This PDF file contains the front matter associated with SPIE Proceedings Volume 6916, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Coronary territory maps, which associate myocardial regions with the corresponding coronary artery that supply them, are a common visualization technique to assist the physician in the diagnosis of coronary artery disease. However, the commonly used visualization is based on the AHA-17-segment model, which is an empirical population based model. Therefore, it does not necessarily cope with the often highly individual coronary anatomy of a specific patient. In this paper we introduce a novel fully automatic approach to compute the patient individual coronary supply regions in CTA datasets. This approach is divided in three consecutive steps. First, the aorta is fully automatically located in the dataset with a combination of a Hough transform and a cylindrical model matching approach. Having the location of the aorta, a segmentation and skeletonization of the coronary tree is triggered. In the next step, the three main branches (LAD, LCX and RCX) are automatically labeled, based on the knowledge of the pose of the aorta and the left ventricle. In the last step the labeled coronary tree is projected on the left ventricular surface, which can afterward be subdivided into the coronary supply regions, based on a Voronoi transform. The resulting supply regions can be either shown in 3D on the epicardiac surface of the left ventricle, or as a subdivision of a polarmap.

Quantitative measurements of the progression (or regression) of carotid plaque burden are important in monitoring patients and evaluating new treatment options. 3D ultrasound (US) has been used to monitor the progression of carotid artery plaques in symptomatic and asymptomatic patients. Different methods of measuring various ultrasound phenotypes of atherosclerosis have been developed. In this work, we extended concepts used in intima-media thickness (IMT) measurements based on 2D images and introduced a metric called 3D vessel-wall-plus-plaque thickness (3D VWT), which was obtained by computing the distance between the carotid wall and lumen surfaces on a point-by-point basis in a 3D image of the carotid arteries. The VWT measurements were then superimposed on the arterial wall to produce the VWT map. Since the progression of plaque thickness is important in monitoring patients who are at risk for stroke, we also computed the change of VWT by comparing the VWT maps obtained for a patient at two different time points. In order to facilitate the visualization and interpretation of the 3D VWT and VWT-Change maps, we proposed a technique to flatten these maps in an area-preserving manner.

Circumferential strain of the left ventricle reflects myocardial contractility and is considered a key index of cardiac function. It is also an important parameter in the quantitative evaluation of heart failure. Circumferential compression encoding, CIRCOME, is a novel method in cardiac MRI to evaluate this strain non-invasively and quickly. This strain encoding technique avoids the explicit measurement of the displacement field and does not require calculation of strain through spatial differentiation. CIRCOME bypasses these two time-consuming and noise sensitive steps by directly using the frequency domain (k-space) information from radially tagged myocardium, before and after deformation. It uses the ring-shaped crown region of the k-space, generated by the taglines, to reconstruct circumferentially compression-weighted images of the heart before and after deformation. CIRCOME then calculates the circumferential strain through relative changes in the compression level of corresponding regions before and after deformation. This technique can be implemented in 3D as well as 2D and may be employed to estimate the overall global or regional circumferential strain. The main parameters that affect the accuracy of this method are spatial resolution, signal to noise ratio, eccentricity of the center of radial taglines their fading and their density. Also, a variety of possible image reconstruction and filtering options may influence the accuracy of the method. This study describes the pulse sequence, algorithm, influencing factors and limiting criteria for CIRCOME and provides the simulated results.

Purpose: To assess the performance of a motion map algorithm to automatically determine the optimal systolic and diastolic reconstruction window for coronary CT Angiography using Dual Source CT. Materials and Methods: Dual Source coronary CT angiography data sets (Somatom Definition, Siemens Medical Solutions) from 50 consecutive patients were included in the analysis. Optimal systolic and diastolic reconstruction windows were determined using a motion map algorithm (BestPhase, Siemens Medical Solutions). Additionally data sets were reconstructed in 5% steps throughout the RR-interval. For each major vessel (RCA, LAD and LCX) an optimal systolic and diastolic reconstruction window was manually determined by two independent readers using volume rendering displays. Image quality was rated using a five-point scale (1 = no motion artifacts, 5 = severe motion artifacts over entire length of the vessel). Results: The mean heart rate during the scan was 72.4bpm (±15.8bpm). Median systolic and diastolic reconstruction windows using the BestPhase algorithm were at 37% and 73% RR. The median manually selected systolic reconstruction window was 35 %, 30% and 35% for RCA, LAD, and LCX. For all vessels the median observer selected diastolic reconstruction window was 75%. Mean image quality using the BestPhase algorithm was 2.4 ±0.9 for systolic reconstructions and 1.9 ±1.1 for diastolic reconstructions. Using the manual approach, the mean image quality was 1.9 ±0.5 and 1.7 ±0.8 respectively. There was a significant difference in image quality between automatically and manually determined systolic reconstructions (p<0.01) but there was no significant difference in image quality in diastolic reconstructions. Conclusion: Automatic determination of the optimal reconstruction interval using the BestPhase algorithm is feasible and yields reconstruction windows similar to observer selected reconstruction windows. In diastolic reconstructions overall image quality is similar to the image quality in manually selected reconstruction windows.

For assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3D) information on vessel morphology and hemodynamics. Rotational angiography is routinely used to determine 3D geometry, and we recently outlined a method to estimate the blood flow waveform and mean volumetric flow rate from images acquired using rotational angiography. Our method uses a model of contrast agent dispersion to estimate the flow parameters from the spatial and temporal progression of the contrast agent concentration, represented by a flow map. Artifacts due to the rotation of the c-arm are overcome by using a reliability map. An attenuation calibration can be used to support our method, but it might not be available in clinical practice. In this paper, we analyze the influence of the attenuation calibration on our method. Furthermore, we concentrate on the validation of the proposed algorithm, with particular emphasis on the influence of parameters such as the length of the analyzed vessel segment, the frame rate of the acquisition, and the duration of the injection on accuracy. For the validation, rotational angiographic image sequences from a computer simulation and from a phantom experiment were used. With a mean error of about 10% for the mean volumetric flow rate and about 13% for the blood flow waveform from the phantom experiments, we conclude that the method has the potential to give quantitative estimates of blood flow parameters during cerebrovascular interventions which are accurate enough to be clinically useful.

A novel fMRI technique has been developed to capture the dynamics of the evolution of brain activity during complex tasks such as those designed to evaluate the neural basis of decision-making under different situations. A task called the Iowa Gambling Task was used as an example. Six normal human volunteers were studied. The task was presented inside a 3T MRI and a dynamic fMRI study of the approximately 2s period between the beginning and end of the decision-making period was conducted by employing a series of reference functions, separated by 200 ms, designed to capture activation at different time-points within this period. As decision-making culminates with a button-press, the timing of the button press was chosen as the reference (t=0) and corresponding reference functions were shifted backward in steps of 200ms from this point up to the time when motor activity from the previous button press became predominant. SPM was used to realign, high-pass filter (cutoff 200s), normalize to the Montreal Neurological Institute (MNI) Template using a 12 parameter affine/non-linear transformation, 8mm Gaussian smoothing, and event-related General Linear Model analysis for each of the shifted reference functions. The t-score of each activated voxel was then examined to find its peaking time. A random effect analysis (p<0.05) showed prefrontal, parietal and bi-lateral hippocampal activation peaking at different times during the decision making period in the n=6 group study.

