This PDF file contains the front matter associated with SPIE Proceedings Volume 9036, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Radiofrequency ablation (RFA) is an important therapeutic procedure for small hepatic tumors. To make sure that the target tumor is effectively treated, RFA monitoring is essential. While several imaging modalities can observe the ablation procedure, it is not clear how ablated lesions on the images correspond to actual necroses. This uncertainty contributes to the high local recurrence rates (up to 55%) after radiofrequency ablative therapy. This study investigates a novel approach to correlate images of ablated lesions with actual necroses. We mapped both intraoperative images of the lesion and a slice through the actual necrosis in a common reference frame. An electromagnetic tracking system was used to accurately match lesion slices from different imaging modalities. To minimize the liver deformation effect, the tracking reference frame was defined inside the tissue by anchoring an electromagnetic sensor adjacent to the lesion. A validation test was performed using a phantom and proved that the end-to-end accuracy of the approach was within 2mm. In an in-vivo experiment, intraoperative magnetic resonance imaging (MRI) and ultrasound (US) ablation images were correlated to gross and histopathology. The results indicate that the proposed method can accurately correlate invivo ablations on different modalities. Ultimately, this will improve the interpretation of the ablation monitoring and reduce the recurrence rates associated with RFA.

The increase of technological complexity in surgery has created a need for novel man-machine interaction techniques. Specifically, context-aware systems which automatically adapt themselves to the current circumstances in the OR have great potential in this regard. To create such systems, models of surgical procedures are vital, as they allow analyzing the current situation and assessing the context. For this purpose, we have developed a Surgical Process Model based on Description Logics. It incorporates general medical background knowledge as well as intraoperatively observed situational knowledge. The representation consists of three parts: the Background Knowledge Model, the Preoperative Process Model and the Integrated Intraoperative Process Model. All models depend on each other and create a concise view on the surgery. As a proof of concept, we applied the system to a specific intervention, the laparoscopic distal pancreatectomy.

Radiofrequency ablation (RFA) is becoming a standard procedure for minimally invasive tumor treatment in clinical practice. Due to its common technical procedure, low complication rate, and low cost, RFA has become an alternative to surgical resection in the liver. To evaluate the therapy success of RFA, thorough follow-up imaging is essential. Conventionally, shape, size, and position of tumor and coagulation are visually compared in a side-by-side manner using pre- and post-interventional images. To objectify the verification of the treatment success, a novel software assistant allowing for fast and accurate comparison of tumor and coagulation is proposed.

In this work, the clinical value of the proposed assessment software is evaluated. In a retrospective clinical study, 39 cases of hepatic tumor ablation are evaluated using the prototype software and conventional image comparison by four radiologists with different levels of experience. The cases are randomized and evaluated in two sessions to avoid any recall-bias. Self-confidence of correct diagnosis (local recurrence vs. no local recurrence) on a six-point scale is given for each case by the radiologists. Sensitivity, specificity, positive and negative predictive values as well as receiver operating curves are calculated for both methods. It is shown that the software-assisted method allows physicians to correctly identify local tumor recurrence with a higher percentage than the conventional method (sensitivity: 0.6 vs. 0.35), whereas the percentage of correctly identified successful ablations is slightly reduced (specificity: 0.83 vs. 0.89).

A coordinate system describing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specific anatomical landmarks, the coordinate system allows integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric coordinate systems over the surface of anatomical shapes, given their flexibility to set values at specific locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at sites of limited geometric diversity. In this paper we present a method for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the volume medial surface. We have applied the methodology to define a common reference system for the liver shape and functional anatomy. This reference system sets a solid base for creating anatomical models of the patient's liver, and allows comparing livers from several patients in a common framework of reference.

Background: Precise needle puncture of renal calyces is a challenging and essential step for successful percutaneous nephrolithotomy. This work tests and evaluates, through a clinical trial, a real-time navigation system to plan and guide percutaneous kidney puncture. Methods: A novel system, entitled i3DPuncture, was developed to aid surgeons in establishing the desired puncture site and the best virtual puncture trajectory, by gathering and processing data from a tracked needle with optical passive markers. In order to navigate and superimpose the needle to a preoperative volume, the patient, 3D image data and tracker system were previously registered intraoperatively using seven points that were strategically chosen based on rigid bone structures and nearby kidney area. In addition, relevant anatomical structures for surgical navigation were automatically segmented using a multi-organ segmentation algorithm that clusters volumes based on statistical properties and minimum description length criterion. For each cluster, a rendering transfer function enhanced the visualization of different organs and surrounding tissues. Results: One puncture attempt was sufficient to achieve a successful kidney puncture. The puncture took 265 seconds, and 32 seconds were necessary to plan the puncture trajectory. The virtual puncture path was followed correctively until the needle tip reached the desired kidney calyceal. Conclusions: This new solution provided spatial information regarding the needle inside the body and the possibility to visualize surrounding organs. It may offer a promising and innovative solution for percutaneous punctures.

Bronchoscopy enables a number of minimally invasive chest procedures for diseases such as lung cancer and asthma. For example, using the bronchoscope’s continuous video stream as a guide, a physician can navigate through the lung airways to examine general airway health, collect tissue samples, or administer a disease treatment. In addition, physicians can now use new image-guided intervention (IGI) systems, which draw upon both three-dimensional (3D) multi-detector computed tomography (MDCT) chest scans and bronchoscopic video, to assist with bronchoscope navigation. Unfortunately, little use is made of the acquired video stream, a potentially invaluable source of information. In addition, little effort has been made to link the bronchoscopic video stream to the detailed anatomical information given by a patient’s 3D MDCT chest scan. We propose a method for constructing a multimodal CT-video model of the chest. After automatically computing a patient’s 3D MDCT-based airway-tree model, the method next parses the available video data to generate a positional linkage between a sparse set of key video frames and airway path locations. Next, a fusion/mapping of the video’s color mucosal information and MDCT-based endoluminal surfaces is performed. This results in the final multimodal CT-video chest model. The data structure constituting the model provides a history of those airway locations visited during bronchoscopy. It also provides for quick visual access to relevant sections of the airway wall by condensing large portions of endoscopic video into representative frames containing important structural and textural information. When examined with a set of interactive visualization tools, the resulting fused data structure provides a rich multimodal data source. We demonstrate the potential of the multimodal model with both phantom and human data.

Intraoperative tracking of laparoscopic instruments is a prerequisite to realize further assistance functions. Since endoscopic images are always available, this sensor input can be used to localize the instruments without special devices or robot kinematics. In this paper, we present an image-based markerless 3D tracking of different da Vinci instruments in near real-time without an explicit model. The method is based on different visual cues to segment the instrument tip, calculates a tip point and uses a multiple object particle filter for tracking. The accuracy and robustness is evaluated with in vivo data.

Potentially precancerous polyps detected with CT colonography (CTC) need to be removed subsequently, using an optical colonoscope (OC). Due to large colonic deformations induced by the colonoscope, even very experienced colonoscopists find it difficult to pinpoint the exact location of the colonoscope tip in relation to polyps reported on CTC. This can cause unduly prolonged OC examinations that are stressful for the patient, colonoscopist and supporting staff. We developed a method, based on monocular 3D reconstruction from OC images, that automatically matches polyps observed in OC with polyps reported on prior CTC. A matching cost is computed, using rigid point-based registration between surface point clouds extracted from both modalities. A 3D printed and painted phantom of a 25 cm long transverse colon segment was used to validate the method on two medium sized polyps. Results indicate that the matching cost is smaller at the correct corresponding polyp between OC and CTC: the value is 3.9 times higher at the incorrect polyp, comparing the correct match between polyps to the incorrect match. Furthermore, we evaluate the matching of the reconstructed polyp from OC with other colonic endoluminal surface structures such as haustral folds and show that there is a minimum at the correct polyp from CTC. Automated matching between polyps observed at OC and prior CTC would facilitate the biopsy or removal of true-positive pathology or exclusion of false-positive CTC findings, and would reduce colonoscopy false-negative (missed) polyps. Ultimately, such a method might reduce healthcare costs, patient inconvenience and discomfort.

