Confocal microscopy enables us to track myocytes in the embryonic zebrafish heart. The Zeiss LSM 5 Live high speed confocal microscope has been used to take optical sections (at 3 μm intervals and 151 frames per second) through a fluorescently labeled zebrafish heart at two developmental stages (26 and 34 hours post fertilization (hpf)). This data provides unique information allowing us to conjecture on the morphology and biomechanics of the developing vertebrate heart. Nevertheless, the myocytes, whose positions could be determined in a reliable manner, were located sparsely and mostly in one side of the heart tube. This difficulty was overcome using computational methods, that give longitudinal, radial and circumferential displacements of the myocytes as well as their contractile behavior. Applied strain analysis has shown that in the early embryonic heart tube, only the caudal region (near the in-flow) and another point in the middle of the tube can be active; the rest appears to be mostly passive. This statement is based on the delay between major strain and displacement which a material point experiences. Wave-like propagation of all three components of the displacement, especially in the circumferential direction, as well as the almost-periodic changes of the maximum strain support the hypothesis of helical muscle structure embedded in the tube. Changes of geometry in the embryonic heart after several hours are used to verify speculations about the structure based on the earlier images and aforementioned methods.

Obesity is a global epidemic and a comorbidity for many diseases. We are using MRI to characterize obesity in rodents, especially with regard to visceral fat. Rats were scanned on a 1.5T clinical scanner, and a T1W, water-spoiled image (fat only) was divided by a matched T1W image (fat + water) to yield a ratio image related to the lipid content in each voxel. The ratio eliminated coil sensitivity inhomogeneity and gave flat values across a fat pad, except for outlier voxels (> 1.0) due to motion. Following sacrifice, fat pad volumes were dissected and measured by displacement in canola oil. In our study of 6 lean (SHR), 6 dietary obese (SHR-DO), and 9 genetically obese rats (SHROB), significant differences in visceral fat volume was observed with an average of 29±16 ml increase due to diet and 84±44 ml increase due to genetics relative to lean control with a volume of 11±4 ml. Subcutaneous fat increased 14±8 ml due to diet and 198±105 ml due to genetics relative to the lean control with 7±3 ml. Visceral fat strongly correlated between MRI and dissection (R² = 0.94), but MRI detected over five times the subcutaneous fat found with error-prone dissection. Using a semi-automated images segmentation method on the ratio images, intra-subject variation was very low. Fat pad composition as estimated from ratio images consistently differentiated the strains with SHROB having a greater lipid concentration in adipose tissues. Future work will include in vivo studies of diet versus genetics, identification of new phenotypes, and corrective measures for obesity; technical efforts will focus on correction for motion and automation in quantification.

We believe that small-animal positron emission tomography (μPET) can play an important role in phenotyping and drug screening. For such applications, imaging throughput becomes an important issue because one needs to image a considerable number of subjects in a study. Toward enabling high-throughput μPET imaging, we are developing a prototype that consists of two large-area, high-performance flat detectors. These detectors are placed opposed to each other with a small spacing for providing large detection solid angle and detection sensitive volume. The resulting scanner geometry produces data having missing views and projection truncations, therefore posing a particular challenge in reconstruction. In this paper, we developed a new iterative reconstruction method that addresses this challenge. By using 2D simulated data, we find that this new method can accurately reconstruct an extended detection volume of the prototype. Because our prototype shares the same configuration with positron emission mammography (PEM), the new reconstruction method is also applicable for PEM reconstruction.

Technological advances in micro-CT scanners have introduced dynamic, flat-panel scanners, which allow the acquisition of volume images in a few seconds. However, motion artefacts associated with normal respiratory motion arise when imaging the thorax or abdomen. To reduce these artefacts and the accompanying loss of spatial resolution, and to enable the study of rodent respiratory function, we developed a retrospective respiratory gating technique for volume micro-CT imaging of free-breathing rodents. Anaesthetized male C57BL6 mice were placed in the prone position on a custom-made bed containing an embedded pressure chamber that was connected to a pressure transducer. Inhalation motion caused an increase in the chamber pressure, which was monitored as a surrogate for the respiratory waveform, and measured throughout the scan. Projection images of the mouse thorax were acquired using a GE Locus Ultra micro-CT scanner, at 80 kVp, 50 mA (entrance exposure of approximately 2.7 cGy per rotation), over ten rotations in less than 1 minute. Respiratory gating was performed retrospectively by selecting projections that were obtained during the same portion of the respiratory cycle prior to reconstruction; CT images reconstructed from three to ten rotations were evaluated. The nominal voxel spacing was 0.15 mm isotropic. Images were assessed for image noise, artefacts and measurement accuracy of physiologically relevant structures. These measurements showed no significant differences for images reconstructed from projection images from five to ten rotations. The optimum number of rotations for imaging mouse lungs was found to be six, corresponding to a 30 second (16.2 cGy) scan.

Micro-CT, a technique for imaging small objects at high resolution using micro focused x-rays, is becoming widely available for small animal imaging. With the growing number of mouse models of pulmonary pathology, there is great interest in following disease progression and evaluating the alteration in longitudinal studies. Along with the high resolution associated with micro CT comes increased scanning times, and hence minimization of motion artifacts is required. We propose a new technique for imaging mouse lungs in vivo by inducing an intermittent iso-pressure breath hold (IIBH) with a fixed level of positive airway pressure during image acquisition, to decrease motion artifacts and increase image resolution and quality. Mechanical ventilation of the respiratory system for such a setup consists of three phases, 1) tidal breathing (hyperventilated), 2) a breath hold during a fixed level of applied positive airway pressure, 3) periodic deep sighs. Image acquisition is triggered over the stable segment of the IIBH period. Comparison of images acquired from the same mouse lung using three imaging techniques (normal breathing / no gating, normal breathing with gating at End Inspiration (EI) and finally the IIBH technique) demonstrated substantial improvements in resolution and quality when using the IIBH gating. Using IIBH triggering the total image acquisition time increased from 15 minutes to 35 minutes, although total x-ray exposure time and hence animal dosage remains the same. This technique is an important step in providing high quality lung imaging of the mouse in vivo, and will provide a good foundation for future longitudinal studies.

