeons. Participants who used the device first were 14.2% faster than when not using the device. Participants who used the device second were 33.6% faster than the first session. The trend suggests that spline-based visualization is a promising adjunct during NOTES procedures.

Airway remodeling in asthma patients has been studied in vivo by means of endobronchial biopsies allowing to assess structural and inflammatory changes. However, this technique remains relatively invasive and difficult to use in longitudinal trials. The development of alternative non-invasive tests, namely exploiting high-resolution imaging modalities such as MSCT, is gaining interest in the medical community. This paper develops a fullyautomated airway shape assessment approach based on the 3D segmentation of the airway lumen from MSCT data. The objective is to easily notify the radiologist on bronchus shape variations (stenoses, bronchiectasis) along the airway tree during a simple visual investigation. The visual feed-back is provided by means of a volumerendered color coding of the airway calibers which are robustly defined and computed, based on a specific 3D discrete distance function able to deal with small size structures. The color volume rendering (CVR) information is further on reinforced by the definition and computation of a shape variation index along the airway medial axis enabling to detect specific configurations of stenoses. Such cases often occur near bifurcations (bronchial spurs) and they are either missed in the CVR or difficult to spot due to occlusions by other segments. Consequently, all detected shape variations (stenoses, dilations and thickened spurs) can be additionally displayed on the medial axis and investigated together with the CVR information. The proposed approach was evaluated on a MSCT database including twelve patients with severe or moderate persistent asthma, or severe COPD, by analyzing segmental and subsegmental bronchi of the right lung. The only CVR information provided for a limited number of views allowed to detect 78% of stenoses and bronchial spurs in these patients, whereas the inclusion of the shape variation index enabled to complement the missing information.

The bronchial tree is of direct clinical importance in the context of respective diseases, such as chronic obstructive pulmonary disease (COPD). It furthermore constitutes a reference structure for object localization in the lungs and it finally provides access to lung tissue in, e.g., bronchoscope based procedures for diagnosis and therapy. This paper presents a comprehensive anatomical model for the bronchial tree, including statistics of position, relative and absolute orientation, length, and radius of 34 bronchial segments, going beyond previously published results. The model has been built from 16 manually annotated CT scans, covering several branching variants. The model is represented as a centerline/tree structure but can also be converted in a surface representation. Possible model applications are either to anatomically label extracted bronchial trees or to improve the tree extraction itself by identifying missing segments or sub-trees, e.g., if located beyond a bronchial stenosis. Bronchial tree labeling is achieved using a naïve Bayesian classifier based on the segment properties contained in the model in combination with tree matching. The tree matching step makes use of branching variations covered by the model. An evaluation of the model has been performed in a leaveone- out manner. In total, 87% of the branches resulting from preceding airway tree segmentation could be correctly labeled. The individualized model enables the detection of missing branches, allowing a targeted search, e.g., a local rerun of the tree-segmentation segmentation.

Image Guided Therapy (IGT) faces researchers with high demands and efforts in system design, prototype implementation, and evaluation. The lack of standardized software tools, like algorithm implementations, tracking device and tool setups, and data processing methods escalate the labor for system development and sustainable system evaluation. In this paper, a new toolkit component of the Medical Imaging and Interaction Toolkit (MITK), the MITK-IGT, and its exemplary application for computer-assisted prostate surgery are presented. MITK-IGT aims at integrating software tools, algorithms and tracking device interfaces into the MITK toolkit to provide a comprehensive software framework for computer aided diagnosis support, therapy planning, treatment support, and radiological follow-up. An exemplary application of the MITK-IGT framework is introduced with a surgical navigation system for laparos-copic prostate surgery. It illustrates the broad range of application possibilities provided by the framework, as well as its simple extensibility with custom algorithms and other software modules.

The current protocol for image-guidance in liver surgeries involves rigid registration algorithm. Systematic studies have shown that the liver can deform up to 2cms during surgeries thereby compromising the accuracy of the surgical navigation systems. Compensating for intraoperative deformations using computational models has shown promising results. In this work, we follow up the initial rigid registration with a computational approach. The proposed computational approach relies on the closest point distances between the undeformed pre-operative surface and the rigidly registered deformed intra-operative surface. We also introduce a spatial smoothing filter to generate a realistic deformation field using the closest point distances. The proposed approach was validated in both phantom experiments and clinical cases. Preliminary results are encouraging and suggest that computational models can be used to improve the accuracy of image-guided liver surgeries.

We present an image-guided intervention system based on tracked 3D elasticity imaging (EI) to provide a novel interventional modality for registration with pre-operative CT. The system can be integrated in both laparoscopic and robotic partial nephrectomies scenarios, where this new use of EI makes exact intra-operative execution of pre-operative planning possible. Quick acquisition and registration of 3D-B-Mode and 3D-EI volume data allows intra-operative registration with CT and thus with pre-defined target and critical regions (e.g. tumors and vasculature). Their real-time location information is then overlaid onto a tracked endoscopic video stream to help the surgeon avoid vessel damage and still completely resect tumors including safety boundaries. The presented system promises to increase the success rate for partial nephrectomies and potentially for a wide range of other laparoscopic and robotic soft tissue interventions. This is enabled by the three components of robust real-time elastography, fast 3D-EI/CT registration, and intra-operative tracking. With high quality, robust strain imaging (through a combination of parallelized 2D-EI, optimal frame pair selection, and optimized palpation motions), kidney tumors that were previously unregistrable or sometimes even considered isoechoic with conventional B-mode ultrasound can now be imaged reliably in interventional settings. Furthermore, this allows the transformation of planning CT data of kidney ROIs to the intra-operative setting with a markerless mutual-information-based registration, using EM sensors for intraoperative motion tracking. Overall, we present a complete procedure and its development, including new phantom models - both ex vivo and synthetic - to validate image-guided technology and training, tracked elasticity imaging, real-time EI frame selection, registration of CT with EI, and finally a real-time, distributed software architecture. Together, the system allows the surgeon to concentrate on intervention completion with less time pressure.

Percutaneous image-guided interventions, such as radiofrequency ablation (RFA), biopsy, seed implantation, and several types of drainage, employ needle shaped instruments which have to be inserted into the patient's body. Precise planning of needle placement is a key to a successful intervention. The planning of the access path has to be carried out with respect to a variety of criteria for all possible trajectories to the selected target. Since the planning is performed in 2D slices, it demands considerable experience and constitutes a significant mental task. To support the process of finding a suitable path for hepatic interventions, we propose a fast automatic method that computes a list of path proposals for a given target point inside the liver with respect to multiple criteria that affect safety and practicability. Prerequisites include segmentation masks of the liver, of all relevant risk structures and, depending on the kind of procedure, of the tumor. The path proposals are computed based on a weighted combination of cylindrical projections. Each projection represents one path criterion and is generated using the graphics hardware of the workstation. The list of path proposals is generated in less than one second. Hence, updates of the proposals upon changes of the target point and other relevant input parameters can be carried out interactively. The results of a preliminary evaluation indicate that the proposed paths are comparable to those chosen by experienced radiologists and therefore are suited to support planning in the clinical environment. Our implementation focuses on RFA and biopsy in the liver but may be adapted to other types of interventions.