Fluid attenuated inversion recovery (FLAIR) detects the T2 prolongation in whiter matter lesions (WML) measured on a macroscopic scale, whereas diffusion tensor imaging (DTI) more specifically detects the white matter (WM) integrity alterations as measured by water diffusion on a microscopic scale. Both techniques have been widely used to evaluate WM changes associated with aging, dementia and cerebral vascular disease, however, the relationship between white matter lesions (FLAIR) and changes of DTI remains largely unknown. We addressed this issue using a voxel based correlation analysis between DTI and FLAIR images acquired from 33 elderly subjects at 4T. The WML volume and intensity were correlated the fraction anisotropy (FA) or mean diffusivity (MD) across all the subjects on a voxelwise basis. Our results revealed that significant DTI-WML correlations occur at regions overlapping the major WML distributions with moderate intensity, and that no significant correlations were detected in periventricular regions where the FLAIR intensities are particularly high. We investigated WM degeneration as a continuum from normal WM to cerebrospinal fluid (CSF) using a two-compartment WM model. The simulation results indicated that the FLAIR intensity of WML reaches a maximum when the lesion severity is around 0.7, which is the same point where correlations between DTI and WML disappear. Based on these findings, WM degeneration in elderly subjects may be better characterized by using regional DTI-WML correlations in different stages of WM degeneration. DTI and FLAIR, taken together improve specificity for characterization of WM degeneration than each measure alone.

Using optimized voxel-based morphometry (VBM), this study systematically investigated gender differences in brain development through magnetic resonance imaging (MRI) data in 158 Chinese normal children and adolescents aged 7.26 to 22.80 years (mean age 15.03±4.70 years, 78 boys and 80 girls). Gender groups were matched for measures of age, handedness, education level. The customized brain templates, including T₁-weighted image and gray matter (GM)/white matter (WM)/cerebro-spinal fluid (CSF) prior probability maps, were created from all participants. Results showed that the total intracranial volume (TIV), global absolute GM and global WM volume in girls were significantly smaller than those in boys. The hippocampus grew faster in girls than that in boys, but the amygdala grew faster in boys than that in girls. The rate of regional GM decreases with age was steeper in the left superior parietal lobule, bilateral inferior parietal lobule, left precuneus, and bilateral supramarginal gyrus in boys compared to girls, which was possibly related to better spatial processing ability in boys. Regional GM volumes were greater in bilateral superior temporal gyrus, bilateral inferior frontal gyrus and bilateral middle frontal gyrus in girls. Regional WM volumes were greater in the left temporal lobe, right inferior parietal and bilateral middle frontal gyrus in girls. The gender differences in the temporal and frontal lobe maybe be related to better language ability in girls. These findings may aid in understanding the differences in cognitive function between boys and girls.

The height and width of colonic polyps are important characteristics to evaluate the status and malignancy of polyps. We borrow the idea from geographic information systems to employ topographic height maps to compute the polyp height and width. The height map is generated using a ray-casting algorithm through an orthogonal projection. A concentric index is devised to gauge the quality of the height map and is maximized in a multi-scale spiral spherical search for the optimal projection. We then locate the polyp tip and neck using directional height profiles, and derive height and width measurement based on geometrical analysis. We manually measured the height and width of 58 polyps and performed paired t-tests between manual measurement and height map measurement. The test shows that Pearson correlation is 0.742 and P(T<=t) is 0.01 for height measurement; and Pearson correlation is 0.663 and P(T<=t) is 0.002 for width measurement.

CT colonography is a minimally-invasive screening technique for colorectal polyps in which X-ray CT images of the distended colon are acquired, usually in the prone and supine positions. Registration of segmented colons from both images will be useful for computer-assisted polyp detection. We have previously presented algorithms for registration of the prone and supine colon when both are well distended and there is a single connected lumen. However due to inadequate bowel preparation or peristalsis there may be collapsed segments in one or both of the colons resulting in a topological change in the images. Such changes make deformable registrations of the colons difficult, and at present there are no registration algorithms which can accommodate them. In this paper we present an algorithm which can perform volume registration of prone/supine colon images in the presence of a topological change.

This paper proposes a method for extracting teniae coli from abdominal CT volumes. In CT colonography, two types of CT volumes are taken in prone- and supine-positions. These images are used for preventing misdiagnosis caused by fluid stool. However, since the shape of the colon easily changes, it is difficult to find correspondences between such two volumes. Radiologists need to carefully read both prone- and supine-position volumes. This imposes heavy loads on radiologists who need to CT volumes for CT colonography. Development of a method for registering prone- and supine-position volumes to assist diagnostic process is strongly expected to be developed for reduction of radiologists' load. This paper shows a fully automated method to extract teniae coli regions, which are one of promising land marks of the colon, from CT volumes. Teniae coli region information would be useful for registering CT volumes taken in prone- and supine-positions. Since teniae coli are observed as sequences of ridge-breaks traversing haustral folds, they can be extracted by analyzing positions of the haustral folds. First, we extract haustral folds regions based on curvatures on the colonic wall. Three-lines passing through gravity centers of haustral folds are computed. Teniae coli regions are extracted as lines running between these three lines. We applied the proposed method to eight cases of colonic CT volumes. The experimental results showed that the proposed method was able to extract teniae coli satisfactorily for cases where haustral folds were extracted correctly.

Many computer aided diagnosis (CAD) schemes have been developed for colon cancer detection using Virtual Colonoscopy (VC). In earlier work, we developed an automatic polyp detection method integrating flow visualization techniques, that forms part of the CAD functionality of an existing Virtual Colonoscopy pipeline. Curvature streamlines were used to characterize polyp surface shape. Features derived from curvature streamlines correlated highly with true polyp detections. During testing with a large number of patient data sets, we found that the correlation between streamline features and true polyps could be affected by noise and our streamline generation technique. The seeding and spacing constraints and CT noise could lead to streamline fragmentation, which reduced the discriminating power of our streamline features. In this paper, we present two major improvements of our curvature streamline generation. First, we adapted our streamline seeding strategy to the local surface properties and made the streamline generation faster. It generates a significantly smaller number of seeds but still results in a comparable and suitable streamline distribution. Second, based on our observation that longer streamlines are better surface shape descriptors, we improved our streamline tracing algorithm to produce longer streamlines. Our improved techniques are more effcient and also guide the streamline geometry to correspond better to colonic surface shape. These two adaptations support a robust and high correlation between our streamline features and true positive detections and lead to better polyp detection results.

Virtual colonoscopy (VC) is a noninvasive method for colonic polyp screening, by reconstructing three-dimensional models of the colon using computerized tomography (CT). In virtual colonoscopy fly-through navigation, it is crucial to generate an optimal camera path for efficient clinical examination. In conventional methods, the centerline of the colon lumen is usually used as the camera path. In order to extract colon centerline, some time consuming pre-processing algorithms must be performed before the fly-through navigation, such as colon segmentation, distance transformation, or topological thinning. In this paper, we present an efficient image-based path planning algorithm for automated virtual colonoscopy fly-through navigation without the requirement of any pre-processing. Our algorithm only needs the physician to provide a seed point as the starting camera position using 2D axial CT images. A wide angle fisheye camera model is used to generate a depth image from the current camera position. Two types of navigational landmarks, safe regions and target regions are extracted from the depth images. Camera position and its corresponding view direction are then determined using these landmarks. The experimental results show that the generated paths are accurate and increase the user comfort during the fly-through navigation. Moreover, because of the efficiency of our path planning algorithm and rendering algorithm, our VC fly-through navigation system can still guarantee 30 FPS.