Cardiac arrhythmias are a major cause of death (7 million cases annually worldwide; 400,000 in the U.S. alone) and disability. Yet, a noninvasive imaging modality to identify patients at risk, provide accurate diagnosis and guide therapy is not yet available in clinical practice. In my conference presentation and proceedings article, I will describe examples of the application of Electrocardiographic Imaging (ECGI) in humans. ECGI is a new noninvasive imaging modality for cardiac arrhythmias developed in our laboratory. It combines recordings of 224 body-surface electrocardiograms and a thoracic CT scan to reconstruct potentials, electrograms and isochrones (activation sequences) on the heart surface. Examples include: (1) normal activation and repolarization; (2) activation during ventricular pacing; and (3) atrial flutter.

Conventional analysis of cardiac ventricular magnetic resonance images is performed using short axis images and does not guarantee completeness and consistency of the ventricle coverage. In this paper, a four-dimensional (4D, 3D+time) left and right ventricle statistical shape model was generated from the combination of the long axis and short axis images. Iterative mutual intensity registration and interpolation were used to merge the long axis and short axis images into isotropic 4D images and simultaneously correct existing breathing artifact. Distance-based shape interpolation and approximation were used to generate complete ventricle shapes from the long axis and short axis manual segmentations. Landmarks were automatically generated and propagated to 4D data samples using rigid alignment, distance-based merging, and B-spline transform. Principal component analysis (PCA) was used in model creation and analysis. The two strongest modes of the shape model captured the most important shape feature of Tetralogy of Fallot (TOF) patients, right ventricle enlargement. Classification of cardiac images into classes of normal and TOF subjects performed on 3D and 4D models showed 100% classification correctness rates for both normal and TOF subjects using k-Nearest Neighbor (k=1 or 3) classifier and the two strongest shape modes.

The noninvasive assessment of coronary atherosclerosis holds great promise for the future of cardiovascular medicine, and multidetector computed tomography (MDCT) has recently taken the lead in this area. Earlier studies have shown the ability of MDCT to visualize the coronary lumen and various types of atherosclerotic plaque. The aims of this project are to design, implement, and validate a complete system for the automated, quantitative analysis of coronary MDCT images. The developed system uses graph algorithms and knowledge-based cost functions to automatically segment the lumen and wall, and then uses pattern classification techniques to identify and quantify the tissue types found within the detected vascular wall. The system has been validated in comparison with expert tracings and labels, as well as in comparison with intravascular ultrasound (IVUS). In the former, the radial position of the lumen and adventitia were compared at 360 corresponding angular locations in 299 vascular cross sections (from 13 vessels in 5 patients: 5 RCA, 4 LAD, 4 LCX). Results show a border positioning error of 0.150 ± 0.090 mm unsigned / 0.007 ± 0.001 mm signed for the lumen, and 0.210 ± 0.120 mm unsigned / 0.020 ± 0.030 mm signed for the vessel wall. In the comparison with IVUS, the luminal and vascular cross sectional areas were compared in 7 vessels; good correlation was shown for both the lumen (R=0.83) and the vessel wall (R=0.76). The plaque characterization algorithm correctly classified 92% of calcified plaques and 87% of non-calcified plaques.

The adventitia and outer media of large blood vessels are supplied with nutrients by microscopic blood vessels called vasa vasorum. While vasa vasorum have been implicated in a number of diseases including atherosclerosis, knowledge of their functional anatomy and specific role in these diseases has been hindered due to the small size of the vasa vasorum, and difficulty in accessing them. Micro-CT and histological methods have been used in ex-vivo animal studies of the vasa vasorum, but these techniques are limited by their inability to be used for in-vivo investigation. As such, there is very little in-vivo human data available. Intra-vascular ultrasound can acquire high-resolution anatomic images of coronary vessels. ChromaFlo IVUS has been used to identify blood flow in vessel lumens and has exciting prospect for in-vivo studies of vasa vasorum functional anatomy. In this study, ChromaFlo IVUS images of the human mid-left anterior descending coronary artery (LAD) were segmented to analyze the distribution of adventitial vasa vasorum proximal to intimal plaque. Previous animal studies suggest that formation of intimal plaque is accompanied by increased density of adventitial vasa vasorum. The data collected with ChromaFlo ultrasound is inconsistent with the current literature. While IVUS has the fidelity to acquire high-resolution US images of the coronary arteries, ChromaFlo lacks the necessary resolving power to differentiate the vasa vasorum. Further study of IVUS and other imaging methods on a large cohort will provide the basis for future in-vivo analysis of coronary disease.

Recent studies have demonstrated that atherosclerotic plaque surface morphology in the carotid arterial system represents an independent risk factor for embolus formation and subsequent cerebrovascular occlusive events. The primary aim of the current retrospective study is to enhance the clinical utility of this key finding by developing and evaluating objective, quantitative methods for characterizing plaque surface irregularity from Gadolinium-enhanced magnetic resonance angiography (MRA) studies. Nine metrics were analyzed for correlation with percent stenosis in 78 arteries from 43 patients with carotid artery disease. Most of the metrics comprised measurements obtained from a surface model of the stenotic lesion derived from the MRA via the Marching Cubes algorithm with application of the Isosurface Deformable Model. Percent stenosis was determined through real-time volume rendering of 3D MIP MRA studies in Vitrea2. Six of the analyzed metrics revealed significant correlation to percent stenosis (p<0.01). Reproducibility of all metrics was evaluated in a set of 14 randomly selected arteries from 13 patients by way of a single-trial, two-observer analysis. Six of the nine metrics demonstrated significant inter-observer reproducibility by way of single-factor ANOVA analysis (p<0.02). Collectively, the findings reported herein demonstrate an objective and reliable method for quantifying carotid plaque surface irregularity from standard MRA techniques with possible future clinical application in refining risk of ischemic cerebrovascular events and associated need for prophylactic intervention.