An important aspect of implant replacement of the human joint is the fit achieved between the implant and bone canal. As the implant is inserted within the medullary canal, its position and orientation is subjected to a variety of constraints introduced either by the external forces and moments applied by the surgeon or by the interaction of the implant with the cortical wall of the medullary canal. This study evaluated the implant-bone interaction of a humeral stem in elbow replacement surgery as an example, but the principles can also be applied to other joints. After converting CT scan data of the humerus to the parametric NURBS-based representation, a collision detection procedure based on existing Computer-Aided Engineering techniques was employed to control the instantaneous kinematics and dynamics of the insertion of a humeral implant in an attempt to determine its final posture within the canal. By measuring the misalignment between the native flexion-extension (FE) axis of the distal humerus and the prosthesis, a prediction was made regarding the fit between the canal and the implant. This technique was shown to be effective in predicting the final misalignment of the implant axis with respect to the native FE axis of the distal humerus using a cadaver specimen for in-vitro validation.

With the increased use and development of image-guided surgical applications, there is a need for methods of analysis of the accuracy and precision of the components which compose these systems. One primary component of an image-guided surgery system is the position tracking system which allows for the localization of a tool within the surgical field and provides information which is translated back to the images. Previously much work has been done in characterizing these systems for spatial accuracy and precision. Much of this previous work examines single tracking systems or modalities. We have devised a method which allows for the characterization of a novel tracking system independent of modality and location. We describe the development of a phantom system which allows for rapid design and creation of surfaces with different geometries. We have also demonstrated a method of analysis of the data generated by this phantom system, and used it to compare Biosense-Webster's CartoXP^TM, and Northern Digital's Aurora^TMmagnetic trackers. We have determined that the accuracy and precision of the CartoXP was best, followed closely by the Aurora's dome volume, then the Aurora's cube volume. The mean accuracy for all systems was better than 3mm and decays with distance from the field generator.

Advances in medical imaging technologies are assisting radiologists in more accurate diagnoses. This paper details an autostereoscopic static volumetric display, called CSpace®, capable of projecting three-dimensional (3D) medical imaging data in 3D world coordinates. Using this innovative technology, the displayed 3D data set can be viewed in the optical medium from any perspective angle without the use of any viewing aid. The design of CSpace® allows a volume rendering of the surface and the interior of any organ of the human body. As a result, adjacent tissues can be better monitored, and disease diagnoses can be more accurate. In conjunction with CSpace hardware, we have developed a software architecture that can read digital imaging and communication in medicine (DICOM) files whether captured by ultrasound devices, magnetic resonance imaging (MRI), or computed tomography (CT) scanners. The software acquires the imaging parameters from the files' header, and then applies the parameters on the rendered 3D object to display it in the exact form it was captured.

The paper reports a fully-automated, cross-modality sensor data registration scheme between video and magnetic tracker data. This registration scheme is intended for use in computerized imaging systems to model the appearance, structure, and dimension of human anatomy in three dimensions (3D) from endoscopic videos, particularly colonoscopic videos, for cancer research and clinical practices. The proposed cross-modality calibration procedure operates this way: Before a colonoscopic procedure, the surgeon inserts a magnetic tracker into the working channel of the endoscope or otherwise fixes the tracker's position on the scope. The surgeon then maneuvers the scope-tracker assembly to view a checkerboard calibration pattern from a few different viewpoints for a few seconds. The calibration procedure is then completed, and the relative pose (translation and rotation) between the reference frames of the magnetic tracker and the scope is determined. During the colonoscopic procedure, the readings from the magnetic tracker are used to automatically deduce the pose (both position and orientation) of the scope's reference frame over time, without complicated image analysis. Knowing the scope movement over time then allows us to infer the 3D appearance and structure of the organs and tissues in the scene. While there are other well-established mechanisms for inferring the movement of the camera (scope) from images, they are often sensitive to mistakes in image analysis, error accumulation, and structure deformation. The proposed method using a magnetic tracker to establish the camera motion parameters thus provides a robust and efficient alternative for 3D model construction. Furthermore, the calibration procedure does not require special training nor use expensive calibration equipment (except for a camera calibration pattern-a checkerboard pattern-that can be printed on any laser or inkjet printer).

In modern medical CT, the primary source of data is a set of X-ray projections acquired around the object, which are then used to reconstruct a discrete regular grid of sample points. Conventional volume rendering methods use this reconstructed regular grid to estimate unknown off-grid values via interpolation. However, these interpolated values may not match the values that would have been generated had they been reconstructed directly with CT. The consequence can be simple blurring, but also the omission of fine object detail which usually contains precious information. To avoid these problems, in the method we propose, instead of reconstructing a lattice of volume sample points, we derive a highfidelity object model directly from the reconstruction process, fitting a localized object model to the acquired raw data within tight tolerances. This model can then be easily evaluated both for slice-based viewing as well as in GPU 3D volume rendering, offering excellent detail preservation in zooming operations. Furthermore, the model-driven representation also supports high-precision analytical ray casting.

A growing number of applications in the field of computer-assisted laparoscopic interventions depend on accurate and fast 3D surface acquisition. The most commonly applied methods for 3D reconstruction of organ surfaces from 2D endoscopic images involve establishment of correspondences in image pairs to allow for computation of 3D point coordinates via triangulation. The popular feature-based approach for correspondence search applies a feature descriptor to compute high-dimensional feature vectors describing the characteristics of selected image points. Correspondences are established between image points with similar feature vectors. In a previous study, the performance of a large set of state-of-the art descriptors for the use in minimally invasive surgery was assessed. However, standard Phase Alternating Line (PAL) endoscopic images were utilized for this purpose. In this paper, we apply some of the best performing feature descriptors to in-vivo PAL endoscopic images as well as to High Definition Television (HDTV) endoscopic images of the same scene and show that the quality of the correspondences can be increased significantly when using high resolution images.

Previous work indicated that using ordered subsets (OS-SIRT) for iterative CT can optimize the reconstruction performance once optimal settings for parameters such as number of subsets and relaxation factor have been identified. However, recent work also indicated that the optimal settings have dependent relations with regards to the quality of the projection data (such as SNR-level), which are hard to obtain a-priori. In addition, users may also have preferences in trading off between the dependent parameters, such as reconstruction speed and quality, which makes these (independent) parameters even more difficult to determine in an automated manner. Therefore, we devise an effective parameter space navigation interface allowing users to interactively assist parameter selection for iterative CT reconstruction algorithms (here for OS-SIRT). It is based on a 2D scatter plot with six display modes to show different features of the reconstruction results based on the user preferences. It also enables a dynamic visualization by gradual parameter alteration for illustrating the rate of impact of a given parameter constellation. Finally, we note the generality of our approach, which could be applied to assist any parameter selection related systems.

This paper discusses the methods for real-time rendering of time-varying dynamic fluoroscope images including fluid flow for the ERCP (Endoscopic Retrograde Cholangiopancreatography) simulation. A volume rendering technique is used to generate virtual fluoroscopy images. This paper develops an image-overlaying method which overlaps the timevarying images onto the constant background image. The full size fluoroscopy image is computed from the initial volume data set during the pre-processing stage, which is then saved as the background image. Only the time-varying images are computed from the time-varying volume data set during the actual simulation. This involves relatively small computation compared with the background image. The time-varying images are then overlaid onto the background image to obtain the complete images. This method reduces computational overhead by removing redundant computations. A simplified particle dynamics model is employed for fast simulation of the fluid flow. The fluid model, a collection of particles, interacts only with the ducts based on principles of a complete elastic collision. Hence, the velocity of the particles, when they collide with the duct, can be computed by using simple algebraic equations. The methods are implemented for fast simulation of the ERCP.