This paper proposes an adaptive fiducial-free registration method that uses a multiple point selection strategy based on sensor orientation and endoscope radius information. To develop a flexible endoscopy navigation system, since we use an electromagnetic tracker with positional sensors to estimate bronchoscope movements, we must synchronize such tracker and pre-operative image coordinate systems using either marker-based or fiducial-free registration methods. Fiducial-free methods assume that bronchoscopes are operated along bronchial centerlines. Unfortunately, such an assumption is easily violated during interventions. To address such a tough assumption, we utilize an adaptive strategy that generates multiple points in terms of sensor measurements and bronchoscope radius information. From these generated points, we adaptively choose the optimal point, which is the closest to its assigned bronchial centerline, to perform registration. The experimental results from phantom validation demonstrate that our proposed adaptive strategy significantly improved the fiducial-free registration accuracy from at least 5.4 to 2.2 mm compared to current available methods.

Wireless video capsule endoscope (VCE) provides a noninvasive method to examine the entire gastrointestinal (GI) tract, especially small intestine, where other endoscopic instruments can barely reach. VCE is able to continuously provide clear pictures in short fixed intervals, and as such researchers have attempted to use image processing methods to track the video capsule in order to locate the abnormalities inside the GI tract. To correctly estimate the speed of the motion of the endoscope capsule, the radius of the intestinal track must be known a priori. Physiological factors such as intestinal contraction, however, dynamically change the radius of the small intestine, which could bring large errors in speed estimation. In this paper, we are aiming to estimate the radius of the contracted intestinal track. First a geometric model is presented for estimating the radius of small intestine based on the black hole on endoscopic images. To validate our proposed model, a 3-dimentional virtual testbed that emulates the intestinal contraction is then introduced in details. After measuring the size of the black holes on the test images, we used our model to esimate the radius of the contracted intestinal track. Comparision between analytical results and the emulation model parameters has verified that our proposed method could preciously estimate the radius of the contracted small intestine based on endoscopic images.

Endoscopic and laparoscopic surgeries are used for many minimally invasive procedures but limit the visual and haptic feedback available to the surgeon. This can make vessel sparing procedures particularly challenging to perform. Previous approaches have focused on hardware intensive intraoperative imaging or augmented reality systems that are difficult to integrate into the operating room. This paper presents a simple approach in which motion is visually enhanced in the endoscopic video to reveal pulsating arteries. This is accomplished by amplifying subtle, periodic changes in intensity coinciding with the patient’s pulse. This method is then applied to two procedures to illustrate its potential. The first, endoscopic third ventriculostomy, is a neurosurgical procedure where the floor of the third ventricle must be fenestrated without injury to the basilar artery. The second, nerve-sparing robotic prostatectomy, involves removing the prostate while limiting damage to the neurovascular bundles. In both procedures, motion magnification can enhance subtle pulsation in these structures to aid in identifying and avoiding them.

Desorption electrospray ionization mass spectrometry (DESI-MS) provides a highly sensitive imaging technique for differentiating normal and cancerous tissue at the molecular level. This can be very useful, especially under intra-operative conditions where the surgeon has to make crucial decision about the tumor boundary. In such situations, the time it takes for imaging and data analysis becomes a critical factor. Therefore, in this work we utilize compressive sensing to perform the sparse sampling of the tissue, which halves the scanning time. Furthermore, sparse feature selection is performed, which not only reduces the dimension of data from about 10⁴ to less than 50, and thus significantly shortens the analysis time. This procedure also identifies biochemically important molecules for further pathological analysis. The methods are validated on brain and breast tumor data sets.

Intraarticular fractures of the calcaneus are routinely treated by open reduction and internal fixation followed by intraoperative imaging to validate the repositioning of bone fragments. C-Arm CT offers surgeons the possibility to directly verify the alignment of the fracture parts in 3D. Although the device provides more mobility, there is no sufficient information about the device-to-patient orientation for standard plane reconstruction. Hence, physicians have to manually align the image planes in a position that intersects with the articular surfaces. This can be a time-consuming step and imprecise adjustments lead to diagnostic errors. We address this issue by introducing novel semi-/automatic methods for adjustment of the standard planes on mobile C-Arm CT images. With the semi-automatic method, physicians can quickly adjust the planes by setting six points based on anatomical landmarks. The automatic method reconstructs the standard planes in two steps, first SURF keypoints (2D and newly introduced pseudo-3D) are generated for each image slice; secondly, these features are registered to an atlas point set and the parameters of the image planes are transformed accordingly. The accuracy of our method was evaluated on 51 mobile C-Arm CT images from clinical routine with manually adjusted standard planes by three physicians of different expertise. The average time of the experts (46s) deviated from the intermediate user (55s) by 9 seconds. By applying 2D SURF key points 88% of the articular surfaces were intersected correctly by the transformed standard planes with a calculation time of 10 seconds. The pseudo-3D features performed even better with 91% and 8 seconds.

Image-guided percutaneous ablation is the standard treatment for focal liver tumors deemed inoperable and is commonly used to maintain eligibility for patients on transplant waitlists. Radiofrequency (RFA), microwave (MWA) and cryoablation technologies are all delivered via one or a number of needle-shaped probes inserted directly into the tumor. Planning is mostly based on contrast CT/MRI. While intra-procedural CT is commonly used to confirm the intended probe placement, 2D ultrasound (US) remains the main, and in some centers the only imaging modality used for needle guidance. Corresponding intraoperative 2D US with planning and other intra-procedural imaging modalities is essential for accurate needle placement. However, identification of matching features of interest among these images is often challenging given the limited field-of-view (FOV) and low quality of 2D US images. We have developed a passive tracking arm with a motorized scan-head and software tools to improve guiding capabilities of conventional US by large FOV 3D US scans that provides more anatomical landmarks that can facilitate registration of US with both planning and intra-procedural images. The tracker arm is used to scan the whole liver with a high geometrical accuracy that facilitates multi-modality landmark based image registration. Software tools are provided to assist with the segmentation of the ablation probes and tumors, find the 2D view that best shows the probe(s) from a 3D US image, and to identify the corresponding image from planning CT scans. In this paper, evaluation results from laboratory testing and a phase 1 clinical trial for planning and guiding RFA and MWA procedures using the developed system will be presented. Early clinical results show a comparable performance to intra-procedural CT that suggests 3D US as a cost-effective alternative with no side-effects in centers where CT is not available.

Optoacoustic sensing is a hybrid technique that combines the advantages of high sensing depth of ultrasound with contrast of optical absorption. In this study a miniature optoacoustic probe that can characterize the target properties located at the distal end of a catheter is investigated. The probe includes an optical fiber to illuminate the target with the pulsed laser light and a hydrophone to detect the generated optoacoustic signal. The probe is designed for the forwardsensing and therefore the acoustic signal propagates along the tube before being detected. Due to the circular geometry, the waves inside the tube are highly complex. A three dimensional numerical simulation is performed to model the optoacoustic wave generation and propagation inside the water filled cylindrical tubes. The effect of the boundary condition, tube diameter and target size on the detected signal is systematically evaluated. A prototype of the probe is made and tested for detecting an absorbing target inside a 2mm diameter tube submerged in water. The preliminary experimental results corresponding to the simulation is acquired. Although many different medical applications for this miniature probe may exist, our main focus is on detecting the occlusion inside the ventricular shunts. These catheters are used to divert the excess cerebrospinal fluid to the absorption site and regulate inter cranial pressure of hydrocephalous patients. Unfortunately the malfunction rate of these catheters due to blockage is very high. This sensing tool could locate the occluding tissue non-invasively and can potentially characterize the occlusion composites by scanning at different wavelengths of the light.