This paper presents a method for automated anatomical labeling of bronchial branches (ALBB) extracted from 3D CT datasets. The proposed method constructs classifiers that output anatomical names of bronchial branches by employing the machine-learning approach. We also present its application to a bronchoscopy guidance system. Since the bronchus has a complex tree structure, bronchoscopists easily tend to get disoriented and lose the way to a target location. A bronchoscopy guidance system is strongly expected to be developed to assist bronchoscopists. In such guidance system, automated presentation of anatomical names is quite useful information for bronchoscopy. Although several methods for automated ALBB were reported, most of them constructed models taking only variations of branching patterns into account and did not consider those of running directions. Since the running directions of bronchial branches differ greatly in individuals, they could not perform ALBB accurately when running directions of bronchial branches were different from those of models. Our method tries to solve such problems by utilizing the machine-learning approach. Actual procedure consists of three steps: (a) extraction of bronchial tree structures from 3D CT datasets, (b) construction of classifiers using the multi-class AdaBoost technique, and (c) automated classification of bronchial branches by using the constructed classifiers. We applied the proposed method to 51 cases of 3D CT datasets. The constructed classifiers were evaluated by leave-one-out scheme. The experimental results showed that the proposed method could assign correct anatomical names to bronchial branches of 89.1% up to segmental lobe branches. Also, we confirmed that it was quite useful to assist the bronchoscopy by presenting anatomical names of bronchial branches on real bronchoscopic views.

Bronchoscopy is often performed for diagnosing lung cancer. The recent development of multidetector CT (MDCT) scanners and ultrathin bronchoscopes now enable the bronchoscopic biopsy and treatment of peripheral regions of interest (ROIs). Because the peripheral ROIs are often located several generations within the airway tree, careful planning is required prior to a procedure. The current practice for planning peripheral bronchoscopic procedures, however, is difficult, error-prone, and time-consuming. We propose a system for planning peripheral bronchoscopic procedures using patient-specific MDCT chest scans. The planning process begins with a semi-automatic segmentation of ROIs. The remaining system components are completely automatic, beginning with a new strategy for tracheobronchial airway-tree segmentation. The system then uses a new locally-adaptive approach for finding the interior airway-wall surfaces. From the polygonal airway-tree surfaces, a centerline-analysis method extracts the central axes of the airway tree. The system's route-planning component then analyzes the data generated in the previous stages to determine an appropriate path through the airway tree to the ROI. Finally, an automated report generator gives quantitative data about the route and both static and dynamic previews of the procedure. These previews consist of virtual bronchoscopic endoluminal renderings at bifurcations encountered along the route and renderings of the airway tree and ROI at the suggested biopsy location. The system is currently in use for a human lung-cancer patient pilot study involving the planning and subsequent live image-based guidance of suspect peripheral cancer nodules.

Realistic surgical simulation requires both visual and biomechanical fidelity. In this paper, a technique is described where patient specific deformation can be incorporated into finite element modelling. Time dependant tissue deformation is captured in vivo via video bronchoscopy and estimated using image feature tracking. This motion is factored into rigid and non-rigid components via 2D/3D registration based on bronchoscope video and 3D tomographic reconstruction of the same patient. Non-rigid deformation is decomposed into a linear combination of localised deformations due to regional forces. Through optimisation, the forces are scaled over time to derive a physically plausible deformation without having to invert the finite element equations or minimising a system with a large degree of freedom. Error analysis demonstrates the viability of the method to reproduce deformations similar to that observed in bronchoscope video. Detailed analysis is provided for assessing the robustness of the method in the presence of outliers and missing landmarks.

We present a set of tools used to enhance the optical colonoscopy procedure in a novel manner with the aim of improving both the accuracy and efficiency of this procedure. In order to better present the colon information to the gastroenterologist performing a conventional (optical) colonoscopy, we undistort the radial distortion of the fisheye view of the colonoscope. The radial distortion is modeled with a function that converts the fisheye view to the perspective view, where the shape and size of polyps can be more readily observed. The conversion, accelerated on the graphics processing unit and running in real-time, calculates the corresponding position in the fisheye view of each pixel on the perspective image. We also merge our previous work in computer-aided polyp detection for virtual colonoscopy into the optical colonoscopy environment. The physical colonoscope path in the optical colonoscopy is approximated with the hugging corner shortest path, which is correlated with the centerline in the virtual colonoscopy. With the estimated distance that the colonoscope has been inserted, we are able to provide the gastroenterologist with visual cues along the observation path as to the location of possible polyps found by the detection process. In order to present the information to the gastroenterologist in a non-intrusive manner, we have developed a friendly user interface to enhance the optical colonoscopy without being cumbersome, distracting, or resulting in a more lackadaisical inspection by the gastroenterologist.

In this study, the relationship between hemodynamics patterns and aneurysmal rupture was investigated in cerebral aneurysms of a single morphological type (terminal aneurysms) regardless of their location. Hemodynamics information (intra-aneurysmal velocity and pressure fields and wall shear stress distributions) was derived from image-based computational fluid dynamics models with realistic patient specific anatomies. A total of 41 patient-specific models constructed from 3D rotational angiography images were analyzed. The results suggest that high wall shear stress may be associated with aneurysm rupture and that in turn different flow splitting patterns from the parent artery to the daughter branches and the aneurysm produce different levels of wall shear stress.

Image-based modeling of cardiovascular biomechanics may be very helpful for patients with aortic aneurysms to predict the risk of rupture and evaluate the necessity of a surgical intervention. In order to generate a reliable support it is necessary to develop exact patient-specific models that simulate biomechanical parameters and provide individual structural analysis of the state of fatigue and characterize this to the potential of rupture of the aortic wall. The patient-specific geometry used here originates from a CT scan of an Abdominal Aortic Aneurysm (AAA). The computations are based on the Finite Element Method (FEM) and simulate the wall stress distribution and the vessel deformation. The wall transient boundary conditions are based on real time-dependent pressure simulations obtained from a previous computational fluid dynamics study. The physiological wall material properties consider a nonlinear hyperelastic constitutive model, based on realistic ex-vivo analysis of the aneurismal arterial tissue. The results showed complex deformation and stress distribution on the AAA wall. The maximum stresses occurred at the systole and are found around the aneurismal bulge in regions close to inflection points. Biomechanical modeling based on medical images and coupled with patient-specific hemodynamics allows analysing and quantifying the effects of dilatation of the arterial wall due to the pulsatile aortic pressure. It provides a physical and realistic insight into the wall mechanics and enables predictive simulations of AAA growth and assessment of rupture. Further development integrating endovascular models would help evaluating non-invasively individual treatment strategies for optimal placement and improved device design.

The parts of the human body affected by a disease do not only undergo structural changes but also demonstrate significant physiological (functional) abnormalities. An important parameter that reveals the functional state of tissue is the flow of blood per unit tissue volume or perfusion, which can be obtained using dynamic imaging methods. One mathematical approach widely used for estimating perfusion from dynamic imaging data is based on a convolutional tissue-flow model. In these approaches, deconvolution of the observed data is necessary to obtain the important physiological parameters within a voxel. Although several alternatives have been proposed for deconvolution, all of them treat neighboring voxels independently and do not exploit the spatial correlation between voxels or the temporal correlation within a voxel over time. These simplistic approaches result in a noisy perfusion map with poorly defined region boundaries. In this paper, we propose a novel perfusion estimation method which incorporates spatial as well as temporal correlation into the deconvolution process. Performance of our method is compared to standard methods using independent voxel processing. Both simulated and real data experiments illustrate the potential of our method.