By combining the strengths of various imaging modalities, the multimodality imaging approach has potential to improve tumor staging, delineation of tumor boundaries, chemo-radiotherapy regime design, and treatment response assessment in cancer management. To address the urgent needs for efficient tools to analyze large-scale clinical trial data, we have developed an integrated multimodality, functional and anatomical imaging analysis software package for target definition and therapy response assessment in pediatric radiotherapy (RT) patients. Our software provides quantitative tools for automated image segmentation, region-of-interest (ROI) histogram analysis, spatial volume-of-interest (VOI) analysis, and voxel-wise correlation across modalities. To demonstrate the clinical applicability of this software, histogram analyses were performed on baseline and follow-up ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET images of nine patients with rhabdomyosarcoma enrolled in an institutional clinical trial at St. Jude Children's Research Hospital. In addition, we combined ¹⁸F-FDG PET, dynamic-contrast-enhanced (DCE) MR, and anatomical MR data to visualize the heterogeneity in tumor pathophysiology with the ultimate goal of adaptive targeting of regions with high tumor burden. Our software is able to simultaneously analyze multimodality images across multiple time points, which could greatly speed up the analysis of large-scale clinical trial data and validation of potential imaging biomarkers.

We call the computerized assistive process of recognizing, delineating, and quantifying organs and tissue regions in medical imaging, occurring automatically during clinical image interpretation, automatic anatomy recognition (AAR). The AAR system we are developing includes five main parts: model building, object recognition, object delineation, pathology detection, and organ system quantification. In this paper, we focus on the delineation part. For the modeling part, we employ the active shape model (ASM) strategy. For recognition and delineation, we integrate several hybrid strategies of combining purely image based methods with ASM. In this paper, an iterative Graph-Cut ASM (IGCASM) method is proposed for object delineation. An algorithm called GC-ASM was presented at this symposium last year for object delineation in 2D images which attempted to combine synergistically ASM and GC. Here, we extend this method to 3D medical image delineation. The IGCASM method effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. We propose a new GC cost function, which effectively integrates the specific image information with the ASM shape model information. The proposed methods are tested on a clinical abdominal CT data set. The preliminary results show that: (a) it is feasible to explicitly bring prior 3D statistical shape information into the GC framework; (b) the 3D IGCASM delineation method improves on ASM and GC and can provide practical operational time on clinical images.

Minimally invasive catheter ablation procedures are guided by biplane fluoroscopy images visualising the interventional scene from two different orientations. However, these images do not provide direct access to their inherent spatial information. A three-dimensional reconstruction and visualisation of the catheters from such projections has the potential to support quick and precise catheter navigation. It enhances the perception of the interventional situation and provides means of three-dimensional catheter pose documentation. In this contribution we develop an algorithm for tracking the three-dimensional pose of electro-physiological catheters in biplane fluoroscopy images. It is based on the B-Snake algorithm which had to be adapted to the biplane and in particular the asynchronous image acquisition situation. A three-dimensional B-spline curve is transformed so that its projections are consistent with the catheter path enhancing feature images, while the information from the missing image caused by the asynchronous acquisition is interpolated from its sequence neighbours. In order to analyse the three-dimensional precision, virtual images were created from patient data sets and threedimensional ground truth catheter paths. The evaluation of the three-dimensional catheter pose reconstruction by means of our algorithm on 33 of such virtual image sets indicated a mean catheter pose error of 1.26 mm and a mean tip deviation of 3.28 mm. The tracking capability of the algorithm was evaluated on 10 patient data sets. In 94 % of all images our algorithm followed the catheter projections.

In this paper, we propose a new gold standard data set for the validation of 2D/3D image registration algorithms for image guided radiotherapy. A gold standard data set was calculated using a pig head with attached fiducial markers. We used several imaging modalities common in diagnostic imaging or radiotherapy which include 64-slice computed tomography (CT), magnetic resonance imaging (MRI) using T1, T2 and proton density (PD) sequences, and cone beam CT (CBCT) imaging data. Radiographic data were acquired using kilovoltage (kV) and megavoltage (MV) imaging techniques. The image information reflects both anatomy and reliable fiducial marker information, and improves over existing data sets by the level of anatomical detail and image data quality. The markers of three dimensional (3D) and two dimensional (2D) images were segmented using Analyze 9.0 (AnalyzeDirect, Inc) and an in-house software. The projection distance errors (PDE) and the expected target registration errors (TRE) over all the image data sets were found to be less than 1.7 mm and 1.3 mm, respectively. The gold standard data set, obtained with state-of-the-art imaging technology, has the potential to improve the validation of 2D/3D registration algorithms for image guided therapy.

Lots of procedures in computer assisted interventions register pre-interventionally generated 3D data sets to the intraoperative situation using fast and simply generated 2D images, e.g. from a C-Arm, a B-mode Ultrasound, etc. Registration is typically done by generating a 2D image out of the 3D data set, comparison to the original 2D image using a planar similarity measure and subsequent optimisation. As these two images can be very different, a lot of different comparison functions are in use. In a recent article Stochastic Rank Correlation, a merit function based on Spearman's rank correlation coefficient was presented. By comparing randomly chosen subsets of the images, the authors wanted to avoid the computational expense of sorting all the points in the image. In the current paper we show that, because of the limited grey level range in medical images, full image rank correlation can be computed almost as fast as Pearson's correlation coefficient. A run time estimation is illustrated with numerical results using a 2D Shepp-Logan phantom at different sizes, and a sample data set of a pig.

Acquisition-to-acquisition signal intensity variations (non-standardness) are inherent in MR images. Standardization is a post processing method for correcting inter-subject intensity variations through transforming all images from the given image gray scale into a standard gray scale wherein similar intensities achieve similar tissue meanings. The lack of a standard image intensity scale in MRI leads to many difficulties in tissue characterizability, image display, and analysis, including image segmentation. This phenomenon has been documented well; however, effects of standardization on medical image registration have not been studied yet. In this paper, we investigate the influence of intensity standardization in registration tasks with systematic and analytic evaluations involving clinical MR images. We conducted nearly 20,000 clinical MR image registration experiments and evaluated the quality of registrations both quantitatively and qualitatively. The evaluations show that intensity variations between images degrades the accuracy of registration performance. The results imply that the accuracy of image registration not only depends on spatial and geometric similarity but also on the similarity of the intensity values for the same tissues in different images.

Diseases of the intestinal tract often begin with changes altering the bowel tissue elasticity. Therefore, quantification of bowel motion would be desirable for diagnosis, treatment monitoring and follow-up. Dynamic MRI can capture such changes, but quantification requires non-rigid registration. Towards a computer-assisted quantification for bowel diseases, two innovative methods for detection of bowel motility restrictions have been developed and evaluated. Therefore a coronal 2D+t image will be extracted from a dynamic 3D MRI dataset and registered non-rigidly over multiple time steps. The first method generates a new image from the resulting motion maps by adding the absolute value of the vector for each pixel to the corresponding values in following time steps. The second method calculates the absolute values only from the lateral part of the vectors, skipping the coronal part, and thus removes large distortions due to movements caused by breathing. In this preliminary evaluation both methods will be compared in regard to 5 healthy subjects (volunteers) and 5 patients with proven restrictions in bowel motility. It was shown, that for the first method with respiration a classification of volunteers and patients is only partly possible. However, the second method turns out to be capable of classifying normal and restricted bowel peristalsis. For the second method the mean motion from patients motion maps are about 34.4% lower than that from volunteers motion maps. Therefore, for the first time such a classification is possible.