A novel imaging system that recommends potential suture placement for anastomosis to surgeons is developed. This is achieved by a multispectral imaging system coupled with polarizers and image analysis software. We performed preliminary imaging of ex vivo porcine intestine to evaluate the system. Vulnerable tissue regions including blood vessels were successfully identified and segmented. Thickness of different tissue areas is visualized. Strategies towards optimal points for suture placements have been discussed. Preliminary data suggest our imaging platform and analysis algorithm may be useful in avoiding blood vessels, identifying optimal regions for suture placements to perform safer operations in possibly reduced time.

Purpose. We evaluated the feasibility of a surface scanning system (Catalyst) for respiratory motion monitoring of breast cancer patients treated with radiotherapy in deep inspiration breath-hold (DIBH). DIBH is used to reduce the radiation dose to the heart and lung. In contrast to RPM, a competing marker-based system, Catalyst does not require an objectmarker on the patient’s skin.

Materials and Methods. Experiment 1: a manikin was used to simulate sinusoidal breathing. The amplitude, period and baseline (signal value at end-expiration) were estimated with RPM and Catalyst. Experiment 2 and 3: the Quasar phantom was used to study if the angle of the monitored surface affects the amplitude of the recorded signal.

Results. Experiment 1: we observed comparable period estimates for both systems. The amplitudes were 8 ± 0.1 mm (Catalyst) and 4.9 ± 0.1 mm (RPM). Independent check with in-room lasers showed an amplitude of approximately 8 mm, supporting Catalyst measurements. Large baseline errors were seen with RPM. Experiment 2: RPM underestimated the amplitude if the object-marker was angled during vertical motion. This result explains the amplitude underestimation by RPM seen in Experiment 1. Experiment 3: an increased (fixed) surface angle during breathing motion resulted in an overestimated amplitude with RPM, while the amplitude estimated by Catalyst was unaffected.

Conclusion. Our study showed that Catalyst can be used as a better alternative to the RPM. With Catalyst, the amplitude estimates are more accurate and do not depend on the angle of the tracked surface, and the baseline errors are smaller.

Respiratory motion and its variability lead to location uncertainties in radiation therapy (RT) of thoracic and abdominal tumors. Current approaches for motion compensation in RT are usually driven by respiratory surrogate signals, e.g., spirometry. In this contribution, we present an approach for statistical analysis, modeling and subsequent simulation of surrogate signals on a cycle-by-cycle basis. The simulated signals represent typical patient-specific variations of, e.g., breathing amplitude and cycle period. For the underlying statistical analysis, all breathing cycles of an observed signal are consistently parameterized using approximating B-spline curves. Statistics on breathing cycles are then performed by using the parameters of the B-spline approximations. Assuming that these parameters follow a multivariate Gaussian distribution, realistic time-continuous surrogate signals of arbitrary length can be generated and used to simulate the internal motion of tumors and organs based on a patient-specific diffeomorphic correspondence model. As an example, we show how this approach can be employed in RT treatment planning to calculate tumor appearance probabilities and to statistically assess the impact of respiratory motion and its variability on planned dose distributions.

Patient respiratory motion is a major problem during external beam radiotherapy of the thoracic and abdominal regions due to the associated organ and target motion. In addition, such motion introduces uncertainty in both radiotherapy planning and delivery and may potentially vary between the planning and delivery sessions. The aim of this work is to examine subject-specific external respiratory motion and its associated drift from an assumed average cycle which is the basis for many respiratory motion compensated applications including radiotherapy treatment planning and delivery. External respiratory motion data were acquired from a group of 20 volunteers using a marker-less 3D depth camera, Kinect for Windows. The anterior surface encompassing thoracic and abdominal regions were subject to principal component analysis (PCA) to investigate dominant variations. The first principal component typically describes more than 70% of the motion data variance in the thoracic and abdominal surfaces. Across all of the subjects used in this study, 58% of subjects demonstrate largely abdominal breathing and 33% exhibited largely thoracic dominated breathing. In most cases there is observable drift in respiratory motion during the 300s capture period, which is visually demonstrated using Kernel Density Estimation. This study demonstrates that for this cohort of apparently healthy volunteers, there is significant respiratory motion drift in most cases, in terms of amplitude and relative displacement between the thoracic and abdominal respiratory components. This has implications for the development of effective motion compensation methodology.

In SPECT imaging, motion from respiration and body motion can reduce image quality by introducing motion-related artifacts. A minimally-invasive way to track patient motion is to attach external markers to the patient’s body and record their location throughout the imaging study. If a patient exhibits multiple movements simultaneously, such as respiration and body-movement, each marker location data will contain a mixture of these motions. Decomposing this complex compound motion into separate simplified motions can have the benefit of applying a more robust motion correction to the specific type of motion. Most motion tracking and correction techniques target a single type of motion and either ignore compound motion or treat it as noise. Few methods account for compound motion exist, but they fail to disambiguate super-position in the compound motion (i.e. inspiration in addition to body movement in the positive anterior/posterior direction). We propose a new method for decomposing the complex compound patient motion using an unsupervised learning technique called Independent Component Analysis (ICA). Our method can automatically detect and separate different motions while preserving nuanced features of the motion without the drawbacks of previous methods. Our main contributions are the development of a method for addressing multiple compound motions, the novel use of ICA in detecting and separating mixed independent motions, and generating motion transform with 12 DOFs to account for twisting and shearing. We show that our method works with clinical datasets and can be employed to improve motion correction in single photon emission computed tomography (SPECT) images.

Calcaneal fractures are commonly treated by open reduction and internal fixation. An anatomical reconstruction of involved joints is mandatory to prevent cartilage damage and premature arthritis. In order to avoid intraarticular screw placements, the use of mobile C-arm CT devices is required. However, for analyzing the screw placement in detail, a time-consuming human-computer interaction is necessary to navigate through 3D images and therefore to view a single screw in detail. Established interaction procedures of repeatedly positioning and rotating sectional planes are inconvenient and impede the intraoperative assessment of the screw positioning. To simplify the interaction with 3D images, we propose an automatic screw segmentation that allows for an immediate selection of relevant sectional planes. Our algorithm consists of three major steps. At first, cylindrical characteristics are determined from local gradient structures with the help of RANSAC. In a second step, a DBScan clustering algorithm is applied to group similar cylinder characteristics. Each detected cluster represents a screw, whose determined location is then refined by a cylinder-to-image registration in a third step. Our evaluation with 309 screws in 50 images shows robust and precise results. The algorithm detected 98% (303) of the screws correctly. Thirteen clusters led to falsely identified screws. The mean distance error for the screw tip was 0.8 ± 0.8 mm and for the screw head 1.2 ± 1 mm. The mean orientation error was 1.4 ± 1.2 degrees.

Automatic segmentation of the prostate zones has great potential of improving the accuracy of lesion detection during the image-guided prostate interventions. In this paper, we present a novel compact method to segment the prostate and its zones using multi-parametric magnetic resonance imaging (MRI) and the anatomical priors. The proposed method comprises of a prostate tissue representation using Gaussian mixture model (GMM), a prostate localization using the mean shift with the kernel of the prostate atlas and a prostate partition using the probabilistic valley between zones. The proposed method was tested on four sets of multi-parametric MRIs. The average Dice coefficient resulted from the segmentation of the prostate is 0.80 ± 0.03, the central zone 0.83 ± 0.04, and the peripheral zone 0.52 ± 0.09. The average computing time of the online segmentation is 1 min and 10 s per datasets on a PC with 2.4 GHz and 4.0 GB RAM. The proposed method is fast and has the potential to be used in clinical practices.