Respiratory dynamics poses a main source of error in radiotherapy of thoracic tumors. Development and optimization of methods to adequately account for breathing motion require detailed knowledge of the dynamics and its impact on e. g. the dose delivered by radiation. Thus, computer aided modeling and model based simulation of respiratory motion gains in importance. In this paper a biophysical approach for modeling lung motion is described. Main aspects of the process of lung ventilation are identified and outlined as the starting point of modeling. They are formulated as a contact problem of linear elasticity theory. The resulting boundary value problem is solved using Finite Element Methods (FEM). 4D (= 3D+t) CT image data are used to evaluate the modeling approach. Model based three-dimensional vector fields representing respiratory motion are computed for different patients. Simulated motion patterns of inner lung landmarks like prominent bifurcations of the bronchial tree and the tumor mass center are compared with corresponding motion patterns observed in the 4D CT data. The influence of geometrical and biomechanical parameters like mesh quality and values of elasticity constants on the modeling process is investigated. Differences between model based predicted landmark positions and corresponding landmark positions identified interactively are mostly within the variability of interactive landmark positioning across multiple observers (interobserver variability). The impact of geometrical and biomechanical parameters on resulting vector fields is fairly small. Outcomes suggest that FEM state an adequate strategy to model aspects of the physiology of breathing.

Truncated singular value decomposition and Tikhonov regularization are the most popular methods in perfusion model deconvolution. The first method uses a global truncation threshold and the second method uses a global regularization matrix. In this research a pixel-specific Tikhonov-based regularization, as well as a novel optimization based approach for bolus delay correction are suggested. The two techniques can be integrated into an iterative process. The new approach implements prior information to improve the regularization of the residue function and reduce the underestimation of flow rate. To compare its accuracy with current methods, deconvolution and delay correction for different perfusion related parameters and noise levels were simulated. Based on the simulation results, the new method showed more accuracy in preserving the structural shape of the residue function and estimating the perfusion-related parameters, especially in low SNRs.

In this study, we compare the results obtained from Time-Density Curve (TDC) analysis of angiographic imaging sequences with histological evaluation for a rabbit aneurysm model treated with standard stents and new asymmetric vascular stents (AVS) placed by image-guided endovascular deployment. AVSs are stents having a low-porosity patch region designed to cover the aneurysm neck and occlude blood flow inside. To evaluate the AVSs, rabbits with elastase-induced aneurysm models (n=20) were divided into three groups: the first (n=10) was treated with an AVS, the second (n=5) with a non-patch standard coronary stent, and third was untreated as a control (n=5). We used TDC analysis to measure how much contrast media entered the aneurysm before and after treatment. TDCs track contrast-media-density changes as a function of time over the region of interest in x-ray DSA cine-sequences. After 28 days, the animals were sacrificed and the explanted specimens were histologically evaluated. The first group showed an average reduction of contrast flow into the aneurysm of 95% after treatment with an AVS with fully developed thrombus at 28 days follow-up. The rabbits treated with standard stents showed an increase in TDC residency time after treatment and partial-thrombogenesis. The untreated control aneurysms displayed no reduction in flow and were still patent at follow-up. The quantitative TDC analysis findings were confirmed by histological evaluation suggesting that the new AVS has great potential as a definitive treatment for cerebro-vascular aneurysms and that angiographic TDC analysis can provide in-vivo verification.

The purpose of this study is to clarify embolic effects of embolic agents for partial splenic embolization. Partial splenic embolization is a minimally invasive technique for splenomegaly. However, embolic agents have been empirically chosen because embolic effects have never been studied quantitatively. We have constructed a quantitative 3-D analysis system of microvascular architecture. The system has consisted of data acquisition, segmentation, and measurement of diameters of end arterioles. 3-D volumetric data of samples with ultra-high resolution was acquired using a synchrotron radiation CT constructed in SPring-8. Segmented microvascular architecture was obtained applying an adaptive region growing method. This method is a kind of dynamic thresholding to cope with nonuniformity of the voxel intensity. To recognize end of arterioles, distance map from initial point placed at the root of the major trunk have been generated applying single-seeded coding. Diameters of vasculature are measured using single-seeded clusters which are formed from the same single-seeded code in the distance map and Euclidean distance transform which measures the minimum distances between each voxel and vascular boundary. Diameters of end arterioles are obtained choosing the maximum value in the result of Euclidian distance transform in the most distant cluster. In this study, we found diameters of embolized end arterioles were ranging from 48 to 72 micrometers with the analysis system. We have concluded that a quantitative 3-D analysis system have been successfully developed for microvascular architecture. A new approach to establish theoretical basis of embolization therapy with microspheres have been provide owing to the system.

Endovascular aortic replacement (EVAR) is an established technique, which uses stentgrafts to treat aortic aneurysms in patients at risk of aneurysm rupture. The long-term durability of a stentgraft is affected by the stresses and hemodynamic forces applied to it, and may be reflected by the movements of the stentgraft itself during the cardiac cycle. A conventional CT scan (which results in a 3D volume) is not able to visualize these movements. However, applying ECG-gating does provide insight in the motion of the stentgraft caused by hemodynamic forces at different phases of the cardiac cycle. The amount of data obtained is a factor of ten larger compared to conventional CT, but the radiation dose is kept similar for patient safety. This causes the data to be noisy, and streak artifacts are more common. Algorithms for automatic stentgraft detection must be able to cope with this. Segmentation of the stentgraft is performed by examining slices perpendicular to the centreline. Regions with high CT-values exist at the locations where the metallic frame penetrates the slice. These regions are well suited for detection and sub-pixel localization. Spurious points can be removed by means of a clustering algorithm, leaving only points on the contour of the stent. We compare the performance of several different point detection methods and clustering algorithms. The position of the stent's centreline is calculated by fitting a circle through these points. The proposed method can detect several stentgraft types, and is robust against noise and streak artifacts.

Mathematical difficulty affects approximately 5-9% of the population. Studies on individuals with dyscalculia, a neurologically based math disorder, provide important insight into the neural correlates of mathematical ability. For example, cognitive theories, neuropsychological studies, and functional neuroimaging studies in individuals with dyscalculia suggest that the bilateral parietal lobes and intraparietal sulcus are central to mathematical performance. The purpose of the present study was to investigate morphological differences in a group of third grade children with poor math skills. We compare population averages of children with low math skill (MD) to gender and age matched controls with average math ability. Anatomical data were gathered with high resolution MRI and four different population averaging methods were used to study the effect of the normalization technique on the results. Statistical results based on the deformation fields between the two groups show anatomical differences in the bilateral parietal lobes, right frontal lobe, and left occipital/parietal lobe.

We developed, tested, and evaluated a 3D segmentation and analysis system for in vivo MRI examinations of the human vocal tract during phonation. For this purpose, six professionally trained speakers, age 22-34y, were examined using a standardized MRI protocol (1.5 T, T1w FLASH, ST 4mm, 23 slices, acq. time 21s). The volunteers performed a prolonged (≥21s) emission of sounds of the German phonemic inventory. Simultaneous audio tape recording was obtained to control correct utterance. Scans were made in axial, coronal, and sagittal planes each. Computer-aided quantitative 3D evaluation included (i) automated registration of the phoneme-specific data acquired in different slice orientations, (ii) semi-automated segmentation of oropharyngeal structures, (iii) computation of a curvilinear vocal tract midline in 3D by nonlinear PCA, (iv) computation of cross-sectional areas of the vocal tract perpendicular to this midline. For the vowels /a/,/e/,/i/,/o/,/ø/,/u/,/y/, the extracted area functions were used to synthesize phoneme sounds based on an articulatory-acoustic model. For quantitative analysis, recorded and synthesized phonemes were compared, where area functions extracted from 2D midsagittal slices were used as a reference. All vowels could be identified correctly based on the synthesized phoneme sounds. The comparison between synthesized and recorded vowel phonemes revealed that the quality of phoneme sound synthesis was improved for phonemes /a/ and /y/, if 3D instead of 2D data were used, as measured by the average relative frequency shift between recorded and synthesized vowel formants (p<0.05, one-sided Wilcoxon rank sum test). In summary, the combination of fast MRI followed by subsequent 3D segmentation and analysis is a novel approach to examine human phonation in vivo. It unveils functional anatomical findings that may be essential for realistic modelling of the human vocal tract during speech production.