New advances in MRI technology enable fast acquisition of high-resolution images. In combination with the new open architecture this scanners are entering the surgical suite being used as intra-operative imaging modality for minimally invasive interventions. However, for a usage on a large scale the major issue of availability of appropriate surgical tools is still unsolved. Such instruments, i.e. needles and catheters have to be MR-safe and -compatible but in contrast still have to be visible within the MRI image. This usually is solved by integration of markers onto non-magnetic devices. For reasons of MR-safety, work-flow and cost effectiveness semi-active markers without any connection to the outside are preferable. The challenge in development and integration of such resonant markers is to precisely meet the MRI frequency by keeping the geometrical dimensions of the interventional tool constant. This paper focuses on the reliable integration and easy fabrication of such resonant markers on the tip of an interventional instrument. Starting with a theoretical background for resonant labels a self-sufficient pre-tuned marker consisting of a standard capacitor and a thin-film inductor is presented. A prototype is built using aerosol deposition for the inductor on a 6-F polymer catheter and by integration of an off-the-shelf capacitor into the lumen of the catheter. Due to the fact that the dielectric materials of some capacitors lead to artifacts in the MRI image different capacitor technologies are investigated. The prototypes are scanned by an interventional MRI device proving the proper functionality of the tools.

Small mammals, namely mice and rats, play an important role in biomedical research. Imaging, in conjunction with accurate therapeutic agent delivery, has tremendous value in small animal research since it enables serial, non-destructive testing of animals and facilitates the study of biomarkers of disease progression. The small size of organs in mice lends some difficulty to accurate biopsies and therapeutic agent delivery. Image guidance with the use of robotic devices should enable more accurate and repeatable targeting for biopsies and delivery of therapeutic agents, as well as the ability to acquire tissue from a pre-specified location based on image anatomy. This paper presents our work in integrating a robotic needle guide device, specialized stereotaxic mouse holder, and magnetic resonance imaging, with a long-term goal of performing accurate and repeatable targeting in anesthetized mice studies.

This paper proposes a rapid method for compensating registration error between the tracker and the endoscope in a flexible neuroendoscopic surgery navigation system, as well as evaluates the accuracy of the proposed method. Recently, flexible neuroendoscopic surgery navigation systems have been developed utilizing an electromagnetic tracker (EMT). In such systems, an electromagnetic tracker sensor is fixed at the tip of a flexible endoscope to get the position and the orientation of the endoscope camera by using the relationship between the camera and the sensor. Usually, the relationship is estimated by a registration method using a calibration chart. Then, virtual images corresponding to real endoscopic views are generated by using the position and orientation of the camera. However, in the clinical application, the sensor has to be re-fixed before or during the surgery due to its disinfection or breakage. Although the sensor can be re-fixed at the same position as the registered position, it is difficult to ensure the roll of sensor in the same because the senor is a cylinder. Furthermore, the sensor can also be rotated by the operation of tools during surgery. As a result, the virtual images will be rotated and become greatly different from the real endoscopic views. In this case, the relationship between camera and sensor has to be re-estimated by a registration method or manually, which makes the operation of endoscope complicated and nonfeasible. In order to overcome this problem, we proposed a rapid method for compensating the rotational error between real and virtual cameras using the epipolar geometry. In this study, various experiments of the method are performed in order to evaluate and to improve its accuracy. Experimental results suggested estimation accuracy can be improved by reducing the relative error of EMT outputs, and it is necessary to ensure the quality of images which are used in the estimation.

In modern oncology, thermal ablations are increasingly used as a regional treatment option to supplement systemic treatment strategies such as chemotherapy and immunotherapy. The goal of all thermal ablation procedures is to cause cell death of disease tissue while sparing adjacent healthy tissue. Real-time assessment of thermal damage is the key to therapeutic efficiency and safety of such procedures. Magnetic resonance thermometry is capable of monitoring the spatial distribution and temporal evolution of temperature changes during thermal ablations. In this work, we present an advanced monitoring system for MR-guided thermal ablations that includes multiplanar visualization, specialized overlay visualization methods, and additional methods for correcting errors resulting from magnetic field shifts and motion. To ensure the reliability of the displayed thermal data, systematic quality control of thermal maps is carried out on-line. The primary purpose of this work is to provide clinicians with an intuitive tool for accurately visualizing the progress of thermal treatment at the time of the procedure. Importantly, the system is designed to be independent of the heating source. The presented system is expected to be of great value not only to guide thermal procedures but also to further explore the relationship between temperature-time exposure and tissue damage. The software application was implemented within the eXtensible Imaging Platform (XIP) and has been validated with clinical data.

Accurate needle placement is a common need in the medical environment. While the use of small diameter needles for clinical applications such as biopsy, anesthesia and cholangiography is preferred over the use of larger diameter needles, precision placement can often be challenging, particularly for needles with a bevel tip. This is due to deflection of the needle shaft caused by asymmetry of the needle tip. Factors such as the needle shaft material, bevel design, and properties of the tissue penetrated determine the nature and extent to which a needle bends. In recent years, several models have been developed to characterize the bending of the needle, which provides a method of determining the trajectory of the needle through tissue. This paper explores the use of a nonholonomic model to characterize needle bending while providing added capabilities of path planning, obstacle avoidance, and path correction for lung biopsy procedures. We used a ballistic gel media phantom and a robotic needle placement device to experimentally assess the accuracy of simulated needle paths based on the nonholonomic model. Two sets of experiments were conducted, one for a single bend profile of the needle and the second set of tests for double bending of the needle. The tests provided an average error between the simulated path and the actual path of 0.8 mm for the single bend profile and 0.9 mm for the double bend profile tests over a 110 mm long insertion distance. The maximum error was 7.4 mm and 6.9 mm for the single and double bend profile tests respectively. The nonholonomic model is therefore shown to provide a reasonable prediction of needle bending.

Electromagnetic (EM) tracking systems are often used for real time navigation of medical tools in an Image Guided Therapy (IGT) system. They are specifically advantageous when the medical device requires tracking within the body of a patient where line of sight constraints prevent the use of conventional optical tracking. EM tracking systems are however very sensitive to electromagnetic field distortions. These distortions, arising from changes in the electromagnetic environment due to the presence of conductive ferromagnetic surgical tools or other medical equipment, limit the accuracy of EM tracking, in some cases potentially rendering tracking data unusable. We present a mapping method for the operating region over which EM tracking sensors are used, allowing for characterization of measurement errors, in turn providing physicians with visual feedback about measurement confidence or reliability of localization estimates. In this instance, we employ a calibration phantom to assess distortion within the operating field of the EM tracker and to display in real time the distribution of measurement errors, as well as the location and extent of the field associated with minimal spatial distortion. The accuracy is assessed relative to successive measurements. Error is computed for a reference point and consecutive measurement errors are displayed relative to the reference in order to characterize the accuracy in near-real-time. In an initial set-up phase, the phantom geometry is calibrated by registering the data from a multitude of EM sensors in a non-ferromagnetic ("clean") EM environment. The registration results in the locations of sensors with respect to each other and defines the geometry of the sensors in the phantom. In a measurement phase, the position and orientation data from all sensors are compared with the known geometry of the sensor spacing, and localization errors (displacement and orientation) are computed. Based on error thresholds provided by the operator, the spatial distribution of localization errors are clustered and dynamically displayed as separate confidence zones within the operating region of the EM tracker space.