Our research project investigates a multi-port approach for minimally-invasive otologic surgery. For planning such a surgery, an accurate segmentation of the risk structures is crucial. However, the segmentation of these risk structures is a challenging task: The anatomical structures are very small and some have a complex shape, low contrast and vary both in shape and appearance. Therefore, prior knowledge is needed which is why we apply model-based approaches. In the present work, we use the Probabilistic Active Shape Model (PASM), which is a more flexible and specific variant of the Active Shape Model (ASM), to segment the following risk structures: cochlea, semicircular canals, facial nerve, chorda tympani, ossicles, internal auditory canal, external auditory canal and internal carotid artery. For the evaluation we trained and tested the algorithm on 42 computed tomography data sets using leave-one-out tests. Visual assessment of the results shows in general a good agreement of manual and algorithmic segmentations. Further, we achieve a good Average Symmetric Surface Distance while the maximum error is comparatively large due to low contrast at start and end points. Last, we compare the PASM to the standard ASM and show that the PASM leads to a higher accuracy.

Segmentation of vertebral structures in magnetic resonance (MR) images is challenging because of poor con­trast between bone surfaces and surrounding soft tissue. This paper describes a semi-automatic method for segmenting vertebral bodies in multi-slice MR images. In order to achieve a fast and reliable segmentation, the method takes advantage of the correlation between shape and pose of different vertebrae in the same patient by using a statistical multi-vertebrae anatomical shape+pose model. Given a set of MR images of the spine, we initially reduce the intensity inhomogeneity in the images by using an intensity-correction algorithm. Then a 3D anisotropic diffusion filter smooths the images. Afterwards, we extract edges from a relatively small region of the pre-processed image with a simple user interaction. Subsequently, an iterative Expectation Maximization tech­nique is used to register the statistical multi-vertebrae anatomical model to the extracted edge points in order to achieve a fast and reliable segmentation for lumbar vertebral bodies. We evaluate our method in terms of speed and accuracy by applying it to volumetric MR images of the spine acquired from nine patients. Quantitative and visual results demonstrate that the method is promising for segmentation of vertebral bodies in volumetric MR images.

Background: Snake-like dexterous manipulators may offer significant advantages in minimally-invasive surgery in areas not reachable with conventional tools. Precise control of a wire-driven manipulator is challenging due to factors such as cable deformation, unknown internal (cable friction) and external forces, thus requiring correcting the calibration intraoperatively by determining the actual pose of the manipulator. Method: A method for simultaneously estimating pose and kinematic configuration of a piecewise-rigid object such as a snake-like manipulator from a single x-ray projection is presented. The method parameterizes kinematics using a small number of variables (e.g., 5), and optimizes them simultaneously with the 6 degree-of-freedom pose parameter of the base link using an image similarity between digitally reconstructed radiographs (DRRs) of the manipulator’s attenuation model and the real x-ray projection. Result: Simulation studies assumed various geometric magnifications (1.2–2.6) and out-of-plane angulations (0°–90°) in a scenario of hip osteolysis treatment, which demonstrated the median joint angle error was 0.04° (for 2.0 magnification, ±10° out-of-plane rotation). Average computation time was 57.6 sec with 82,953 function evaluations on a mid-range GPU. The joint angle error remained lower than 0.07° while out-of-plane rotation was 0°–60°. An experiment using video images of a real manipulator demonstrated a similar trend as the simulation study except for slightly larger error around the tip attributed to accumulation of errors induced by deformation around each joint not modeled with a simple pin joint. Conclusions: The proposed approach enables high precision tracking of a piecewise-rigid object (i.e., a series of connected rigid structures) using a single projection image by incorporating prior knowledge about the shape and kinematic behavior of the object (e.g., each rigid structure connected by a pin joint parameterized by a low degree polynomial basis). Potential applications of the proposed approach include pose estimation of vertebrae in spine and a series of electrodes in coronary sinus catheter. Improvement of GPU performance is expected to further augment computational speed.

Purpose: Deformable registration of preoperative and intraoperative images facilitates accurate localization of target and critical anatomy in image-guided spine surgery. However, conventional deformable registration fails to preserve the morphology of rigid bone anatomy and can impart distortions that confound high-precision intervention. We propose a constrained registration method that preserves rigid morphology while allowing deformation of surrounding soft tissues. Method: The registration method aligns preoperative 3D CT to intraoperative cone-beam CT (CBCT) using free-form deformation (FFD) with penalties on rigid body motion imposed according to a simple intensity threshold. The penalties enforced 3 properties of a rigid transformation – namely, constraints on affinity (AC), orthogonality (OC), and properness (PC). The method also incorporated an injectivity constraint (IC) to preserve topology. Physical experiments (involving phantoms, an ovine spine, and a human cadaver) as well as digital simulations were performed to evaluate the sensitivity to registration parameters, preservation of rigid body morphology, and overall registration accuracy of constrained FFD in comparison to conventional unconstrained FFD (denoted uFFD) and Demons registration. Result: FFD with orthogonality and injectivity constraints (denoted FFD+OC+IC) demonstrated improved performance compared to uFFD and Demons. Affinity and properness constraints offered little or no additional improvement. The FFD+OC+IC method preserved rigid body morphology at near-ideal values of zero dilatation (D = 0.05, compared to 0.39 and 0.56 for uFFD and Demons, respectively) and shear (S = 0.08, compared to 0.36 and 0.44 for uFFD and Demons, respectively). Target registration error (TRE) was similarly improved for FFD+OC+IC (0.7 mm), compared to 1.4 and 1.8 mm for uFFD and Demons. Results were validated in human cadaver studies using CT and CBCT images, with FFD+OC+IC providing excellent preservation of rigid morphology and equivalent or improved TRE. Conclusions: A promising method for deformable registration in CBCT-guided spine surgery has been identified incorporating a constrained FFD to preserve bone morphology. The approach overcomes distortions intrinsic to unconstrained FFD and could better facilitate high-precision image-guided spine surgery.

The determination of tumor margins during surgical resection remains a challenging task. A complete removal of malignant tissue and conservation of healthy tissue is important for the preservation of organ function, patient satisfaction, and quality of life. Visual inspection and palpation is not sufficient for discriminating between malignant and normal tissue types. Hyperspectral imaging (HSI) technology has the potential to noninvasively delineate surgical tumor margin and can be used as an intra-operative visual aid tool. Since histological images provide the ground truth of cancer margins, it is necessary to warp the cancer regions in ex vivo histological images back to in vivo hyperspectral images in order to validate the tumor margins detected by HSI and to optimize the imaging parameters. In this paper, principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which is then used to register HSI images with the corresponding histological image. Affine registration is chosen to model the global transformation. A B-spline free form deformation (FFD) method is used to model the local non-rigid deformation. Registration experiment was performed on animal hyperspectral and histological images. Experimental results from animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection.

Real-time registration of pre-operative magnetic resonance (MR) or computed tomography (CT) images with intra-operative Ultrasound (US) images can be a valuable tool in image-guided therapies and interventions. This paper presents an automatic method for dynamically tracking the deformation of a soft tissue based on registering pre-operative three-dimensional (3D) MR images to intra-operative two-dimensional (2D) US images. The registration algorithm is based on concepts in state estimation where a dynamic finite element (FE)- based linear elastic deformation model correlates the imaging data in the spatial and temporal domains. A Kalman-like filtering process estimates the unknown deformation states of the soft tissue using the deformation model and a measure of error between the predicted and the observed intra-operative imaging data. The error is computed based on an intensity-based distance metric, namely, modality independent neighborhood descriptor (MIND), and no segmentation or feature extraction from images is required. The performance of the proposed method is evaluated by dynamically deforming 3D pre-operative MR images of a breast phantom tissue based on real-time 2D images obtained from an US probe. Experimental results on different registration scenarios showed that deformation tracking converges in a few iterations. The average target registration error on the plane of 2D US images for manually selected fiducial points was between 0.3 and 1.5 mm depending on the size of deformation.