Atherosclerosis is characterized by the development of plaques in the arterial wall, which ultimately leads to heart attacks and stroke. 3D ultrasound (US) has been used to screen patients' carotid arteries. Plaque measurements obtained from these images may aid in the management and monitoring of patients, and in evaluating the effect of new treatment options. Different types of measures for ultrasound phenotypes of atherosclerosis have been proposed. Here, we report on the development and application of a method used to analyze changes in carotid plaque morphology from 3D US images obtained at two different time points. We evaluated our technique using manual segmentations of the wall and lumen of the carotid artery from images acquired in two US scanning sessions. To incorporate the effect of intraobserver variability in our evaluation, manual segmentation was performed five times each for the arterial wall and lumen. From this set of five segmentations, the mean wall and lumen surfaces were reconstructed, with the standard deviation at each point mapped onto the surfaces. A correspondence map between the mean wall and lumen surfaces was then established, and the thickness of the atherosclerotic plaque at each point in the vessel was estimated to be the distance between each correspondence pairs. The two-sample Student's t-test was used to judge whether the difference between the thickness values at each pair corresponding points of the arteries in the two 3D US images was statistically significant.

In the past, several methods based on iterative solution of pressure-Poisson equation have been developed for measurement of pressure from phase-contrast magnetic resonance (PC-MR) data. We have developed a novel non-iterative harmonics-based orthogonal projection method which can keep the pressures measured based on the Navier-Stokes equation independent of the path of integration. The gradient of pressure calculated with Navier-Stokes equation is expanded with a series of orthogonal basis functions, and is subsequently projected onto an integrable subspace. Before the projection step however, a scheme is devised to eliminate the discontinuity at the vessel boundaries. The approach was applied to noise-added velocities obtained for both steady and pulsatile stenotic flows from computational fluid dynamics (CFD) simulations and compared with pressures independently obtained by CFD. Additionally, MR velocity data for steady flows measured in in-vitro phantom models with different degree of stenoses and different flow rates were used to test the algorithm and results were compared with CFD simulations.

Finite element method based patient-specific wall stress in abdominal aortic aneurysm (AAA) may provide a more accurate rupture risk predictor than the currently used maximum transverse diameter. In this study, we have investigated the sensitivity of the wall stress in AAA with respect to geometrical variations. We have acquired MR and CT images for four patients with AAA. Three individual users have delineated the AAA vessel wall contours on the image slices. These contours were used to generate synthetic feature images for a deformable model based segmentation method. We investigated the reproducibility and the influence of the user variability on the wall stress. For sufficiently smooth models of the AAA wall, the peak wall stress is reproducible for three out of the four AAA geometries. The 0.99 percentiles of the wall stress show excellent reproducibility for all four AAAs. The variations induced by user variability are larger than the errors caused by the segmentation variability. The influence of the user variability appears to be similar for MR and CT. We conclude that the peak wall stress in AAA is sensitive to small geometrical variations. To increase reproducibility it appears to be best not to allow too much geometrical detail in the simulations. This could be achieved either by using a sufficiently smooth geometry representation or by using a more robust statistical parameter derived from the wall stress distribution.

This study shows the influence of the upstream parent artery geometry on intra-aneurysmal hemodynamics of cerebral aneurysms. Patient-specific models of four cerebral aneurysms at four typical locations were constructed from 3D rotational angiography images. Two geometrical models were constructed for each patient, one with the native parent vessel geometry and another with the parent vessel truncated approximately 1cm upstream from the aneurysm. For one aneurysm, two images were used to construct a model as realistic and large as possible - down to the carotid bifurcation - which was cut at seven different locations. Corresponding finite element grids were generated and computational fluid dynamics simulations were carried out under pulsatile flow conditions. It was found that truncated models tended to underestimate the wall shear stress in the aneurysm and to shift the impaction zone to the neck when compared with the native geometry. In one aneurysm the parent vessel included a tortuous segment close to the neck that strongly influenced the flow pattern entering the aneurysm. Thus, including longer portions of the parent vessel beyond this segment did not have a substantial effect. Depending on the dominant geometrical features the length of the parent artery needed for an accurate representation of the intraaneurysmal hemodynamics may vary among individuals. In conclusion, failure to properly model the inflow stream determined by the upstream parent artery can significantly influence the results of intra-aneurysmal hemodynamic models. The upstream portion of the parent vessel of cerebral aneurysms should be included in order to accurately represent the intraaneurysmal hemodynamics.

One of the factors affecting the accuracy of patient-specific, imaging-based computational hemodynamic studies is the accuracy of geometric models created from medical images. In the present study we have investigated as to how accurate the geometric models should be in the context of cerebral aneurysms in order to obtain an accurate reproduction of intra-aneurysmal hemodynamics in individual patients using numerical simulations. Computed tomography angiography (CTA) images obtained for a patient-specific anterior communicating artery (ACoA) aneurysm and a patient-specific middle cerebral artery (MCA) aneurysm were used to construct the geometric models. For each aneurysm, two models were created, one using a different threshold value for image segmentation than the other. The average distance between the models was about the size of one in-plane pixel. It was found that for the MCA aneurysm, the simulated pressure and shear stress distributions for the two models were entirely different while for the ACoA aneurysm the mean pressure distribution obtained for the two models were similar, but the shear stress distributions were completely different. These results indicate that accurate reproduction of intra-aneurysmal hemodynamics would require the geometric reconstruction from medical images to be highly accurate.