Virtual cystoscopy (VC) is a developing noninvasive, safe, and low-cost technique for bladder cancer screening. Multispectral (T₁- and T₂-weighted) magnetic resonance (MR) images provide a better tissue contrast between bladder wall and bladder lumen comparing with computed tomography (CT) images. The intrinsic T₁ and T₂ contrast of the urine against the bladder wall eliminates the invasive air insufflation procedure which is often used in CT-based VC. We propose a new partial volume (PV) segmentation scheme with inhomogeneity correction to segment multispectral MR images for tumor screening by virtual cystoscopy. The proposed PV segmentation algorithm automatically estimates the bias field and segments tissue mixtures inside each voxel of MR images, thus preserving texture information. Experimental results indicate that the present scheme is promising towards mass screening by virtual cystoscopy means.

Research has suggested that athletes involved in high-intensity sports for sustained periods have a higher probability of experiencing prolonged second stage of labour compared to non-athletes. The mechanism responsible for this complication is unknown but may depend on the relative size or tone of the pelvic floor muscles. Prolonged training can result in enlargement and stiffening of these muscles, providing increased resistance as the fetal head descends through the birth canal during a vaginal birth. On the other hand, recent studies have suggested an association between increased muscle bulk in athletes and higher distensibility. This project aims to use mathematical modelling to study the relationship between the size and tone of the pelvic floor muscles and the level of difficulty during childbirth. We obtained sets of magnetic resonance (MR) images of the pelvic floor region for a female athlete and a female non-athlete. Thirteen components of the pelvic floor were segmented and used to generate finite element (FE) models. The fetal head data was obtained by laser scanning a skull replica and a FE model was fitted to these data. We used contact mechanics to simulate the motion of the fetal head moving through the pelvic floor, constructed from the non-athlete data. A maximum stretch ratio of 3.2 was induced in the muscle at the left lateral attachment point to the pubis. We plan to further improve our modelling framework to include active muscle contraction and fetal head rotations in order to address the hypotheses that there is a correlation between the level of difficulty and the size or tone of the pelvic floor muscles.

Quantitative and spatially accurate maps of local NMR relaxation rates from single-shot acquisitions are of value for functional MRI and dynamic contrast studies. Addressing this need is SS-PARSE (Single-shot parameter assessment by recovery from signal encoding), a recently introduced MRI technique for mapping magnetization magnitude and phase, frequency, and net transverse decay rate R₂* from a single-shot (<70 msec) signal. Instead of implicitly modeling the local signal as arising from a constant magnetization vector, SS-PARSE models the evolution in phase and the decay in amplitude of the local signal and estimates the local parameter maps producing the observed signal. Because the local signal model used is fundamentally more accurate than the model implicitly used in most current MRI methodology, SS-PARSE maps are inherently free from geometric errors due to off-resonance frequencies. The accuracy of the parameter estimates is determined by (a) the information available in the signal (the form of the local signal model, the sampling pattern, and random noise), and by (b) the effectiveness of the estimation algorithm in extracting the information present in the signal. Sources of bias and random errors are discussed. The performance of the method is investigated using experimental phantom data.

Current methods of magnetic particle imaging generate a signal by cyclically saturating nanoparticles creating measurable harmonics in the induced magnetization. The sensitivity promises to be competitive with SPECT so molecular imaging is possible. The signal was localized by saturating the nanoparticles outside a voxel using a strong static magnetic field and sweeping the voxel across the sample to form an image. However, in applications where enough nanoparticles are present, signal can be detected at several higher harmonic frequencies and we show that the distribution of signal between those frequencies contributes localizing information. We tested one-dimensional implementations but the methods can be generalized to three dimensions. Spatial encoding was accomplished by using multiple drive frequencies that varied spatially. Two drive coils tuned to different drive frequencies and mounted on the same axis were used to explore the method. The response was measured from a single sample of iron oxide nanoparticles at eight positions along that axis to estimate response function for the reconstruction. Then two identical samples were placed at pairs of locations to test the method. The sample positions were reconstructed from the measured spectrum of the signal generated. The number of independent parameters is limited but four independent parameters can be achieved with very good conditioning and eight independent parameters can be achieved with reasonable conditioning.

Fluid registration is widely used in medical imaging to track anatomical changes, to correct image distortions, and to integrate multi-modality data. Fluid mappings guarantee that the template image deforms smoothly into the target, without tearing or folding, even when large deformations are required for accurate matching. Here we implemented an intensity-based fluid registration algorithm, accelerated by using a filter designed by Bro-Nielsen and Gramkow. We validated the algorithm on 2D and 3D geometric phantoms using the mean square difference between the final registered image and target as a measure of the accuracy of the registration. In tests on phantom images with different levels of overlap, varying amounts of Gaussian noise, and different intensity gradients, the fluid method outperformed a more commonly used elastic registration method, both in terms of accuracy and in avoiding topological errors during deformation. We also studied the effect of varying the viscosity coefficients in the viscous fluid equation, to optimize registration accuracy. Finally, we applied the fluid registration algorithm to a dataset of 2D binary corpus callosum images and 3D volumetric brain MRIs from 14 healthy individuals to assess its accuracy and robustness.

Intracranial compliance (ICC) determines the ability of the intracranial space to accommodate increase in volume (e.g., brain swelling) without a large increase in intracranial pressure (ICP). Therefore, measurement of ICC is potentially important for diagnosis and guiding treatment of related neurological problems. Modeling based approach uses an assumed lumped-parameter model of the craniospinal system (CSS) (e.g., RCL circuit), with either the arterial or the net transcranial blood flow (arterial inflow minus venous outflow) as input and the cranio-spinal cerebrospinal fluid (CSF) flow as output. The phase difference between the output and input is then often used as a measure of ICC However, it is not clear whether there is a predetermined relationship between ICC and the phase difference between these waveforms. A different approach for estimation of ICC has been recently proposed. This approach estimates ICC from the ratio of the intracranial volume and pressure changes that occur naturally with each heartbeat. The current study evaluates the sensitivity of the phase-based and the direct approach to changes in ICC. An RLC circuit model of the cranio-spinal system is used to simulate the cranio-spinal CSF flow for 3 different ICC states using the transcranial blood flows measured by MRI phase contrast from healthy human subjects. The effect of the increase in the ICC on the magnitude and phase response is calculated from the system's transfer function. We observed that within the heart rate frequency range, changes in ICC predominantly affected the amplitude of CSF pulsation and less so the phases. The compliance is then obtained for the different ICC states using the direct approach. The measures of compliance calculated using the direct approach demonstrated the highest sensitivity for changes in ICC. This work explains why phase shift based measure of ICC is less sensitive than amplitude based measures such as the direct approach method.