We propose an analytic equation for approximating expected root mean square (RMS) target registration error (TRE) for rigid shape-based registration where measured noisy data points are matched to a rigid shape. The noise distribution of the data points is assumed to be zero-mean, independent, and non-identical; i.e., the noise covariance may be different for each data point. The equation was derived by extending a previously published spatial stiffness model of registration. The equation was validated by performing registration experiments with both synthetic registration data and data collected using an optically tracked pointing stylus. The synthetic registration data were generated from the surface of an ellipsoid. The optically tracked data were collected from three plastic replicas of human radii and registered to isosurface models of the radii computed from CT scans. Noise covariances for the data points were computed by considering the pose of the tracked stylus, the positions of the individual fiducial markers on the stylus coordinate reference frame, and the calibrated position of the stylus tip; these quantities and an estimate of the fiducial localization covariance of the tracking system were used as inputs to a previously published algorithm for estimating the covariance of TRE for point-based (fiducial) registration. Registration simulations were performed using a modified version of the iterated closest point algorithm and the resulting RMS TREs were compared to the values predicted by our analytic equation.

Laparoscopic liver resection is increasingly being performed with results comparable to open cases while incurring less trauma and reducing recovery time. The tradeoff is increased difficulty due to limited visibility and restricted freedom of movement. Image-guided surgical navigation systems have the potential to help localize anatomical features to improve procedural safety and achieve better surgical resection outcome. Previous research has demonstrated that intraoperative surface data can be used to drive a finite element tissue mechanics organ model such that high resolution preoperative scans are registered and visualized in the context of the current surgical pose. In this paper we present an investigation of using sparse data as imposed by laparoscopic limitations to drive a registration model. Non-contact laparoscopicallyacquired surface swabbing and mock-ultrasound subsurface data were used within the context of a nonrigid registration methodology to align mock deformed intraoperative surface data to the corresponding preoperative liver model as derived from pre-operative image segmentations. The mock testing setup to validate the potential of this approach used a tissue-mimicking liver phantom with a realistic abdomen-port patient configuration. Experimental results demonstrates a range of target registration errors (TRE) on the order of 5mm were achieving using only surface swab data, while use of only subsurface data yielded errors on the order of 6mm. Registrations using a combination of both datasets achieved TRE on the order of 2.5mm and represent a sizeable improvement over either dataset alone.

Three of the most important forces driving medical care are: patient specificity, treatment specificity and the move from discovery to design. Engineers while trained in specificity, efficiency, and design are often not trained in either biology or medical processes. Yet they are increasing critical to medical care. For example, modern medical imaging at US hospitals generates 1 exabyte (10^18 bytes) of data per year clearly beyond unassisted human analysis. It is not desirable to involve engineers in the acquisition, storage and analysis of this data, it is essential. While in the past we have nibbled around the edges of medical care, it is time and perhaps past time to insert ourselves more squarely into medical processes, making them more efficient, more specific and more robust. This requires engineers who understand biology and physicians who are willing to step away from classic medical thinking to try new approaches. But once the idea is proven in a laboratory, it must move into use and then into common practice. This requires additional engineering to make the process robust to noisy data and imprecise practices as well as workflow analysis to get the new technique into operating and treatment rooms. True innovation and true translation will require physicians, engineers, other medical stakeholders and even corporate involvement to take a new, important idea and move it not just to a patient but to all patients.

Dartmouth and Medtronic Navigation have established an academic-industrial partnership to develop, validate, and evaluate a multi-modality neurosurgical image-guidance platform for brain tumor resection surgery that is capable of updating the spatial relationships between preoperative images and the current surgical field. A stereovision system has been developed and optimized for intraoperative use through integration with a surgical microscope and an image-guided surgery system. The microscope optics and stereovision CCD sensors are localized relative to the surgical field using optical tracking and can efficiently acquire stereo image pairs from which a localized 3D profile of the exposed surface is reconstructed. This paper reports the first demonstration of intraoperative acquisition, reconstruction and visualization of 3D stereovision surface data in the context of an industry-standard image-guided surgery system. The integrated system is capable of computing and presenting a stereovision-based update of the exposed cortical surface in less than one minute. Alternative methods for visualization of high-resolution, texture-mapped stereovision surface data are also investigated with the objective of determining the technical feasibility of direct incorporation of intraoperative stereo imaging into future iterations of Medtronic’s navigation platform.

In laparoscopic surgery, live video provides visualization of the exposed organ surfaces in the surgical field, but is unable to show internal structures beneath those surfaces. The laparoscopic ultrasound is often used to visualize the internal structures, but its use is limited to intermittent confirmation because of the need for an extra hand to maneuver the ultrasound probe. Other limitations of using ultrasound are the difficulty of interpretation and the need for an extra port. The size of the ultrasound transducer may also be too large for its usage in small children. In this paper, we report on an augmented reality (AR) visualization system that features continuous hands-free volumetric ultrasound scanning of the surgical anatomy and video imaging from a stereoscopic laparoscope. The acquisition of volumetric ultrasound image is realized by precisely controlling a back-and-forth movement of an ultrasound transducer mounted on a linear slider. Furthermore, the ultrasound volume is refreshed several times per minute. This scanner will sit outside of the body in the envisioned use scenario and could be even integrated into the operating table. An overlay of the maximum intensity projection (MIP) of ultrasound volume on the laparoscopic stereo video through geometric transformations features an AR visualization system particularly suitable for children, because ultrasound is radiation-free and provides higher-quality images in small patients. The proposed AR representation promises to be better than the AR representation using ultrasound slice data.

In recent years, bone-attached robots and microstereotactic frames have attracted increasing interest due to the promising targeting accuracy they provide. Such devices attach to a patient's skull via bone anchors, which are used as landmarks during intervention planning as well. However, as simulation results reveal, the performance of such mechanisms is limited by errors occurring during the localization of their bone anchors in preoperatively acquired computed tomography images. Therefore, it is desirable to identify the most suitable fiducials as well as the most accurate method for fiducial localization. We present experimental results of a study focusing on the fiducial localization error (FLE) of spheres. Two phantoms equipped with fiducials made from ferromagnetic steel and titanium, respectively, are used to compare two clinically available imaging modalities (multi-slice CT (MSCT) and cone-beam CT (CBCT)), three localization algorithms as well as two methods for approximating the FLE. Furthermore, the impact of cubic interpolation applied to the images is investigated. Results reveal that, generally, the achievable localization accuracy in CBCT image data is significantly higher compared to MSCT imaging. The lowest FLEs (approx. 40 μm) are obtained using spheres made from titanium, CBCT imaging, template matching based on cross correlation for localization, and interpolating the images by a factor of sixteen. Nevertheless, the achievable localization accuracy of spheres made from steel is only slightly inferior. The outcomes of the presented study will be valuable considering the optimization of future microstereotactic frame prototypes as well as the operative workflow.

Over the last years, several methods have been proposed to guide the physician during reduction and fixation of bone fractures. Available solutions often use bulky instrumentation inside the operating room (OR). The latter ones usually consist of a stereo camera, placed outside the operative field, and optical markers directly attached to both the patient and the surgical instrumentation, held by the surgeon. Recently proposed techniques try to reduce the required additional instrumentation as well as the radiation exposure to both patient and physician. In this paper, we present the adaptation and the first implementation of our recently proposed video camera-based solution for screw fixation guidance. Based on the simulations conducted in our previous work, we mounted a small camera on a drill in order to recover its tip position and axis orientation w.r.t our custom-made drill sleeve with attached markers. Since drill-position accuracy is critical, we thoroughly evaluated the accuracy of our implementation. We used an optical tracking system for ground truth data collection. For this purpose, we built a custom plate reference system and attached reflective markers to both the instrument and the plate. Free drilling was then performed 19 times. The position of the drill axis was continuously recovered using both our video camera solution and the tracking system for comparison. The recorded data covered targeting, perforation of the surface bone by the drill bit and bone drilling. The orientation of the instrument axis and the position of the instrument tip were recovered with an accuracy of 1:60 +/- 1:22° and 2:03 +/- 1:36 mm respectively.