Conventional evaluation of the significance of vascular disease has focused on estimates of geometric factors. There is now substantial interest in investigating whether the onset and progression of vascular pathology can be related to hemodynamic factors. Current imaging modalities have excellent capabilities in delineating the geometric boundaries of the vascular lumen. Advanced non-invasive imaging modalities such as Multi Detector CT and MRI are also able to define the extent of disease within the vessel wall and to provide information on the composition of thrombotic and atherosclerotic components. Finally, it is also possible to use imaging techniques to measure flow velocities across the lumen of vessels of interest, and to determine the pulsatile variation of these velocities through the cardiac cycle. Despite these advanced capabilities, imaging alone is unable to determine important features of the vascular hemodynamics such as wall shear stress or pressure distributions. However, the information on lumenal geometry and the inlet and outlet flow conditions can be used as input into numerical simulation models that are able to predict those quantities. These Computational Fluid Dynamics models can be used to predict hemodynamic parameters on a patient-specific basis. It is therefore possible to use non-invasive imaging methods to follow the progression of vascular disease over time, and to relate changes in lumenal and wall structure to calculated hemodynamic descriptors. This approach can be used not only to understand the natural progression of vascular disease, but as a tool to predict the likely outcome of a surgical intervention.

The accuracy and reproducibility of hemodynamic simulation for a brain aneurysm system was determined by comparison of physical measurements made in a curved duct with the corresponding simulations produced by three different solvers, and by inter-solver comparison of blood flow in a patient-specific, imaging-based model of an aneurysm. The simulations were in close agreement with measurements made in the square duct. This suggests that hemodynamic simulation is accurate for models with strong curvature flow. The simulation results produced by solvers using the model of the brain aneurysm were consistent with each other, suggesting that hemodynamic simulations of patient-specific imaging-based aneurysm models are consistent and reproducible by different solvers. These results support the validity of patient-specific imaging-based simulations.

In this study, the effects of unequal physiologic flow conditions in the internal carotid arteries on the intra-aneurysmal hemodynamics of anterior communicating artery aneurysms were investigated. Patient-specific vascular computational fluid dynamics models of five cerebral aneurysms were constructed from bilateral 3D rotational angiography images. The aneurysmal hemodynamics was analyzed under a range of physiologic flow conditions including the effects of unequal mean flows and phase shifts between the flow waveforms of the left and right internal carotid arteries. A total of five simulations were performed for each patient, and unsteady wall shear stress (WSS) maps were created for each flow condition. Time dependent curves of average WSS magnitude over selected regions on the aneurysms were constructed and used to analyze the influence of the inflow conditions. It was found that mean flow imbalances in the feeding vessels tend to shift the regions of elevated WSS (flow impingement region) towards the dominating inflow jet and to change the magnitude of the WSS peaks. However, the overall qualitative appearance of the WSS distribution and velocity simulations is not substantially affected. In contrast, phase differences tend to increase the temporal complexity of the hemodynamic patterns and to destabilize the intra-aneurysmal flow pattern. However, these effects are less important when the A1 confluence is less symmetric, i.e. dominated by one of the A1 segments. Conditions affecting the flow characteristics in the parent arteries of cerebral aneurysms with more than one avenue of inflow should be incorporated into flow models.

An asymmetric stent with low porosity patch across the intracranial aneurysm neck and high porosity elsewhere is designed to modify the flow to result in thrombogenesis and occlusion of the aneurysm and yet to reduce the possibility of also occluding adjacent perforator vessels. The purposes of this study are to evaluate the flow field induced by an asymmetric stent using both numerical and digital subtraction angiography (DSA) methods and to quantify the flow dynamics of an asymmetric stent in an in vivo aneurysm model. We created a vein-pouch aneurysm model on the canine carotid artery. An asymmetric stent was implanted at the aneurysm, with 25% porosity across the aneurysm neck and 80% porosity elsewhere. The aneurysm geometry, before and after stent implantation, was acquired using cone beam CT and reconstructed for computational fluid dynamics (CFD) analysis. Both steady-state and pulsatile flow conditions using the measured waveforms from the aneurysm model were studied. To reduce computational costs, we modeled the asymmetric stent effect by specifying a pressure drop over the layer across the aneurysm orifice where the low porosity patch was located. From the CFD results, we found the asymmetric stent reduced the inflow into the aneurysm by 51%, and appeared to create a stasis-like environment which favors thrombus formation. The DSA sequences also showed substantial flow reduction into the aneurysm. Asymmetric stents may be a viable image guided intervention for treating intracranial aneurysms with desired flow modification features.

Modeling aneurysm growth using stress-mediated growth laws requires accurate knowledge of the hemodynamic stresses and strains acting on the aneurysm wall due to the internal blood flow and the external tissue support. Therefore, solving the coupled problem of blood flow and vessel wall deformation represents a critical step in the evaluation of these hemodynamic stresses, but for large, patient-specific models of the vasculature one that is computationally expensive. In this work, we present the application of a new formulation, the Coupled Momentum Method for Fluid-Solid Interaction (CMM-FSI), to compute blood flow and vessel wall deformation under realistic ranges of pressures for large patient-specific models of the cerebro-vasculature. The method couples the equations of the deformation of the vessel wall at the variational level as a boundary condition for the fluid domain. We consider a strong coupling of the degrees-of-freedom of the fluid interface and the wall domains. The effect of the vessel wall boundary is therefore added in a monolithic way to the fluid equations, resulting in a remarkably robust and computationally-efficient scheme. The method is applied to patient-specific model of the Circle of Willis featuring a saccular aneurysm, using resistance outflow boundary conditions. The wall normal and shear stresses resulting from the simulation can then be used as the hemodynamic forces mediating the aneurysm wall adaptation in the algorithm shown in the second part of this work.