This paper presents on-going research that addresses uncertainty along the Z-axis in image-guided surgery, for applications to large surgical workspaces, including those found in veterinary medicine. Veterinary medicine lags human medicine in using image guidance, despite MR and CT data scanning of animals. The positional uncertainty of a surgical tracking device can be modeled as an octahedron with one long axis coinciding with the depth axis of the sensor, where the short axes are determined by pixel resolution and workspace dimensions. The further a 3D point is from this device, the more elongated is this long axis, and the greater the uncertainty along Z of this point's position, in relation to its components along X and Y. Moreover, for a triangulation-based tracker, its position error degrades with the square of distance. Our approach is to use two or more Micron Trackers to communicate with each other, and combine this feature with flexible positioning. Prior knowledge of the type of surgical procedure, and if applicable, the species of animal that determines the scale of the workspace, would allow the surgeon to pre-operatively configure the trackers in the OR for optimal accuracy. Our research also leverages the open-source Image-guided Surgery Toolkit (IGSTK).

In a detailed laboratory investigation we performed a series of experiments in order to assess the validity of the widely used TRE concept to predict the application accuracy. On base of 1mm CT scan a plastic skull, a cadaver head and a volunteer were registered to an in house navigation system. We stored the position data of an optical camera (NDI Polaris) for registration with pre-defined CT coordinates. For every specimen we choose 3, 5, 7 and 9 registration and 10 evaluation points, respectively, performing 10 registrations. The data were evaluated both with the Arun and the Horn approaches. The vectorial difference between actual and predefined position in the CT data set was stored and evaluated for FRE and TRE. Evaluation and visualization was implemented in Matlab. The data were analyzed, specifically for normal distribution, with MS Excel and SPSS Version 15.0. For the plastic skull and the anatomic specimen submillimetric application accuracy was found experimentally and confirmed by the calculated TRE. Since for the volunteer no Titanium screws were implanted anatomic landmarks had to be used for registration and evaluation; an application accuracy in the low millimeter regime was found in all approaches. However, the detailed statistical analysis of the data revealed that the model predictions and the actual measurements do not exhibit a strong statistical correlation (p < 0.05). These data suggest that the TRE predictions are too optimistic and should be used with caution intraoperatively.

With its high sensitivity, dynamic contrast-enhanced MR imaging (DCE-MRI) of the breast is today one of the first-line tools for early detection and diagnosis of breast cancer, particularly in the dense breast of young women. However, many relevant findings are very small or occult on targeted ultrasound images or mammography, so that MRI guided biopsy is the only option for a precise histological work-up [1]. State-of-the-art software tools for computer-aided diagnosis of breast cancer in DCE-MRI data offer also means for image-based planning of biopsy interventions. One step in the MRI guided biopsy workflow is the alignment of the patient position with the preoperative MR images. In these images, the location and orientation of the coil localization unit can be inferred from a number of fiducial markers, which for this purpose have to be manually or semi-automatically detected by the user. In this study, we propose a method for precise, full-automatic localization of fiducial markers, on which basis a virtual localization unit can be subsequently placed in the image volume for the purpose of determining the parameters for needle navigation. The method is based on adaptive thresholding for separating breast tissue from background followed by rigid registration of marker templates. In an evaluation of 25 clinical cases comprising 4 different commercial coil array models and 3 different MR imaging protocols, the method yielded a sensitivity of 0.96 at a false positive rate of 0.44 markers per case. The mean distance deviation between detected fiducial centers and ground truth information that was appointed from a radiologist was 0.94mm.

The optimal transfer of preoperative planning data and risk evaluations to the operative site is challenging. A common practice is to use preoperative 3D planning models as a printout or as a presentation on a display. One important aspect is that these models were not developed to provide information in complex workspaces like the operating room. Our aim is to reduce the visual complexity of 3D planning models by mapping surgically relevant information onto a risk map. Therefore, we present methods for the identification and classification of critical anatomical structures in the proximity of a preoperatively planned resection surface. Shadow-like distance indicators are introduced to encode the distance from the resection surface to these critical structures on the risk map. In addition, contour lines are used to accentuate shape and spatial depth. The resulting visualization is clear and intuitive, allowing for a fast mental mapping of the current resection surface to the risk map. Preliminary evaluations by liver surgeons indicate that damage to risk structures may be prevented and patient safety may be enhanced using the proposed methods.

When performing laparoscopies and punctures, the precise anatomic localizations are required. Current techniques very often rely on the mapping between the real situation and preoperative images. The PC based software we present realizes 3D segmentations of regions of interest from CT or MR slices. It allows the planning of punctures or trocars insertion trajectories, taking anatomical constraints into account. Geometrical transformations allow the projection over the patient's body of the organs and lesions shapes, realistically reconstructed, using a standard video projector in the operating room. We developed specific image processing software which automatically segments and registers images of a webcam used in the operating room to give feedback to the user.

The motivation for shell dose irradiation is to deliver a high therapeutic dose to the surrounding supplying blood-vessels of a lesion. Our approach's main utility is in enabling laboratory experiments to test the much disputed hypothesis about tumor vascular damage. That is, at high doses, tumor control is driven by damage to the tumor vascular supply and not the damage to the tumor cells themselves. There is new evidence that bone marrow derived cells can reconstitute tumor blood vessels in mice after irradiation. Shell dosimetry is also of interest to study the effect of radiation on neurogenic stem cells that reside in small niche surface of the mouse ventricles, a generalized form of shell. The type of surface that we are considering as a shell is a sphere which is created by intersection of cylinders. The results are then extended to create the contours of different organ shapes. Specifically, we present a routine to identify the 3-D structure of a mouse brain, project it into 2-D contours and convert the contours into trajectories that can be executed by our platform. We use the Small Animal Radiation Research Platform (SARRP) to demonstrate the dose delivery procedure. The SARRP is a portable system for precision irradiation with beam sizes down to 0.5 mm and optimally planned radiation with on-board cone-beam CT guidance.

Latest developments in radiation therapy such as IGRT (image guided radiation therapy) and IMRT (intensity modulated radiation therapy) promise to spare organs at risk by applying better dose distribution on the tumor. For any effective application of these methods the exact positioning of the patient and the localization of the exposured organ is crucially. With respect to the filling of rectum and bladder the prostate can move several millimeters up to centimeters. That implies the need of daily determination and correction of the position of the prostate before irradiation. We calibrated a scan head of a B-mode US machine (Ultramark 9, advanced Technology Laboratories, USA) by means of an optical tracking system (Polaris, NDI, Can). 2D/3D registration was accomplished by minimizing an adapted mutual information function. Before the registration procedure was started CT respectively US images were preprocessed by applying various filters and masks. For registration between the tracking system and the coordinate system of the Linac (defined by the positioning laser system) a block with three drilled holes was used. Since the coordinates of the holes are known in both coordinate systems a simple point-to-point registration fulfills the task. The complete system setup was evaluated by means of a water-filled balloon embedded in a gelatinetank. For additional evaluation of the 2D/3D registration we used real patient data. For evaluation of the 2D/3D registration on patient data the prostate was drawn in by a physician on the US image and the reformatted CT slice. Then, the Hausdorff distance between the two structures was calculated. The target registration error (TRE) with respect to the balloon experiments amounted to be 2.1mm±1.2mm for 10 targets. The US calibration was accomplished with an error of 0.8mm± 0.2mm (five calibrations). With respect to the 2D/3D registration we found an Hausdorff distance of 2.6mm. The results imply that the method is sufficiently accurate and robust. It can be easily applied to older linear accelerators at low costs.