Detailed morphological analysis of pulmonary structures and tissue, provided by modern CT scanners, is of utmost importance as in the case of oncological applications both for diagnosis, treatment, and follow-up. In this case, a patient may go through several tomographic studies throughout a period of time originating volumetric sets of image data that must be appropriately registered in order to track suspicious radiological findings. The structures or regions of interest may change their position or shape in CT exams acquired at different moments, due to postural, physiologic or pathologic changes, so, the exams should be registered before any follow-up information can be extracted. Postural mismatching throughout time is practically impossible to avoid being particularly evident when imaging is performed at the limiting spatial resolution. In this paper, we propose a method for intra-patient registration of pulmonary CT studies, to assist in the management of the oncological pathology. Our method takes advantage of prior segmentation work. In the first step, the pulmonary segmentation is performed where trachea and main bronchi are identified. Then, the registration method proceeds with a longitudinal alignment based on morphological features of the lungs, such as the position of the carina, the pulmonary areas, the centers of mass and the pulmonary trans-axial principal axis. The final step corresponds to the trans-axial registration of the corresponding pulmonary masked regions. This is accomplished by a pairwise sectional registration process driven by an iterative search of the affine transformation parameters leading to optimal similarity metrics. Results with several cases of intra-patient, intra-modality registration, up to 7 time points, show that this method provides accurate registration which is needed for quantitative tracking of lesions and the development of image fusion strategies that may effectively assist the follow-up process.

Pulmonary arterial hypertension is a known complication associated with sickle-cell disease; roughly 75% of sickle cell disease-afflicted patients have pulmonary arterial hypertension at the time of death. This prospective study investigates the potential of image analysis to act as a surrogate for presence and extent of disease, and whether the size change of the pulmonary arteries of sickle cell patients could be linked to sickle-cell associated pulmonary hypertension. Pulmonary CT-Angiography scans from sickle-cell patients were obtained and retrospectively analyzed. Randomly selected pulmonary CT-Angiography studies from patients without sickle-cell anemia were used as negative controls. First, images were smoothed using anisotropic diffusion. Then, a combination of fast marching and geodesic active contours level sets were employed to segment the pulmonary artery. An algorithm based on fast marching methods was used to compute the centerline of the segmented arteries. From the centerline, the diameters at the pulmonary trunk and first branch of the pulmonary arteries were measured automatically. Arterial diameters were normalized to the width of the thoracic cavity, patient weight and body surface. Results show that the pulmonary trunk and first right and left pulmonary arterial branches at the pulmonary trunk junction are significantly larger in diameter with increased blood flow in sickle-cell anemia patients as compared to controls (p values of 0.0278 for trunk and 0.0007 for branches). CT with image processing shows great potential as a surrogate indicator of pulmonary hemodynamics or response to therapy, which could be an important tool for drug discovery and noninvasive clinical surveillance.

Chronic airway disease causes structural changes in the lungs including peribronchial thickening and airway dilatation. Multi-detector computed tomography (CT) yields detailed near-isotropic images of the lungs, and thus the potential to obtain quantitative measurements of lumen diameter and airway wall thickness. Such measurements would allow standardized assessment, and physicians to diagnose and locate airway abnormalities, adapt treatment, and monitor progress over time. However, due to the sheer number of airways per patient, systematic analysis is infeasible in routine clinical practice without automation. We have developed an automated and real-time method based on active contours to estimate both airway lumen and wall dimensions; the method does not require manual contour initialization but only a starting point on the targeted airway. While the lumen contour segmentation is purely region-based, the estimation of the outer diameter considers the inner wall segmentation as well as local intensity variation, in order anticipate the presence of nearby arteries and exclude them. These properties make the method more robust than the Full-Width Half Maximum (FWHM) approach. Results are demonstrated on a phantom dataset with known dimensions and on a human dataset where the automated measurements are compared against two human operators. The average error on the phantom measurements was 0.10mm and 0.14mm for inner and outer diameters, showing sub-voxel accuracy. Similarly, the mean variation from the average manual measurement was 0.14mm and 0.18mm for inner and outer diameters respectively.

The goal of Lung Tissue Resource Consortium (LTRC) is to improve the management of diffuse lung diseases through a better understanding of the biology of Chronic Obstructive Pulmonary Disease (COPD) and fibrotic interstitial lung disease (ILD) including Idiopathic Pulmonary Fibrosis (IPF). Participants are subjected to a battery of tests including tissue biopsies, physiologic testing, clinical history reporting, and CT scanning of the chest. The LTRC is a repository from which investigators can request tissue specimens and test results as well as semi-quantitative radiology reports, pathology reports, and automated quantitative image analysis results from the CT scan data performed by the LTRC core laboratories. The LTRC Radiology Core Laboratory (RCL), in conjunction with the Biomedical Imaging Resource (BIR), has developed novel processing methods for comprehensive characterization of pulmonary processes on volumetric high-resolution CT scans to quantify how these diseases manifest in radiographic images. Specifically, the RCL has implemented a semi-automated method for segmenting the anatomical regions of the lungs and airways. In these anatomic regions, automated quantification of pathologic features of disease including emphysema volumes and tissue classification are performed using both threshold techniques and advanced texture measures to determine the extent and location of emphysema, ground glass opacities, "honeycombing" (HC) and "irregular linear" or "reticular" pulmonary infiltrates and normal lung. Wall thickness measurements of the trachea, and its branches to the 3^rd and limited 4^th order are also computed. The methods for processing, segmentation and quantification are described. The results are reviewed and verified by an expert radiologist following processing and stored in the public LTRC database for use by pulmonary researchers. To date, over 1200 CT scans have been processed by the RCL and the LTRC project is on target for recruitment of the 2200 patients with 1800 CT scans in the repository for the 5-year effort. Ongoing analysis of the results in the LTRC database by the LTRC participating institutions and outside investigators are underway to look at the clinical and physiological significance of the imaging features of these diseases and correlate these findings with quality of life and other important prognostic indicators of severity. In the future, the quantitative measures of disease may have greater utility by showing correlation with prognosis, disease severity and other physiological parameters. These imaging features may provide non-invasive alternative endpoints or surrogate markers to alleviate the need for tissue biopsy or provide an accurate means to monitor rate of disease progression or response to therapy.

The assessment of airway dimensions is important in understanding the pathophysiology of various lung diseases. A number of methods have been developed to measure airways on computed tomography, but no study has been done to validate the different CT scanning techniques, CT scanners, and reconstruction algorithms. In our study, we constructed an artificial "airway" and "lung" phantom using hollow plastic tubes and foam blocks. The phantom was CT scanned using axial or helical techniques, and the images were reconstructed using a very high spatial frequency algorithm, a high spatial frequency algorithm, or a low spatial frequency algorithm. Custom software was then used to analyze the "airways" and measure lumen area (Ai) and "airway" wall area (Aaw). WA% (WA% = 100 x Aaw / (Ai + Aaw)) was also calculated. The cross-sectional area of the lumen and wall of the plastic tubes were measured using an optical micrometer. CT measurements of airway dimensions were virtually identical, comparing axial and helical techniques, and comparing a single-slice CT scanner to a multi-slice CT scanner. Using the plastic tube measurements as a "gold standard", Ai was estimated better with the very high or high spatial frequency algorithm (4.1 and 7.4 % error) vs. low spatial frequency algorithm (10.4% error). Aaw was better estimated with the low or high special frequency algorithm (3.8% and 6.1%) vs. very high spatial frequency algorithm (12.9%), and WA% was better estimated with the high or low spatial frequency algorithm (3.5% and 5.1%) vs. very high spatial frequency algorithm (7.3%). Based on these results, we recommend the high spatial frequency algorithm for the CT measurement of airway dimensions.