Computational Fluid Dynamics (CFD) techniques were used to assess the dynamic pressure distribution at the sites of three virtually removed paraclinoid aneurysms. 3D Digital Subtraction Angiograms (3D-DSA) were used to create two computational meshes for each aneurysm. The first mesh was derived directly from the angiographic data containing the aneurysms. In the second mesh, the section of the artery across the aneurysm neck was virtually reconstructed by interpolation between the adjacent proximal and distal parts of the parent artery. This mesh is considered to be an approximation to the geometry of the parent artery before aneurysm formation. Results from the simulations showed that the peak dynamic pressures occurred at arterial bends. The change in the magnitude of dynamic pressure over the cycle was about 7.5 mmHg which is about 15% of the change in the background static pressure. However, further study is needed to clarify if this change in dynamic pressure would elicit any biochemical response from the vessel tissue.

In this study we utilize our texture characterization software (3-D AMFM) to characterize interstitial lung diseases (including emphysema) based on MDCT generated volumetric data using 3-dimensional texture features. We have sought to test whether the scanner and reconstruction filter (kernel) type affect the classification of lung diseases using the 3-D AMFM. We collected MDCT images in three subject groups: emphysema (n=9), interstitial pulmonary fibrosis (IPF) (n=10), and normal non-smokers (n=9). In each group, images were scanned either on a Siemens Sensation 16 or 64-slice scanner, (B50f or B30 recon. kernel) or a Philips 4-slice scanner (B recon. kernel). A total of 1516 volumes of interest (VOIs; 21x21 pixels in plane) were marked by two chest imaging experts using the Iowa Pulmonary Analysis Software Suite (PASS). We calculated 24 volumetric features. Bayesian methods were used for classification. Images from different scanners/kernels were combined in all possible combinations to test how robust the tissue classification was relative to the differences in image characteristics. We used 10-fold cross validation for testing the result. Sensitivity, specificity and accuracy were calculated. One-way Analysis of Variances (ANOVA) was used to compare the classification result between the various combinations of scanner and reconstruction kernel types. This study yielded a sensitivity of 94%, 91%, 97%, and 93% for emphysema, ground-glass, honeycombing, and normal non-smoker patterns respectively using a mixture of all three subject groups. The specificity for these characterizations was 97%, 99%, 99%, and 98%, respectively. The F test result of ANOVA shows there is no significant difference (p <0.05) between different combinations of data with respect to scanner and convolution kernel type. Since different MDCT and reconstruction kernel types did not show significant differences in regards to the classification result, this study suggests that the 3-D AMFM can be generally introduced.

Tree matching methods have numerous applications in medical imaging, including registration, anatomical labeling, segmentation, and navigation of structures such as vessels and airway trees. Typical methods for tree matching rely on conventional graph matching techniques and therefore suffer potential limitations such as sensitivity to the accuracy of the extracted tree structures, as well as dependence on the initial alignment. We present a novel path-based tree matching framework independent of graph matching. It is based on a point-by-point feature comparison of complete paths rather than branch points, and consequently is relatively unaffected by spurious airways and/or missing branches. A matching matrix is used to enforce one-to-one matching. Moreover our method can reliably match irregular tree structures, resulting from imperfect segmentation and centerline extraction. Also reflecting the nature of these features, our method does not require a precise alignment or registration of tree structures. To test our method we used two thoracic CT scans from each of ten patients, with a median inter-scan interval of 3 months (range 0.5 to 10 months). The bronchial tree structure was automatically extracted from each scan and a ground truth of matching paths was established between each pair of tree structures. Overall 87% of 702 airway paths (average 35.1 per patient matched both ways) were correctly matched using this technique. Based on this success we also present preliminary results of airway-to-artery matching using our proposed methodology.

When addressing the task of automatic tracheo-bronchial tree matching it seems natural to make use of the tree's graph structure and branching characteristics. In tracheo-bronchial trees that were automatically extracted from multi-slice CT data, however, the graph information is not always reliable, especially for noisy or low-dose data which makes the abovementioned class of approaches prone to error in these situations. In this work we investigate what can be gained by using the spatial position of the bronchial centerline points. For this purpose we introduce, investigate, and compare two approaches to tree matching that are based on the use of centerline point positions alone with no additional connectivity information. As features we use (1) the 3D shape context and (2) statistical moments of the local point distribution. The 3D shape context has recently been introduced as a regional shape descriptor. It is based on a spherical histogram with logarithmic sampling in the radial direction. Both methods are used in order to match an automatically extracted tree to a manually labeled model tree which results in an automatic anatomical labeling of the data tree. Six tracheo-bronchial trees were matched to a given model tree. The data trees covered a range from high quality data to poor quality data. Furthermore two of the cases exhibited strongly distorted anatomy. It could be shown that the 3D shape context feature labeled 69 % of the branches correctly with one of 34 anatomical labels. In the case of the statistical moment feature 40 % of the branches were labeled correctly. We conclude that the set of centerline points alone allows correct labeling of a large portion of lung segments. We propose to combine the valuable local shape information in future work with connectivity and branching information, where the latter is reliably available.

Respiration causes movement and shape changes in thoracic tumors, which has a direct influence on the radio-therapy planning process. Current methods for the estimation of tumor mobility are either two-dimensional (fluoroscopy, 2D dynamic MRI) or based on radiation (3D (+t) CT, implanted gold markers). With current advances in dynamic MRI acquisition, 3D+t image sequences of the thorax can be acquired covering the thorax over the whole breathing cycle. In this work, methods are presented for the interactive segmentation of tumors in dynamic images, the calculation of tumor trajectories, dynamic tumor volumetry and dynamic tumor rotation/deformation based on 3D dynamic MRI. For volumetry calculation, a set of 21 related partial volume correcting volumetry algorithms has been evaluated based on tumor surrogates. Conventional volumetry based on voxel counting yielded a root mean square error of 29% compared to a root mean square error of 11% achieved by the algorithm performing best among the different volumetry methods. The new workflow has been applied to a set of 26 patients. Preliminary results indicate, that 3D dynamic MRI reveals important aspects of tumor behavior during the breathing cycle. This might imply the possibility to further improve high-precision radiotherapy techniques.