In this paper, we present a comprehensive, quantitative imaging framework for improved treatment of prostate cancer via computer-assisted targeted therapy (CATT) to facilitate radiotherapy dose escalation to regions with a high likelihood of disease presence. The framework involves identification of high likelihood prostate cancer regions using computer-aided detection (CAD) classifier on diagnostic MRI, followed by mapping of these regions from MRI onto planning computerized tomography (CT) via image registration. Treatment of prostate cancer by targeted radiotherapy requires CT to formulate a dose plan. While accurate delineation of the prostate and cancer can provide reduced exposure of benign tissue to radiation, as well as a higher dose to the cancer, CT is ineffective in localizing intraprostatic lesions and poor for highlighting the prostate boundary. MR imagery on the other hand allows for greatly improved visualization of the prostate. Further, several studies have demonstrated the utility of CAD for identifying the location of tumors on in vivo multi-functional prostate MRI. Consequently, our objective is to improve the accuracy of radiotherapy dose plans via multimodal fusion of MR and CT. To achieve this objective, the CATT framework presented in this paper comprises the following components: (1) an unsupervised pixel-wise classifier to identify suspicious regions within the prostate on diagnostic MRI, (2) elastic image registration to align corresponding diagnostic MRI, planning MRI, and CT of the prostate, (3) mapping of the suspect regions from diagnostic MRI onto CT, and (4) calculation of a modified radiotherapy plan with escalated dose for cancer. Qualitative comparison of the dose plans (with and without CAD) over a total of 79 2D slices obtained from 10 MR-CT patient studies, suggest that our CATT framework could help in improved targeted treatment of prostate cancer.

4D image-guided radiation therapy (IGRT) for free-breathing lungs is challenging due to the complicated respiratory dynamics. Effective modeling of respiratory motion is crucial to account for the motion affects on the dose to tumors. We propose a shape-correlated statistical model on dense image deformations for patient-specic respiratory motion estimation in 4D lung IGRT. Using the shape deformations of the high-contrast lungs as the surrogate, the statistical model trained from the planning CTs can be used to predict the image deformation during delivery verication time, with the assumption that the respiratory motion at both times are similar for the same patient. Dense image deformation fields obtained by diffeomorphic image registrations characterize the respiratory motion within one breathing cycle. A point-based particle optimization algorithm is used to obtain the shape models of lungs with group-wise surface correspondences. Canonical correlation analysis (CCA) is adopted in training to maximize the linear correlation between the shape variations of the lungs and the corresponding dense image deformations. Both intra- and inter-session CT studies are carried out on a small group of lung cancer patients and evaluated in terms of the tumor location accuracies. The results suggest potential applications using the proposed method.

We describe a software system for building three-dimensional (3D) models from colonoscopic videos. The system is end-to-end in the sense that it takes as input raw image frames-shot during a colon exam-and produces the 3D structure of objects of interest (OOI), such as tumors, polyps, and lesions. We use the structure-from-motion (SfM) approach in computer vision which analyzes an image sequence in which camera's position and aim vary relative to the OOI. The varying pose of the camera relative to the OOI induces the motion-parallax effect which allows 3D depth of the OOI to be inferred. Unlike the traditional SfM system pipeline, our software system contains many check-and-balance mechanisms to ensure robustness, and the analysis from earlier stages of the pipeline is used to guide the later processing stages to better handle challenging medical data. The constructed 3D models allow the pathology (growth and change in both structure and appearance) to be monitored over time.

Surface models from medical image data (intensity, binary) are used for evaluating spatial relationships for intervention or radiation treatment planning. Furthermore, surface models are employed for generating volume meshes for simulating e.g. tissue deformation or blood flow. In such applications, smoothness and accuracy of the models are essential. These aspects may be influenced by image preprocessing, the mesh generation algorithm and mesh postprocessing (smoothing, simplification). Thus, we evaluated the influences of different image preprocessing methods (Gaussian smoothing, morphological operators, shape-based interpolation), model generation (Marching Cubes, Constrained Elastic Surface Nets, MPU Implicits) and mesh postprocessing to intensity and binary data with respect to its application within surgical planning and simulation. The resulting surface meshes are evaluated regarding their smoothness, accuracy and mesh quality. We consider the local curvature, equi-angle skewness, (Hausdorff) distances between two meshes (before and after processing), and volume preservation as measures. We discuss these results concerning their suitability for different applications in the field of surgical planning as well as finite element simulations and make recommendations on how to receive smooth and accurate surface meshes for exemplary cases.

This paper presents a novel method to simulate multiple contacts of deformable objects modeled by the finite-element method. There have been two main approaches for contact models in the literature. The penalty method fails to guarantee the non-penetration condition. In the constraint method, imposition of multiple position constraints on arbitrary points of the surface, caused by the multiple contacts, can be non-deterministic depending on the contact configurations. Infinite number of solutions exists in most cases. The proposed method uses a deformable membrane of mass and spring to determine the initial position constraints to obtain a unified solution regardless of the contact configurations. The membrane is locally generated at the contact region, and is identical to the local triangular surface meshes of the finite-element model. The membrane is then deformed by the contacts caused by rigid objects such as surgical tools. Displacements of the mass points of the deformed membrane at the equilibrium state are, then, applied to the finiteelement model as the position constraints. Simulation results show satisfactory realism in the real-time simulation of the deformation. The proposed method prevents penetration of the rigid object into the deformable object. The method can be applied to interactions between tools and organs of arbitrary shapes.

Live three dimensional (3D) transesophageal echocardiography (TEE) provides real-time imaging of cardiac structure and function, and has been shown to be useful in interventional cardiac procedures. Its application in catheter based cardiac procedures is, however, limited by its limited field of view (FOV). In order to mitigate this limitation, we register pre-operative magnetic resonance (MR) images to live 3D TEE images. Conventional multimodal image registration techniques that use mutual information (MI) as the similarity measure use statistics from the entire image. In these cases, correct registration, however, may not coincide with the global maximum of MI metric. In order to address this problem, we present an automated registration algorithm that balances a combination global and local edge-based statistics. The weighted sum of global and local statistics is computed as the similarity measure, where the weights are decided based on the strength of the local statistics. Phantom validation experiments shows improved capture ranges when compared with conventional MI based methods. The proposed method provided robust results with accuracy better than 3 mm (5°) in the range of -10 to 12 mm (-6 to 3°), -14 to 12 mm (-6 to 6°) and -16 to 6 mm (-6 to 3°) in x-, y-, and z- axes respectively. We believe that the proposed registration method has the potential for real time intra-operative image fusion during percutaneous cardiac interventions.