Radiation therapy is used to treat cancer by delivering high-dose radiation to a pre-defined target volume. Ultrasound (US) has the potential to provide real-time, image-guidance of radiation therapy to identify when a target moves outside of the treatment volume (e.g. due to breathing), but the associated probe-induced tissue deformation causes local anatomical deviations from the treatment plan. If the US probe is placed to achieve similar tissue deformations in the CT images required for treatment planning, its presence causes streak artifacts that will interfere with treatment planning calculations. To overcome these challenges, we propose robot-assisted placement of a real ultrasound probe, followed by probe removal and replacement with a geometrically-identical, CT-compatible model probe. This work is the first to investigate in vivo deformation reproducibility with the proposed approach. A dog's prostate, liver, and pancreas were each implanted with three 2.38-mm spherical metallic markers, and the US probe was placed to visualize the implanted markers in each organ. The real and model probes were automatically removed and returned to the same position (i.e. position control), and CT images were acquired with each probe placement. The model probe was also removed and returned with the same normal force measured with the real US probe (i.e. force control). Marker positions in CT images were analyzed to determine reproducibility, and a corollary reproducibility study was performed on ex vivo tissue. In vivo results indicate that tissue deformations with the real probe were repeatable under position control for the prostate, liver, and pancreas, with median 3D reproducibility of 0.3 mm, 0.3 mm, and 1.6 mm, respectively, compared to 0.6 mm for the ex vivo tissue. For the prostate, the mean 3D tissue displacement errors between the real and model probes were 0.2 mm under position control and 0.6 mm under force control, which are both within acceptable radiotherapy treatment margins. The 3D displacement errors between the real and model probes were less acceptable for the liver and pancreas (4.1-6.1 mm), and force control maintained poorer reproducibility than position control.

Magnetic resonance imaging (MRI) guided transperineal targeted prostate biopsy has become a valuable instrument for detection of prostate cancer in patients with continuing suspicion for aggressive cancer after transrectal ultrasound guided (TRUS) guided biopsy. The MRI-guided procedures are performed using mechanical targeting devices or templates, which suffer from limitations of spatial sampling resolution and/or manual in-bore adjustments. To overcome these limitations, we developed and clinically deployed an MRI-compatible piezoceramic-motor actuated needle guidance device, Smart Template, which allows automated needle guidance with high targeting resolution for use in a wide closed-bore 3-Tesla MRI scanner. One of the main limitations of the MRI-guided procedure is the lengthy procedure time compared to conventional TRUS-guided procedures. In order to optimize the procedure, we assessed workflow of 30 MRI-guided biopsy procedures using the Smart Template with focus on procedure time. An average of 3.4 (range: 2~6) targets were preprocedurally selected per procedure and 2.2 ± 0.8 biopsies were performed for each target with an average insertion attempt of 1.9 ± 0.7 per biopsy. The average technical preparation time was 14 ± 7 min and the in-MRI patient preparation time was 42 ± 7 min. After 21 ± 7 min of initial imaging, 64 ± 12 min of biopsy was performed yielding an average of 10 ± 2 min per tissue sample. The total procedure time occupying the MRI suite was 138 ± 16 min. No noticeable tendency in the length of any time segment was observed over the 30 clinical cases.

Laparoscopic surgery is a minimally invasive surgical approach in which surgical instruments are passed through ports placed at small incisions. This approach can benefit patients by reducing recovery times and scars. Surgeons have gained greater dexterity, accuracy, and vision through adoption of robotic surgical systems. However, in some cases a preselected set of ports cannot be accommodated by the robot; the robot’s arms may cause collisions during the procedure, or the surgical targets may not be reachable through the selected ports. In this case, the surgeon must either make more incisions for more ports, or even abandon the laparoscopic approach entirely. To assist in this, we are building an easytouse system which, given a surgical task and preoperative medical images of the patient, will recommend a suitable port placement plan for the robotic surgery. This work bears two main contributions: 1) a high level user interface that assists the surgeon in operating the complicated underlying planning algorithm; and 2) an interface to assist the surgical team in implementation of the recommended plan in the operating room. We believe that such an automated port placement system would reduce setup time for robotic surgery and reduce the morbidity to patients caused by unsuitable surgical port placement.

Otologic surgery often involves a mastoidectomy procedure, in which part of the temporal bone is milled away in order to visualize critical structures embedded in the bone and safely access the middle and inner ear. We propose to automate this portion of the surgery using a compact, bone-attached milling robot. A high level of accuracy is required t o avoid damage to vital anatomy along the surgical path, most notably the facial nerve, making this procedure well-suited for robotic intervention. In this study, several of the design considerations are discussed and a robot design and prototype are presented. The prototype is a 4 degrees-of-freedom robot similar to a four-axis milling machine that mounts to the patient's skull. A positioning frame, containing fiducial markers and attachment points for the robot, is rigidly attached to the skull of the patient, and a CT scan is acquired. The target bone volume is manually segmented in the CT by the surgeon and automatically converted to a milling path and robot trajectory. The robot is then attached to the positioning frame and is used to drill the desired volume. The accuracy of the entire system (image processing, planning, robot) was evaluated at several critical locations within or near the target bone volume with a mean free space accuracy result of 0.50 mm or less at all points. A milling test in a phantom material was then performed to evaluate the surgical workflow. The resulting milled volume did not violate any critical structures.

In biomechanical simulations of the human breast, the analysed geometry is often reconstructed from in vivo medical imaging procedures. For example in dynamic contrast enhanced magnetic resonance imaging, the acquired geometry of the patient's breast when lying in the prone position represents a deformed configuration that is pre-stressed by typical in vivo conditions and gravity. Thus, physically realistic simulations require consideration of this loading and, hence, establishing the undeformed configuration is an important task for accurate and reliable biomechanical modelling of the breast. We compare three different numerical approaches to recover the unloaded configuration from the loaded geometry given patient-specific biomechanical models built from prone and supine MR images. The algorithms compared are:(i) the simple inversion of gravity without the consideration of pre-stresses, (ii) an inversefinite deformation approach and (iii) afixed point type iterative approach which uses only forward simulations. It is shown that the iterative and the inverse approach produce similar zero-gravity estimates, where as the simple inversion of gravity is only appropriate for small or highly constrained deformations.

Accurate measurement of soft tissue material properties is critical for characterizing its biomechanical behaviors but can be challenging especially for the human brain in vivo. In this study, we investigated the feasibility of inducing and detecting cortical surface deformation intraoperatively for patients undergoing open skull neurosurgeries. A custom diskshaped indenter made of high-density tungsten (diameter of 15 mm with a thickness of 6 mm) was used to induce deformation on the brain cortical surface immediately after dural opening. Before and after placing the indenter, sequences (typically 250 frames at 15 frames-per-second, or ~17 seconds) of high-resolution stereo image pairs were acquired to capture the harmonic motion of the exposed cortical surface as due to blood pressure pulsation and respiration. For each sequence with the first left image serving as a baseline, an optical-flow motion-tracking algorithm was used to detect in-sequence cortical surface deformation. The resulting displacements of the exposed features within the craniotomy were spatially averaged to identify the temporal frames corresponding to motion peak magnitudes. Corresponding image pairs were then selected to reconstruct full-field three-dimensional (3D) cortical surfaces before and after indentation, respectively, from which full 3D displacement fields were obtained by registering their projection images. With one clinical patient case, we illustrate the feasibility of the technique in detecting indenter-induced cortical surface deformation in order to allow subsequent processing to determine material properties of the brain in vivo.