Pulmonary diseases such as bronchiectasis, asthma, and emphysema are characterized by abnormalities in airway dimensions. Multi-slice computed tomography (MSCT) has become one of the primary means to depict these abnormalities, as the availability of high-resolution near-isotropic data makes it possible to evaluate airways at oblique angles to the scanner plane. However, currently, clinical evaluation of airways is typically limited to subjective visual inspection only: systematic evaluation of the airways to take advantage of high-resolution data has not proved practical without automation. We present an automated method to quantitatively evaluate airway lumen diameter, wall thickness and broncho-arterial ratios. In addition, our method provides 3D visualization of these values, graphically illustrating the location and extent of disease. Our algorithm begins by automatic airway segmentation to extract paths to the distal airways, and to create a map of airway diameters. Normally, airway diameters decrease as paths progress distally; failure to taper indicates abnormal dilatation. Our approach monitors airway lumen diameters along each airway path in order to detect abnormal profiles, allowing even subtle degrees of pathologic dilatation to be identified. Our method also systematically computes the broncho-arterial ratio at every terminal branch of the tree model, as a ratio above 1 indicates potentially abnormal bronchial dilatation. Finally, the airway wall thickness is computed at corresponding locations. These measurements are used to highlight abnormal branches for closer inspection, and can be summed to compute a quantitative global score for the entire airway tree, allowing reproducible longitudinal assessment of disease severity. Preliminary tests on patients diagnosed with bronchiectasis demonstrated rapid identification of lack of tapering, which also was confirmed by corresponding demonstration of elevated broncho-arterial ratios.

We introduce an automatic 3D multiscale automatic segmentation algorithm for delineating specific organs in Magnetic Resonance images (MRI). The algorithm can process several modalities simultaneously, and handle both isotropic and anisotropic data in only linear time complexity. It produces a hierarchical decomposition of MRI scans. During this segmentation process a rich set of features describing the segments in terms of intensity, shape and location are calculated, reflecting the formation of the hierarchical decomposition. We show that this method can delineate the entire uterus of the rat abdomen in 3D MR images utilizing a combination of scanning protocols that jointly achieve high contrast between the uterus and other abdominal organs and between inner structures of the rat uterus. Both single and multi-channel automatic segmentation demonstrate high correlation to a manual segmentation. While the focus here is on the rat uterus, the general approach can be applied to recognition in 2D, 3D and multi-channel medical images.

Delineation of objects within medical images is often difficult to perform reproducibly when one relies upon hand-segmentation. To avoid inter- and intra-user variability, a semi-automatic segmentation method can more accurately and consistently determine the object boundaries. This paper presents a semi-automatic process for determining the length and volume of the spinal cord between adjacent pairs of intervertebral discs and the total length and volume of the spinal cord. A level set segmentation was performed on MRI data with user selected landmarks in order to obtain a segmentation of the spinal cord. The length and volume measurements were performed on 20 segments from C1 to L1 with five sets of user selected landmarks. Our results show that the average spinal cord segment length was 21.55 mm with a standard deviation of 25.11% and the average spinal cord segment volume was 2,217.16 mm³ with a standard deviation of 80.51%. The measurement variability of a single anatomical length across multiple trials of different sets of seed points was three orders of magnitude lower (0.06%) than the variability across different anatomical lengths (25.23%), while the measurement variability of a single anatomical volume across multiple trials of different sets of seed points was two orders of magnitude lower (0.37%) than the variability across different anatomical volumes (79.24%). Our method has been demonstrated to be potentially insensitive to intra- and inter-user variability.

Magnetic resonance (MR) imaging is an imaging modality that is used in the management and diagnosis of acute stroke. Common MR imaging techniques such as diffusion weighted imaging (DWI) and apparent diffusion coefficient maps (ADC) are used routinely in the diagnosis of acute infarcts. However, advances in radiology information systems and imaging protocols have led to an overload of image information that can be difficult to manage and time consuming. Automated techniques to assist in the identification of acute ischemic stroke can prove beneficial to 1) the physician by providing a mechanism for early detection and 2) the patient by providing effective stroke therapy at an early stage. We have processed DW images and ADC maps using a novel automated Relative Difference Map (RDM) method that was tailored to the identification and delineation of the stroke region. Results indicate that the technique can delineate regions of acute infarctions on DW images and ADC maps. A formal evaluation of the RDM algorithm was performed by comparing accuracy measurements between 1) expert generated ground truths with the RDM delineated DWI infarcts and 2) RDM delineated DWI infarcts with RDM delineated ADC infarcts. The accuracy measurements indicate that the RDM delineated DWI infarcts are comparable to the expert generated ground truths. The true positive volume fraction value (TPVF), between RDM delineated DWI and ADC infarcts, is nonzero for all cases with an acute infarct while the value for non-acute cases remains zero.

Multiple Sclerosis (MS) is an inflammatory and demyelinating disorder of the central nervous system with a presumed immune-mediated etiology. For treatment of MS, the measurements of white matter (WM), gray matter (GM), and cerebral spinal fluid (CSF) are often used in conjunction with clinical evaluation to provide a more objective measure of MS burden. In this paper, we apply a new unifying automatic mixture-based algorithm for segmentation of brain tissues to quantitatively analyze MS. The method takes into account the following effects that commonly appear in MR imaging: 1) The MR data is modeled as a stochastic process with an inherent inhomogeneity effect of smoothly varying intensity; 2) A new partial volume (PV) model is built in establishing the maximum a posterior (MAP) segmentation scheme; 3) Noise artifacts are minimized by a priori Markov random field (MRF) penalty indicating neighborhood correlation from tissue mixture. The volumes of brain tissues (WM, GM) and CSF are extracted from the mixture-based segmentation. Experimental results of feasibility studies on quantitative analysis of MS are presented.