Realistic colon simulations do not exist but would be valuable for CT colonography (CTC) CAD development and validation of new colon image processing algorithms. The human colon is a convoluted tubular structure and very hard to model physically and electronically. In this investigation, we propose a novel approach to generate realistic colon simulation using mesh skinning. The method proceeds as follows. First, a digital phantom of a cylindrical tube is modeled to simulate a straightened colon. Second, haustral folds and teniae coli are added to the cylindrical tube. Third, a centerline equipped with rotation-minimizing frames (RMF) and distention values is computed. Fourth, mesh skinning is applied to warp the tube around the centerline and generate realistic colon simulation. Lastly, colonic polyps in the shape of ellipsoids are also modeled. Results show that the simulated colon highly resembles the real colon. This is the first colon simulation that incorporates most colon characteristics in one model, including curved centerline, variable distention, haustral folds, teniae coli and colonic polyps.

Recurrent problem in medical image segmentation and analysis, establishing a ground truth for assessment purposes is often difficult. Facing this problem, the scientific community orients its efforts towards the development of objective methods for evaluation, namely by building up or simulating the missing ground truth for analysis. This paper focuses on the case of human pulmonary airways and develops a method 1) to simulate the ground truth for different pathophysiological configurations of the bronchial tree as a mesh model, and 2) to generate synthetic 3D CT images of airways associated with the simulated ground truth. The airway model is here built up based on the information provided by a medial axis (describing bronchus shape, subdivision geometry and local radii), which is computed from real CT data to ensure realism and matching with a patient-specific morphology. The model parameters can be further on adjusted to simulate various pathophysiological conditions of the same patient (longitudinal studies). Based on the airway mesh model, a 3D image model is synthesized by simulating the CT acquisition process. The image realism is achieved by including textural features of the surrounding pulmonary tissue which are obtained by segmentation from the same original CT data providing the airway axis. By varying the scanning simulation parameters, several 3D image models can be generated for the same airway mesh ground truth. Simulation results for physiological and pathological configurations are presented and discussed, illustrating the interest of such a modeling process for designing computer-aided diagnosis systems or for assessing their sensitivity, mainly for follow-up studies in asthma and COPD.

Minimally invasive surgical techniques and advanced imaging systems are gaining prevalence in modern clinical practice. Using miniaturized magnetic trackers in combination with these procedures can help physicians with the orientation and guidance of instruments in graphical displays, navigation during surgery, 3D reconstruction of anatomy, and other applications. Magnetic trackers are often used in conjunction with other sensors or instruments such as endoscopes and optical trackers. In such applications, complex calibration procedures are required to align the coordinate systems of the different devices in order to produce accurate results. Unfortunately, current calibration procedures developed for augmented reality are cumbersome and unsuitable for repeated use in a clinical setting. This paper presents an efficient automated endoscope-tracker calibration algorithm for clinical applications. The algorithm is based on a state-of-the-art trust region optimization method and requires minimal intervention from the endoscope operator. The only required input is a short video of a calibration grid taken with the endoscope and attached magnetic tracker prior to the procedure. The three stage calibration process uses a traditional camera calibration to determine the intrinsic and extrinsic parameters of the endoscope, and then the endoscope is registered in the tracker's reference frame using a novel linear estimation method and a trust region optimization algorithm. This innovative method eliminates the need for complicated calibration procedures and facilitates the use of magnetic tracking devices in clinical settings.

Digital scan conversion algorithm is the most computational intensive part of B-mode ultrasound imaging. Traditionally, in order to meet the requirements of real-time imaging, digital scan conversion algorithm often traded off image quality for speed, such as the use of simple image interpolation algorithm, the use of look-up table to carry out polar coordinates transform and logarithmic compression. This paper presents a GPU-based high-definition real-time ultrasound digital scan conversion algorithm implementation. By rendering appropriate proxy geometry, we can implement a high precision digital scan conversion pipeline, including polar coordinates transform, bi-cubic image interpolation, high dynamic range tone reduction, line average and frame persistence FIR filtering, 2D post filtering, fully in the fragment shader of GPU at real-time speed. The proposed method shows the possibility of updating exist FPGA or ASIC based digital scan conversion implementation to low cost GPU based high-definition digital scan conversion implementation.

Many recent studies have demonstrated the efficacy of interstitial ablative approaches for the treatment of hepatic tumors. Despite these promising results, current systems remain highly dependent on operator skill, and cannot treat many tumors because there is little control of the size and shape of the zone of necrosis, and no control over ablator trajectory within tissue once insertion has taken place. Additionally, tissue deformation and target motion make it extremely difficult to place the ablator device precisely into the target. Irregularly shaped target volumes typically require multiple insertions and several overlapping (thermal) lesions, which are even more challenging to accomplish in a precise, predictable, and timely manner without causing excessive damage to surrounding normal tissues. In answer to these problems, we have developed a steerable acoustic ablator called the ACUSITT with the ability of directional energy delivery to precisely shape the applied thermal dose . In this paper, we address image guidance for this device, proposing an innovative method for accurate tracking and tool registration with spatially-registered intra-operative three-dimensional US volumes, without relying on an external tracking device. This method is applied to guid-ance of the flexible, snake-like, lightweight, and inexpensive ACUSITT to facilitate precise placement of its ablator tip within the liver, with ablation monitoring via strain imaging. Recent advancements in interstitial high-power ultrasound applicators enable controllable and penetrating heating patterns which can be dynamically altered. This paper summarizes the design and development of the first synergistic system that integrates a novel steerable interstitial acoustic ablation device with a novel trackerless 3DUS guidance strategy.

Image segmentation and tissue characterization are fundamental tasks of computer-aided diagnosis (CAD) in medical ultrasound imaging. As an initial step, such algorithms are usually based on extraction of pertinent features from the acquired ultrasound data. Typically, these features are computed directly from localized image segments, thereby representing local statistical properties of the image. However, the process of image formation of medical ultrasound suggests that such an approach could result in a variety of unwanted artifacts (such as excessively smooth segmentation boundaries or misclassification) at subsequent stages of the algorithm. In this work, we propose to first decompose the observed images into a number of their statistically distinct components. The decomposition is based on the maximum-a-posteriori (MAP) statistical framework which has been derived based on the signal and noise models appropriate for the ultrasound setting. Subsequently, each resulting component is used separately to extract a set of its corresponding features. When retrieved in this way (rather than directly from the observed image), the combined set of resulting features is shown to be capable of better discriminating between different tissue types. Examples of in silico simulations and in vivo experiments are provided to illustrate the practical usefulness of this technique for improving the results of ultrasound image segmentation.

The aim of this study was to evaluate the elongation of the Achilles tendon by looking at the changing position of Myo-Tendenious Junction (MTJ) using ultrasound during isometric contraction on an Isometric dynamometer. A sequence of ultrasound images in the form of movie, obtained from a unit operating at a frequency of 12MHz during isometric contraction, was analyzed offline using MATLAB to track the MTJ. This investigation has implemented important techniques for in vivo feature extraction of Achilles tendon. Prior to feature extraction, the images were filtered by anisotropic diffusion method and morphological enhancements. The cross correlation search algorithm with an adaptive mask was utilized to track MTJ by comparing adjacent segmented frames. The present method was studied on seventeen subjects, where it was able to measure the related movement accurately.