Traditional 2D images provide limited use for accurate planning of spine interventions, mainly due to the complex 3D anatomy of the spine and close proximity of nerve bundles and vascular structures that must be avoided during the procedure. Our previously developed clinician-friendly platform for spine surgery planning takes advantage of 3D pre-operative images, to enable oblique reformatting and 3D rendering of individual or multiple vertebrae, interactive templating, and placement of virtual pedicle implants. Here we extend the capabilities of the planning platform and demonstrate how the virtual templating approach not only assists with the selection of the optimal implant size and trajectory, but can also be augmented to provide surrogate estimates of the fastening strength of the implanted pedicle screws based on implant dimension and bone mineral density of the displaced bone substrate. According to the failure theories, each screw withstands a maximum holding power that is directly proportional to the screw diameter (D), the length of the in-bone segm,ent of the screw (L), and the density (i.e., bone mineral density) of the pedicle body. In this application, voxel intensity is used as a surrogate measure of the bone mineral density (BMD) of the pedicle body segment displaced by the screw. We conducted an initial assessment of the developed platform using retrospective pre- and post-operative clinical 3D CT data from four patients who underwent spine surgery, consisting of a total of 26 pedicle screws implanted in the lumbar spine. The Fastening Strength of the planned implants was directly assessed by estimating the intensity - area product across the pedicle volume displaced by the virtually implanted screw. For post-operative assessment, each vertebra was registered to its homologous counterpart in the pre-operative image using an intensity-based rigid registration followed by manual adjustment. Following registration, the Fastening Strength was computed for each displaced bone segment. According to our preliminary clinical study, a comparison between Fastening Strength, displaced bone volume and mean voxel intensity showed similar results (p < 0.1) between the virtually templated plans and the post-operative outcome following the traditional clinical approach. This study has demonstrated the feasibility of the platform in providing estimates the pedicle screw fastening strength via virtual implantation, given the intrinsic vertebral geometry and bone mineral density, enabling the selection of the optimal implant dimension adn trajectory for improved strength.

The skills required for obtaining informative x-ray fluoroscopy images are currently acquired while trainees provide clinical care. As a consequence, trainees and patients are exposed to higher doses of radiation. Use of simulation has the potential to reduce this radiation exposure by enabling trainees to improve their skills in a safe environment prior to treating patients. We describe a low cost, high fidelity, fluoroscopy simulation system. Our system enables operators to practice their skills using the clinical device and simulated x-rays of a virtual patient. The patient is represented using a set of temporal Computed Tomography (CT) images, corresponding to the underlying dynamic processes. Simulated x-ray images, digitally reconstructed radiographs (DRRs), are generated from the CTs using ray-casting with customizable machine specific imaging parameters. To establish the spatial relationship between the CT and the fluoroscopy device, the CT is virtually attached to a patient phantom and a web camera is used to track the phantom’s pose. The camera is mounted on the fluoroscope’s intensifier and the relationship between it and the x-ray source is obtained via calibration. To control image acquisition the operator moves the fluoroscope as in normal operation mode. Control of zoom, collimation and image save is done using a keypad mounted alongside the device’s control panel. Implementation is based on the Image-Guided Surgery Toolkit (IGSTK), and the use of the graphics processing unit (GPU) for accelerated image generation. Our system was evaluated by 11 clinicians and was found to be sufficiently realistic for training purposes.

Cochlear Implant (CI) surgery is a procedure in which an electrode array is inserted into the cochlea. The electrode array is used to stimulate auditory nerve fibers and restore hearing for people with severe to profound hearing loss. The primary goals when placing the electrode array are to fully insert the array into the cochlea while minimizing trauma to the cochlea. Studying the relationship between surgical outcome and various surgical techniques has been difficult since trauma and electrode placement are generally unknown without histology. Our group has created a CI placement simulator that combines an interactive 3D visualization environment with a haptic-feedback-enabled controller. Surgical techniques and patient anatomy can be varied between simulations so that outcomes can be studied under varied conditions. With this system, we envision that through numerous trials we will be able to statistically analyze how outcomes relate to surgical techniques. As a first test of this system, in this work, we have designed an experiment in which we compare the spatial distribution of forces imparted to the cochlea in the array insertion procedure when using two different but commonly used surgical techniques for cochlear access, called round window and cochleostomy access. Our results suggest that CIs implanted using round window access may cause less trauma to deeper intracochlear structures than cochleostomy techniques. This result is of interest because it challenges traditional thinking in the otological community but might offer an explanation for recent anecdotal evidence that suggests that round window access techniques lead to better outcomes.

Brachytherapy as one of the treatment methods for prostate cancer takes place by implantation of radioactive seeds inside the gland. The standard of care for this treatment procedure is to acquire transrectal ultrasound images of the prostate which are segmented in order to plan the appropriate seed placement. The segmentation process is usually performed either manually or semi-automatically and is associated with subjective errors because the prostate visibility is limited in ultrasound images. The current segmentation process also limits the possibility of intra-operative delineation of the prostate to perform real-time dosimetry. In this paper, we propose a computationally inexpensive and fully automatic segmentation approach that takes advantage of previously segmented images to form a joint space of images and their segmentations. We utilize joint Independent Component Analysis method to generate a model which is further employed to produce a probability map of the target segmentation. We evaluate this approach on the transrectal ultrasound volume images of 60 patients using a leave-one-out cross-validation approach. The results are compared with the manually segmented prostate contours that were used by clinicians to plan brachytherapy procedures. We show that the proposed approach is fast with comparable accuracy and precision to those found in previous studies on TRUS segmentation.

In targeted 3D transrectal ultrasound (TRUS)-guided biopsy, patient and prostate movement during the procedure can cause target misalignments that hinder accurate sampling of pre-planned suspicious tissue locations. Multiple solutions have been proposed for motion compensation via registration of intra-procedural TRUS images to a baseline 3D TRUS image acquired at the beginning of the biopsy procedure. While 2D TRUS images are widely used for intra-procedural guidance, some solutions utilize richer intra-procedural images such as bi- or multi-planar TRUS or 3D TRUS, acquired by specialized probes. In this work, we measured the impact of such richer intra-procedural imaging on motion compensation accuracy, to evaluate the tradeoff between cost and complexity of intra-procedural imaging versus improved motion compensation. We acquired baseline and intra-procedural 3D TRUS images from 29 patients at standard sextant-template biopsy locations. We used the planes extracted from the 3D intra-procedural scans to simulate 2D and 3D information available in different clinically relevant scenarios for registration. The registration accuracy was evaluated by calculating the target registration error (TRE) using manually identified homologous fiducial markers (micro-calcifications). Our results indicate that TRE improves gradually when the number of intra-procedural imaging planes used in registration is increased. Full 3D TRUS information helps the registration algorithm to robustly converge to more accurate solutions. These results can also inform the design of a fail-safe workflow during motion compensation in a system using a tracked 2D TRUS probe, by prescribing rotational acquisitions that can be performed quickly and easily by the physician immediately prior to needle targeting.

Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided “fusion” prostate biopsy aims to reduce the ~23% false negative rate of clinical 2D TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsy still yields false negatives. Therefore, we propose optimization of biopsy targeting to meet the clinician’s desired tumor sampling probability, optimizing needle targets within each tumor and accounting for uncertainties due to guidance system errors, image registration errors, and irregular tumor shapes. We obtained multiparametric MRI and 3D TRUS images from 49 patients. A radiologist and radiology resident contoured 81 suspicious regions, yielding 3D surfaces that were registered to 3D TRUS. We estimated the probability, P, of obtaining a tumor sample with a single biopsy. Given an RMS needle delivery error of 3.5 mm for a contemporary fusion biopsy system, P ≥ 95% for 21 out of 81 tumors when the point of optimal sampling probability was targeted. Therefore, more than one biopsy core must be taken from 74% of the tumors to achieve P ≥ 95% for a biopsy system with an error of 3.5 mm. Our experiments indicated that the effect of error along the needle axis on the percentage of core involvement (and thus the measured tumor burden) was mitigated by the 18 mm core length.