This paper presents (1) an improved hierarchical method for segmenting the component tissue regions in fast spin echo T2 and PD images of the brain of Multiple Sclerosis (MS) patients, and (2) a methodology to characterize the disease utilizing the distributions of standardized T2 and PD intensities in the segmented tissue regions. First, the background intensity inhomogeneities are corrected and the intensity scales are standardized for all acquired images. The segmentation method imposes a feedback-like procedure on our previously developed hierarchical brain tissue segmentation method. With gradually simplified patterns in images and stronger evidences, pathological objects are recognized and segmented in an interplay fashion. After the brain parenchymal (BP) mask is generated, an under-estimated gray matter mask (uGM) and an over-estimated white matter mask (oWM) are created. Pure WM (PWM) and lesion (LS) masks are extracted from the all-inclusive oWM mask. By feedback, accurate GM and WM masks are subsequently formed. Finally, partial volume regions of GM and WM as well as Dirty WM (DWM) masks are generated. Intensity histograms and their parameters (peak height, peak location, and 25th, 50th and 75th percentile values) are computed for both T2 and PD images within each tissue region. Tissue volumes are also estimated. Spearman correlation coefficient rank test is then utilized to assess if there exists a trend between clinical states and the image-based parameters. This image analysis method has been applied to a data set consisting of 60 patients with MS and 20 normal controls. LS related parameters and clinical Extended Disability Status Scale (EDSS) scores demonstrate modest correlations. Almost every intensity-based parameter shows statistical difference between normal control and patient groups with a level better than 5%. These results can be utilized to monitor disease progression in MS.

Osteoporosis is the cause of over 1.5 million bone fractures annually. Most of these fractures occur in sites rich in trabecular bone, a complex network of bony struts and plates found throughout the skeleton. The three-dimensional structure of the trabecular bone network significantly determines mechanical strength and thus fracture resistance. Here we present a data acquisition and processing system that allows efficient noninvasive assessment of trabecular bone structure through a "virtual bone biopsy". High-resolution MR images are acquired from which the trabecular bone network is extracted by estimating the partial bone occupancy of each voxel. A heuristic voxel subdivision increases the effective resolution of the bone volume fraction map and serves a basis for subsequent analysis of topological and orientational parameters. Semi-automated registration and segmentation ensure selection of the same anatomical location in subjects imaged at different time points during treatment. It is shown with excerpts from an ongoing clinical study of early post-menopausal women, that significant reduction in network connectivity occurs in the control group while the structural integrity is maintained in the hormone replacement group. The system described should be suited for large-scale studies designed to evaluate the efficacy of therapeutic intervention in subjects with metabolic bone disease.

Biomechanical testing is the gold standard to determine bone competence, and has been used extensively. Direct mechanical testing provides detailed information on overall bone mechanical and material properties, but fails in revealing local properties such as local deformations and strains or quantification of fracture progression. Therefore, we incorporated several imaging methods in our mechanical setups in order to get a better insight into bone deformation and failure characteristics. Our aim was to develop an integrative approach for hierarchical investigation of bone, working at different scales of resolution ranging from the whole bone to its ultrastructure. At a macroscopic level, we used high-resolution and high-speed cameras which drastically increased the amount of information obtained from a biomechanical bone test. The new image data proved especially important when dealing with very small bones such as the murine femur. Here the feedback of the camera in the process of aligning and positioning the samples is indispensable for reproducibility. In addition, global failure behavior and fracture initiation can now be visualized with high temporal resolution. At a microscopic level, bone microstructure, i.e. trabecular architecture and cortical porosity, are known to influence bone strength and failure mechanisms significantly. For this reason, we developed an image-guided failure assessment technique, also referred to as functional microimaging, allowing direct time-lapsed 3D visualization and computation of local displacements and strains for better quantification of fracture initiation and progression at the microscopic level. While the resolution of typical desktop micro-computed tomography is around a few micrometers, highly brilliant X-rays from synchrotron radiation permit to explore the nanometer world. This allowed, for the first time, to uncover fully nondestructively the 3D ultrastructure of bone including vascular and cellular structures and to investigate their role in development of bone microcracks in an unprecedented resolution. We conclude that functional microimaging, i.e. the combination of biomechanical testing with non-destructive 3D imaging and visualization are extremely valuable in studying bone failure mechanisms. Functional investigation of microcrack initiation and propagation will lead to a better understanding of the relative contribution of bone mass and bone quality to bone competence.

MR elastography (MRE) images the intrinsic mechanical properties of soft tissues; e.g., the shear modulus, μ. The μ of the plantar soft tissues is important in understanding the mechanisms whereby the forces induced during normal motion produce ulcers that lead to amputation in diabetic feet. We compared the compliance of the heel fat pad to compressive forces and to shearing forces. The design of prosthetics to protect the foot depends on the proper understanding of the mechanisms inducing damage. In the heel fat pads of six normal subjects, between 25 and 65 years of age, the μ for deformation perpendicular to the direction of weight bearing is similar but not identical to that determined for deformation along the weight bearing axis. The average difference between μ along the weight bearing axis and μ perpendicular to the weight bearing axis, is well correlated with age (Correlation Coefficient = 0.789). The p-value for the data being random was 0.0347 indicating that the observed difference is not likely to be random. The p-value for control points is 0.8989, indicating a random process. The results are suggestive that the high compressive forces imposed during walking damage the heel fat pads over time resulting in softening to compression preferentially over shearing. It is important to validate the observed effect with larger numbers of subjects, and better controls including measures of activity, and to understand if diseases like diabetes increase the observed damage.