We aim to define a biomechanically-guided region of interest inside the proximal femur for improving fracture risk prediction based on bone density measurements. The central hypothesis is that by identifying and focusing on the proximal femoral tissues strongly associated with hip fracture risk, we can provide a better densitometric evaluation of fracture risk compared to current evaluations based on anatomically defined regions of interest using DXA or CT. To achieve this, we have constructed a hip statistical atlas of quantitative computed tomography (QCT) images by applying rigid and non-rigid inter-subject image registration to transform hip QCT scans of 15 fractured patients and 15 controls into a common reference space, and performed voxel-by-voxel t-tests between the two groups to identify bone tissues that showed the strongest relevance to hip fracture. Based on identification of this fracture-relevant tissue volume, we have generated a biomechanically-guided region of interest (B-ROI). We have applied BMD measured from this new region of interest to discriminate the fractured patients and controls, and compared it to BMD measured in the total proximal femur. For the femur ROI approach, the BMD values of the fractured patients and the controls had an overlap of 60 mg/cm³, and only 1 out of 15 fractured patients had BMD below the overlap region; for the B-ROI approach, a much narrower BMD overlap region of 28 mg/cm³ was observed, and 11 out of 15 fractured patients had BMDs below the overlap region.

Independent of overall bone density, 3D trabecular bone (TB) architecture has been shown to play an important role in conferring strength to the skeleton. Advances in imaging technologies such as micro-computed tomography (CT) and micro-magnetic resonance (MR) now permit in vivo imaging of the 3D trabecular network in the distal extremities. However, various experimental factors preclude a straightforward analysis of the 3D trabecular structure on the basis of these in vivo images. For MRI, these factors include blurring due to patient motion, partial volume effects, and measurement noise. While a variety of techniques have been developed to deal with the problem of patient motion, the second and third issues are inherent limitations of the modality. To address these issues, we have developed a series of robust processing steps to be applied to a 3D MR image and leading to a 3D skeleton that accurately represents the trabecular bone structure. Here we describe the algorithm, provide illustrations of its use with both specimen and in vivo micro-MR images, and discuss the accuracy and quantify the relationship between the original bone structure and the resulting 3D skeleton volume.

Recent data have shown that predicting bone strength can be greatly improved by including microarchitectural parameters in the analysis. Moreover, bone ultrastructure has been implicated as an important contributor to bone strength. We therefore hypothesized that a better understanding of phenotypes linked to bone ultrastructure will provide new insight in the assessment of bone quality and its contribution to bone strength and fracture risk. Therefore, we first developed an experimental design to assess quantitatively ultrastructural murine bone tissue properties non-invasively in three dimensions by using synchrotron radiation-based (SR) computed tomography (CT) methods with resolutions on the order of one micrometer and below. New morphometric indices were introduced to quantify ultrastructural phenotypes of murine cortical bone assessed by our SR CT-based setup, namely the canal network and the osteocyte lacunar system. These ultrastructural phenotypes were then successfully studied in two genetically distinct mouse strains. Finally, we provided strong evidence for a significant influence of the canal network on murine bone mechanics. In the long run, we believe that the morphometric analysis of the ultrastructural phenotypes and the study of bone phenotypes at different hierarchy levels, in conjunction with bone mechanics, will provide new insights in the assessment of bone quality.

The objective was to develop a method for high-resolution imaging of dynamic solute transport in cyclically deforming porous scaffolds for tissue engineering applications. A flexible cubic scaffold with single cylindrical channel was fabricated from a biodegradable polymer blend using a combined 3D printing and injection molding technique. The scaffold was attached to the bottom of a fluid reservoir mounted underneath a compression apparatus placed inside the X-ray scanner. The scaffold was positioned with the channel axis perpendicular to the X-ray beam. The container was filled with glycerin, and a solution of the contrast agent sodium iodide (NaI) in glycerin was injected into the scaffold channel. Intervals of compression cycles (14.5 ± 2.1 % compression at 1.0 Hz) were applied to the top face of the scaffold. After each interval the compression was temporarily paused to obtain a two-dimensional image at 20 μm pixel resolution. A series of images was also obtained without application of the compression cycles to quantify the effect of passive diffusional removal of NaI from the channel. The average NaI concentration in the channel decreased by 82% after 300 cycles (5 min.) of compression, by 40% after 60 min. of passive removal. Spatial profiles of the NaI concentration along the channel axis indicated that compression-induced transport preferentially removed the contrast agent at the pore openings. We conclude that convective transport induced by cyclic mechanical deformation of artificial tissue scaffolds could significantly contribute to the rate and depth of nutrient transport inside the scaffold, as compared to slow diffusive transport alone.

Dynamic contrast enhancement (DCE) is the leading technique in magnetic resonance imaging for cancer detection and diagnosis. However, there are large variations in the reported sensitivity and specificity of this method that result from the wide range of contrast-enhanced MRI sequences and protocols, image processing methods, and interpretation criteria. Analysis methods can be divided to physiological based models that take into account the vascular and tissue specific features that influence tracer perfusion, and to model free algorithms that decompose enhancement patterns in order to segment and classify different tissue types. Inhere we present a general hybrid method for analyzing dynamic contrast enhanced images integrating a mathematical, model-free technique with a model derived approach that characterizes tissue microvasculature function. We demonstrate the application of the method for breast cancer diagnosis. A brief description of this approach was recently presented for the diagnosis of prostate cancer. The model free method employed principal component analysis and yielded eigen-vectors of which two were relevant for characterizing breast malignancy. The physiological relevance of the two eigen-vectors was revealed by a quantitative correlation with the model based three time point technique. Projection maps of the eigen-vector that specifically related to the wash-out rate of the contrast agent depicted with high accuracy breast cancer. Overall, this hybrid method is fast, standardized, and yields parametric images characterizing tissue microvascular function. It can improve breast cancer detection and be potentially extended as a computer-aided tool for the detection and diagnosis of other cancers.

In breast elastography, breast tissues usually undergo large compressions resulting in significant geometric and structural changes, and consequently nonlinear mechanical behavior. In this study, an elastography technique is presented where parameters characterizing tissue nonlinear behavior is reconstructed. Such parameters can be used for tumor tissue classification. To model the nonlinear behavior, tissues are treated as hyperelastic materials. The proposed technique uses a constrained iterative inversion method to reconstruct the tissue hyperelastic parameters. The reconstruction technique uses a nonlinear finite element (FE) model for solving the forward problem. In this research, we applied Yeoh and Polynomial models to model the tissue hyperelasticity. To mimic the breast geometry, we used a computational phantom, which comprises of a hemisphere connected to a cylinder. This phantom consists of two types of soft tissue to mimic adipose and fibroglandular tissues and a tumor. Simulation results show the feasibility of the proposed method in reconstructing the hyperelastic parameters of the tumor tissue.

This study was undertaken to estimate metabolic tissue properties from dynamic breast F-18-FDG PET/CT image series and to display them as 3D parametric images. Each temporal PET series was obtained immediately after injection of 10 mCi of F-18-FDG and consisted of fifty 1- minute frames. Each consecutive frame was nonrigidly registered to the first frame using a finite element method (FEM) based model and fiducial skin markers. Nonlinear curve fitting of activity vs. time based on a realistic two-compartment model was performed for each voxel of the volume. Curve fitting was accomplished by application of the Levenburg-Marquardt algorithm (LMA) that minimized X². We evaluated which parameters are most suitable to determine the spatial extent and malignancy in suspicious lesions. In addition, Patlak modeling was applied to the data. A mixture model was constructed and provided a classification system for the breast tissue. It produced unbiased estimation of the spatial extent of the lesions. We conclude that nonrigid registration followed by voxel-by-voxel based nonlinear fitting to a realistic two-compartment model yields better quality parametric images, as compared to unprocessed dynamic breast PET time series. By comparison with the mixture model, we established that the total cumulated activity and maximum activity parametric images provide the best delineation of suspicious breast tissue lesions and hyperactive subregions within the lesion that cannot be discerned in unprocessed images.