Laser interstitial thermotherapy (LITT) is a relatively new focal therapy technique for the ablation of localized prostate cancer. However, very little is known about the specific effects of LITT within the ablation zone and the surrounding normal tissue regions. For instance, it is important to be able to assess the extent of residual cancer within the prostate following LITT, which may be masked by thermally induced benign necrotic changes. Fortunately LITT is MRI compatible and hence this allows for quantitatively assessing LITT induced changes via multi-parametric MRI. Of course definite validation of any LITT induced changes on MRI requires confirmation via histopathology. The aim of this study was to quantitatively assess and distinguish the imaging characteristics of prostate cancer and benign confounding treatment changes following LITTon 3 Tesla multi-parametric MRI by carefully mapping the treatment related changes from the ex vivo surgically resected histopathologic specimens onto the pre-operative in vivo imaging. A better understanding of the imaging characteristics of residual disease and successfully ablated tissue might lead to improved treatment monitoring and as such patient prognosis. A unique clinical trial at the Radboud University Medical Center, in which 3 patients underwent a prostatectomy after LITT treatment, yielded ex-vivo histopathologic specimens along with pre- and post-LITT MRI. Using this data we (1) identified the computer extracted MRI signatures associated with treatment effects including benign necrotic changes and residual disease and (2) subsequently evaluated the computer extracted MRI features previously identified in distinguishing LITT induced changes in the ablated area relative to the residual disease. Towards this end first a pathologist annotated the ablated area and the residual disease on the ex-vivo histology and then we transferred the annotations to the post-LITT MRI using semi-automatic elastic registration. The pre- and post-LITT MRI were subsequently registered and computer-derived multi-parametric MRI features extracted to determine differences in feature values between residual disease and successfully ablated tissue to assess treatment response. A scoring metric allowed us to identify those specific computer-extracted MRI features that maximally and differentially expressed between the ablated regions and the residual cancer, on a voxel- by­ voxel basis. Finally, we used a Fuzzy C-Means algorithm to assess the discriminatory power of these selected features. Our results show that specific computer-extracted features from multi-parametric MRI differentially express within the ablated and residual cancer regions, as evidenced by our ability to, on a voxel-by-voxel basis, classify tissue as residual disease. Additionally, we show that change of feature values between pre- and post­-LITT MRI may be useful as a quantitative marker for treatment response (T2-weighted texture and DCE MRI features showed largest differences between residual disease and successfully ablated tissue). Finally, a clustering approach to separate treatment effects and residual disease incorporating both (1) and (2) yielded a maximum area under the ROC curve of 0.97 on a voxel basis across 3 studies.

Focal therapy of localized prostate cancer is receiving increased attention due to its potential for providing effective cancer control in select patients with minimal treatment-related side effects. Magnetic resonance imaging (MRI)-guided focal laser ablation (FLA) therapy is an attractive modality for such an approach. In FLA therapy, accurate placement of laser fibers is critical to ensuring that the full target volume is ablated. In practice, error in needle placement is invariably present due to pre- to intra-procedure image registration error, needle deflection, prostate motion, and variability in interventionalist skill. In addition, some of these sources of error are difficult to control, since the available workspace and patient positions are restricted within a clinical MRI bore. In an attempt to take full advantage of the utility of intraprocedure MRI, while minimizing error in needle placement, we developed an MRI-compatible mechatronic system for guiding needles to the prostate for FLA therapy. The system has been used to place interstitial catheters for MRI-guided FLA therapy in eight subjects in an ongoing Phase I/II clinical trial. Data from these cases has provided quantification of the level of uncertainty in needle placement error. To relate needle placement error to clinical outcome, we developed a model for predicting the probability of achieving complete focal target ablation for a family of parameterized treatment plans. Results from this work have enabled the specification of evidence-based selection criteria for the maximum target size that can be confidently ablated using this technique, and quantify the benefit that may be gained with improvements in needle placement accuracy.

Gynecologic malignancies, including cervical, endometrial, ovarian, vaginal and vulvar cancers, cause significant mortality in women worldwide. The standard care for many primary and recurrent gynecologic cancers consists of chemoradiation followed by brachytherapy. In high dose rate (HDR) brachytherapy, intracavitary applicators and /or interstitial needles are placed directly inside the cancerous tissue so as to provide catheters to deliver high doses of radiation. Although technology for the navigation of catheters and needles is well developed for procedures such as prostate biopsy, brain biopsy, and cardiac ablation, it is notably lacking for gynecologic HDR brachytherapy. Using a benchtop study that closely mimics the clinical interstitial gynecologic brachytherapy procedure, we developed a method for evaluating the accuracy of image-guided catheter placement. Future bedside translation of this technology offers the potential benefit of maximizing tumor coverage during catheter placement while avoiding damage to the adjacent organs, for example bladder, rectum and bowel. In the study, two independent experiments were performed on a phantom model to evaluate the targeting accuracy of an electromagnetic (EM) tracking system. The procedure was carried out using a laptop computer (2.1GHz Intel Core i7 computer, 8GB RAM, Windows 7 64-bit), an EM Aurora tracking system with a 1.3mm diameter 6 DOF sensor, and 6F (2 mm) brachytherapy catheters inserted through a Syed-Neblett applicator. The 3D Slicer and PLUS open source software were used to develop the system. The mean of the targeting error was less than 2.9mm, which is comparable to the targeting errors in commercial clinical navigation systems.

Dilated lateral ventricles in neonates can be due to many different causes, such as brain loss, or congenital malformation; however, the main cause is hydrocephalus, which is the accumulation of fluid within the ventricular system. Hydrocephalus can raise intracranial pressure resulting in secondary brain damage, and up to 25% of patients with severely enlarged ventricles have epilepsy in later life. Ventricle enlargement is clinically monitored using 2D US through the fontanels. The sensitivity of 2D US to dilation is poor because it cannot provide accurate measurements of irregular volumes such as the ventricles, so most clinical evaluations are of a qualitative nature. We developed a 3D US system to image the cerebral ventricles of neonates within the confines of incubators that can be easily translated to more open environments. Ventricle volumes can be segmented from these images giving a quantitative volumetric measurement of ventricle enlargement without moving the patient into an imaging facility. In this paper, we report on in vivo validation studies: 1) comparing 3D US ventricle volumes before and after clinically necessary interventions removing CSF, and 2) comparing 3D US ventricle volumes to those from MRI. Post-intervention ventricle volumes were less than pre-intervention measurements for all patients and all interventions. We found high correlations (R = 0.97) between the difference in ventricle volume and the reported removed CSF with the slope not significantly different than 1 (p < 0.05). Comparisons between ventricle volumes from 3D US and MR images taken 4 (±3.8) days of each other did not show significant difference (p=0.44) between 3D US and MRI through paired t-test.

The freehand 3D ultrasound technique relies on position sensors attached to the probe to register the location of each image to a 3D space. However, the imprecision of the position sensors reduces the reliability of estimated image locations. In this paper, we propose a novel method to compute the positional uncertainty of an image plane. First, we use rigid body point-based registration to compute the error produced by each pixel of the image during the tracking. The Target Registration Error (TRE) is used to compute the covariance matrix of errors at each pixel position. This covariance matrix is then decomposed as a 3D orientation error, in the x, y and z directions. Considering a volume around the image, we introduce the Image Plane Crossing Probability (IPCP) to determine the probability that the plane passes through each voxel. The result is a point cloud probability around the image plane, where each voxel contains the crossing probability and the contribution of each direction of the error. Finally, a simple volume rendering technique is used to visualize the uncertainty of the plane position. The results are validated in two steps. The first step is a Monte Carlo simulation to validate the estimate of the TRE covariance for the tracking errors. The second step simulates TRE errors on a plane and validates the associated positional uncertainty.