Prostate biopsy procedures are currently limited to using 2D transrectal ultrasound (TRUS) imaging to guide the biopsy needle. Being limited to 2D causes ambiguity in needle guidance and provides an insufficient record to allow guidance to the same suspicious locations or avoid regions that are negative during previous biopsy sessions. We have developed a mechanically assisted 3D ultrasound imaging and needle tracking system, which supports a commercially available TRUS probe and integrated needle guide for prostate biopsy. The mechanical device is fixed to a cart and the mechanical tracking linkage allows its joints to be manually manipulated while fully supporting the weight of the ultrasound probe. The computer interface is provided in order to track the needle trajectory and display its path on a corresponding 3D TRUS image, allowing the physician to aim the needle-guide at predefined targets within the prostate. The system has been designed for use with several end-fired transducers that can be rotated about the longitudinal axis of the probe in order to generate 3D image for 3D navigation. Using the system, 3D TRUS prostate images can be generated in approximately 10 seconds. The system reduces most of the user variability from conventional hand-held probes, which make them unsuitable for precision biopsy, while preserving some of the user familiarity and procedural workflow. In this paper, we describe the 3D TRUS guided biopsy system and report on the initial clinical use of this system for prostate biopsy.

MRI is currently the most promising imaging modality for prostate cancer diagnosis due to its high resolution and multiparametric nature. However, currently there is no standard for integration of diagnostic information from different MRI sequences. We propose a method to increase the diagnostic accuracy of MRI by correlating biopsy specimens with four MRI sequences including T2 weighted MRI, Diffusion Weight Imaging, Dynamic Contrast Enhanced MRI and MRI spectroscopy. This method uses device tracking and image fusion to determine the specimen's position on MRI images. The proposed method is unbiased and cost effective. It does not substantially interfere with the standard biopsy workflow, allowing it to be easily accepted by physicians. A study of 41 patients was carried out to validate the approach. The performance of all four MRI sequences in various combinations is reported. Guidelines are given for multi-parametric imaging and tracked biopsy of prostate cancer.

The treatment of urinary bladder cancer is usually carried out using fluorescence endoscopy. A narrow-band bluish illumination activates a tumor marker resulting in a red fluorescence. Because of low illumination power the distance between endoscope and bladder wall is kept low during the whole bladder scan, which is carried out before treatment. Thus, only a small field of view (FOV) of the operation field is provided, which impedes navigation and relocating of multi-focal tumors. Although off-line calculated panorama images can assist surgery planning, the immediate display of successively growing overview images composed from single video frames in real-time during the bladder scan, is well suited to ease navigation and reduce the risk of missing tumors. Therefore we developed an image mosaicking algorithm for fluorescence endoscopy. Due to fast computation requirements a flexible multi-threaded software architecture based on our RealTimeFrame platform is developed. Different algorithm tasks, like image feature extraction, matching and stitching are separated and applied by independent processing threads. Thus, different implementation of single tasks can be easily evaluated. In an optimization step we evaluate the trade-off between feature repeatability and total processing time, consider the thread synchronization, and achieve a constant workload of each thread. Thus, a fast computation of panoramic images is performed on a standard hardware platform, preserving full input image resolution (780x576) at the same time. Displayed on a second clinical monitor, the extended FOV of the image composition promises high potential for surgery assistance.

C-arm X-ray fluoroscopy-based radioactive seed localization for intraoperative dosimetry of prostate brachytherapy is an active area of research. The fluoroscopy tracking (FTRAC) fiducial is an image-based tracking device composed of radio-opaque BBs, lines, and ellipses that provides an effective means for pose estimation so that three-dimensional reconstruction of the implanted seeds from multiple X-ray images can be related to the ultrasound-computed prostate volume. Both the FTRAC features and the brachytherapy seeds must be segmented quickly and accurately during the surgery, but current segmentation algorithms are inhibitory in the operating room (OR). The first reason is that current algorithms require operators to manually select a region of interest (ROI), preventing automatic pipelining from image acquisition to seed reconstruction. Secondly, these algorithms fail often, requiring operators to manually correct the errors. We propose a fast and effective ROI-free automatic FTRAC and seed segmentation algorithm to minimize such human intervention. The proposed algorithm exploits recent image processing tools to make seed reconstruction as easy and convenient as possible. Preliminary results on 162 patient images show this algorithm to be fast, effective, and accurate for all features to be segmented. With near perfect success rates and subpixel differences to manual segmentation, our automatic FTRAC and seed segmentation algorithm shows promising results to save crucial time in the OR while reducing errors.

Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of the prostate gland when evaluated along with T2-weighted images, diffusion-weighted images (DWI) and their corresponding apparent diffusion coefficient (ADC) maps can yield valuable information in patients with rising or elevated serum prostate-specific antigen (PSA) levels₁. In some cases, patients present with multiple negative trans-rectal ultrasound (TRUS) biopsies, often placing the patient into a cycle of active surveillance. Recently, more patients are undergoing TRIM for targeted biopsy of suspicious findings with a cancer yield of ~59% compared to 15% for second TRUS biopsy₂ to solve this diagnostic dilemma and plan treatment. Patients were imaged in two separate sessions on a 1.5T magnet using a cardiac phased array parallel imaging coil. Automated CAD software was used to identify areas of wash-out. If a suspicious finding was identified on all sequences it was followed by a second imaging session. Under MRI-guidance, cores were acquired from each target region₃. In one case the microscopic diagnosis was prostatic intraepithelial neoplasia (PIN), in the other it was invasive adenocarcinoma. Patient 1 had two negative TRUS biopsies and a PSA level of 9ng/mL. Patient 2 had a PSA of 7.2ng/mL. He underwent TRUS biopsy which was negative for malignancy. He was able to go on to treatment for his prostate carcinoma (PCa)₄. MRI may have an important role in a subset of patients with multiple negative TRUS biopsies and elevated or rising PSA.

We developed an algorithm for tracking prostate motion during MRI-guided prostatic needle placement, with the primary application in prostate biopsy. Our algorithm has been tested on simulated patient and phantom data. The algorithm features a robust automatic restart and a 12-core biopsy error validation scheme. Simulation tests were performed on four patient MRI pre-operative volumes. Three orthogonal slices were extracted from the pre-operative volume to simulate the intra-operative volume and a volume of interest was defined to isolate the prostate. Phantom tests used six datasets, each representing the phantom at a known perturbed position. These volumes were registered to their corresponding reference volume (the phantom at its home position). Convergence tests on the phantom data showed that the algorithm demonstrated accurate results at 100% confidence level for initial misalignments of less than 5mm and at 73% confidence level for initial misalignments less than 10mm. Our algorithm converged in 95% of the cases for the simulated patient data with 0.66mm error and the six phantom registration tests resulted in 1.64mm error.

The Fontan operation is a surgical treatment for patients with severe congenital heart diseases, where a biventricular correction of the heart can't be achieved. In these cases, a uni-ventricular system is established. During the last step of surgery a tunnel segment is placed to connect the inferior caval vein directly with the pulmonary artery, bypassing the right atrium and ventricle. Thus, the existing ventricle works for the body circulation, while the venous blood is passively directed to the pulmonary arteries. Fontan tunnels can be placed intra- and extracardially. The location, length and shape of the tunnel must be planned accurately. Furthermore, if the tunnel is placed extracardially, it must be positioned between other anatomical structures without constraining them. We developed a software system to support planning of the tunnel location, shape, and size, making pre-operative preparation of the tunnel material possible. The system allows for interactive placement and adjustment of the tunnel, affords a three-dimensional visualization of the virtual Fontan tunnel inside the thorax, and provides a quantification of the length, circumferences and diameters of the tunnel segments. The visualization and quantification can be used to plan and prepare the tunnel material for surgery in order to reduce the intra-operative time and to improve the fit of the tunnel patch.