The abnormal thermogram has been shown to be a reliable indicator of a high risk of breast cancer, but an open question is how to quantify the complex relationships between the breast thermal behaviors and the underlying physiological/pathological conditions. Previous thermal modeling techniques generally did not utilize the breast geometry determined by the gravity-induced elastic deformations arising from various body postures. In this paper, a 3-D finite-element method is developed for combined modeling of the thermal and elastic properties of the breast, including the mechanical nonlinearity associated with large deformations. The effects of the thermal and elastic properties of the breast tissues are investigated quantitatively. For the normal breast in a standing/sitting up posture, the gravity-induced deformation alone is found to be able to cause an asymmetric temperature distribution even though all the thermal/elastic properties are symmetrical, and this temperature asymmetry increases for softer and more compressible breast tissues. For a tumorous breast, we found that the surface-temperature alterations generally can be recognizable for superficial tumors at depths less than 20 mm. Tumor size plays a less important role than the tumor depth in determining the tumor-induced temperature difference. This result may imply that a higher thermal sensitivity is critical for a breast thermogram system when deeper tumors are present, even if the tumor is relatively large. We expect this new method to provide a stronger foundation for, and greater specificity and precision in, thermographic diagnosis and treatment of breast tumors.

We introduce a model-based approach for segmenting and quantifying GFP-tagged subcellular structures of the Golgi apparatus in 2D and 3D microscopy images. The approach is based on 2D and 3D intensity models, which are directly fitted to an image within 2D circular or 3D spherical regions-of-interest (ROIs). We also propose automatic approaches for the detection of candidates, for the initialization of the model parameters, and for adapting the size of the ROI used for model fitting. Based on the fitting results, we determine statistical information about the spatial distribution and the total amount of intensity (fluorescence) of the subcellular structures. We demonstrate the applicability of our new approach based on 2D and 3D microscopy images.

We present an image analysis approach as part of a high-throughput microscopy siRNA-based screening system using cell arrays for the identification of cellular genes involved in hepatitis C and dengue virus replication. Our approach comprises: cell nucleus segmentation, quantification of virus replication level in the neighborhood of segmented cell nuclei, localization of regions with transfected cells, cell classification by infection status, and quality assessment of an experiment and single images. In particular, we propose a novel approach for the localization of regions of transfected cells within cell array images, which combines model-based circle fitting and grid fitting. By this scheme we integrate information from single cell array images and knowledge from the complete cell arrays. The approach is fully automatic and has been successfully applied to a large number of cell array images from screening experiments. The experimental results show a good agreement with the expected behaviour of positive as well as negative controls and encourage the application to screens from further high-throughput experiments.

The purpose of this study is to test a new dynamic Perfusion-CT imaging protocol in an animal model and investigate the feasibility of quantifying perfusion of lung parenchyma to perform functional analysis from 4D CT image data. A novel perfusion-CT protocol was designed with 25 scanning time points: the first at baseline and 24 scans after a bolus injection of contrast material. Post-contrast CT scanning images were acquired with a high sampling rate before the first blood recirculation and then a relatively low sampling rate until 10 minutes after administrating contrast agent. Lower radiation techniques were used to keep the radiation dose to an acceptable level. 2 Yorkshire swine with pulmonary emboli underwent this perfusion- CT protocol at suspended end inspiration. The software tools were designed to measure the quantitative perfusion parameters (perfusion, permeability, relative blood volume, blood flow, wash-in & wash-out enhancement) of voxel or interesting area of lung. The perfusion values were calculated for further lung functional analysis and presented visually as contrast enhancement maps for the volume being examined. The results show increased CT temporal sampling rate provides the feasibility of quantifying lung function and evaluating the pulmonary emboli. Differences between areas with known perfusion defects and those without perfusion defects were observed. In conclusion, the techniques to calculate the lung perfusion on animal model have potential application in human lung functional analysis such as evaluation of functional effects of pulmonary embolism. With further study, these techniques might be applicable in human lung parenchyma characterization and possibly for lung nodule characterization.

The Case cryo-imaging system is a section and image system which allows one to acquire micron-scale, information rich, whole mouse color bright field and molecular fluorescence images of an entire mouse. Cryo-imaging is used in a variety of applications, including mouse and embryo anatomical phenotyping, drug delivery, imaging agents, metastastic cancer, stem cells, and very high resolution vascular imaging, among many. Cryo-imaging fills the gap between whole animal in vivo imaging and histology, allowing one to image a mouse along the continuum from the mouse -> organ -> tissue structure -> cell -> sub-cellular domains. In this overview, we describe the technology and a variety of exciting applications. Enhancements to the system now enable tiled acquisition of high resolution images to cover an entire mouse. High resolution fluorescence imaging, aided by a novel subtraction processing algorithm to remove sub-surface fluorescence, makes it possible to detect fluorescently-labeled single cells. Multi-modality experiments in Magnetic Resonance Imaging and Cryo-imaging of a whole mouse demonstrate superior resolution of cryo-images and efficiency of registration techniques. The 3D results demonstrate the novel true-color volume visualization tools we have developed and the inherent advantage of cryo-imaging in providing unlimited depth of field and spatial resolution. The recent results continue to demonstrate the value cryo-imaging provides in the field of small animal imaging research.

As a novel and rapidly growing optical molecular imaging technology, bioluminescence tomography (BLT) can localize and quantify an internal bioluminescent source with the bioluminescent signal on the external surface of a small animal to reveal non-invasive molecular and cellular activities directly. Adaptive finite element method (FEM) based on discretized elements has been introduced into BLT field recently, but the quickly increasing number of subdivided elements will reduce the source reconstruction efficiency greatly along with mesh refinement. In this contribution, a three-dimensional BLT reconstruction method based on nodes of adaptive FEM is developed for determining bioluminescent source distribution to solve the aforementioned problem, which can improve localization of source and enhance the efficiency of reconstruction. Furthermore, BLT is ill-posed for high scattering properties of the biological tissues and the limited boundary detection data. Thus, adequate a priori knowledge should be incorporated in this proposed algorithm to reduce the ill-posedness of BLT, such as optical parameters and anatomical structures information of the tissues. Finally, the performance of this reconstruction method is verified with the homogeneous and heterogeneous mouse chest phantoms and Monte Carlo (MC) simulation data. The results show the effectiveness and merits of this tomographic algorithm for BLT.

This paper presents a new, real-time, ghost correction method for echo planar imaging (EPI) that has been implemented using the Imaging Calculation Environment (ICE) on a 3T Siemens MRI System. Conventional methods for correcting EPI image ghost are based on image phase correction or on a reference scan. This new method is also based on image phase correction, but uses a new algorithm for automatic determination of the phase correction, which allows entirely automated operation. With implementation of the new correction method in ICE, ghost-corrected images are automatically generated and loaded into the system's image database immediately after completion of each EPI scan. Experiments showed that this real time ghost correction method consistently reduced the ghost intensity in EPI images and improved overall image quality. On average, the ghost to signal ratio (GSR) improved from 13.0% to 3.2% using the new method.