PURPOSE: To assess the feasibility of a modified phase-contrast MRI technique (MR Elastography) for quantitatively assessing the mechanical properties of hepatic tissues by imaging propagating acoustic shear waves. MATERIALS AND METHODS: Both phantom and human studies were performed to develop and optimize a practical imaging protocol by visualizing and investigating the diffraction field of shear waves generated from pneumatic longitudinal drivers. The effects of interposed ribs in a transcostal approach were also investigated. A gradient echo MRE pulse sequence was adapted for shear wave imaging in the liver during suspended respiration, and then tested to measure hepatic shear stiffness in 13 healthy volunteers and 1 patient with chronic liver disease to determine the potential of non-invasively detecting liver fibrosis. RESULTS: Phantom studies demonstrate that longitudinal waves generated by the driver are mode-converted to shear waves in a distribution governed by diffraction principles. The transcostal approach was determined to be the most effective method for generating shear waves in human studies. Hepatic stiffness measurements in the 13 normal volunteers demonstrated a mean value of 2.0±0.2kPa. The shear stiffness measurement in the patient was much higher at 8.5kPa. CONCLUSION: MR Elastography of the liver shows promise as a method to non-invasively detect and characterize diffuse liver disease, potentially reducing the need for biopsy to diagnose hepatic fibrosis.

This work extends a recently realized inverse problem technique of extracting soft tissue elasticity information via non-rigid model-based image registration. The algorithm uses the elastic properties of the tissue in a biomechanical model to achieve maximal similarity between image data acquired under different states of loading. A new multi-resolution, non-linear optimization framework has been employed which allows for improved performance and object detection. Prior studies have demonstrated successful reconstructions from images of a tissue-like thin membrane phantom with a single embedded inclusion that was significantly stiffer than its surroundings. For this investigation, a similar phantom was fabricated with two stiff inclusions to test the effectiveness of this method in discriminating multiple smaller objects. Elasticity values generated from both simulation and real data testing scenarios provided sufficient contrast for detection and good quantitative localization of the inclusion areas.

Proper targeting of radiation therapy during the treatment of prostate cancer requires the successful alignment of initial planning images with more recent ones taken during the treatment course. Prostatic displacement, if unaccounted for during the treatment course, can lead to radiation underdosage of the target area or radiation of the surrounding healthy tissue. Studies have shown that prostatic displacement is directly correlated with changes in the volume of the rectum. We have developed a non-rigid image registration system based on a biomechanical model of the prostate, rectum, and surrounding tissues, that incorporates statistical shape information about changes in the volume of the rectum. Using finite-element analysis and statistical shape models, our non-rigid registration method defines a mapping between two prostate image volumes. The proposed method assumes that the prostate image misregistration occurs as a result of changes in the rectum's shape. The change along the rectum's circumference was considered as the displacement boundary condition of the prostate's finite element model. As such we used a mutual information similarity measure in conjunction with the finite element model for computing the optimal boundary condition as well as estimating the location and relative Young's modulus of the central and peripheral zones within the prostate to the surrounding tissue. Compared to other techniques, this registration technique is not only efficient but also capable of providing valuable mechanical properties of tissue in vivo.

Magnetic resonance imaging (MRI) with an endorectal receiver coil (ERC) provides superior visualization of the prostate gland and its surrounding anatomy at the expense of large anatomical deformation. The ability to correct for this deformation is critical to integrate the MR images into the CT-based treatment planning for radiotherapy. The ability to quantify and understand the physiological motion due to large changes in rectal filling can also improve the precision of image-guided procedures. The purpose of this study was to understand the biomechanical relationship between the prostate, rectum, and bladder using a finite element-based multi-organ deformable image registration method, 'Morfeus' developed at our institution. Patients diagnosed with prostate cancer were enrolled in the study. Gold seed markers were implanted in the prostate and MR scans performed with the ERC in place and its surrounding balloon inflated to varying volumes (0-100cc). The prostate, bladder, and rectum were then delineated, converted into finite element models, and assigned appropriate material properties. Morfeus was used to assign surface interfaces between the adjacent organs and deform the bladder and rectum from one position to another, obtaining the position of the prostate through finite element analysis. This approach achieves sub-voxel accuracy of image co-registration in the context of a large ERC deformation, while providing a biomechanical understanding of the multi-organ physiological relationship between the prostate, bladder, and rectum. The development of a deformable registration strategy is essential to integrate the superior information offered in MR images into the treatment planning process.

Previous research has shown that CT-image-based guidance could be useful for the bronchoscopic assessment of lung cancer. This research drew upon the registration of bronchoscopic video images to CT-based endoluminal renderings of the airway tree. The proposed methods either were restricted to discrete single-frame registration, which took several seconds to complete, or required non-real-time buffering and processing of video sequences. We have devised a fast 2D/3D image registration method that performs single-frame CT-Video registration in under 1/15th of a second. This allows the method to be used for real-time registration at full video frame rates without significantly altering the physician's behavior. The method achieves its speed through a gradient-based optimization method that allows most of the computation to be performed off-line. During live registration, the optimization iteratively steps toward the locally optimal viewpoint at which a CT-based endoluminal view is most similar to a current bronchoscopic video frame. After an initial registration to begin the process (generally done in the trachea for bronchoscopy), subsequent registrations are performed in real-time on each incoming video frame. As each new bronchoscopic video frame becomes available, the current optimization is initialized using the previous frame's optimization result, allowing continuous guidance to proceed without manual re-initialization. Tests were performed using both synthetic and pre-recorded bronchoscopic video. The results show that the method is robust to initialization errors, that registration accuracy is high, and that continuous registration can proceed on real-time video at >15 frames per sec. with minimal user-intervention.

This paper presents a method for identifying branches for CT-guided bronchoscopy based on eigenspace image matching. This method outputs the current location of a real bronchoscope (RB) by displaying branches where a bronchoscope is currently observing or by presenting anatomical names of branches currently being observed. In the previous method of bronchoscope navigation, the motion of a real bronchoscope is tracked by image registration between RB and virtual bronchoscopic (VB) images. Although bronchoscope tracking based on image registration gives us very accurate tracking results, it requires a lot of computation time and it is difficult to perform real-time tracking. If we focus only on navigation to a target branch, it is enough to identify a branch where a bronchoscope is currently located. This paper presents a method for identifying branches in which a bronchoscope is currently observing and presenting their anatomical names. Branch identification is done by image matching between RB images and pre-generated VB images. VB images are pre-generated at each bifurcation point based on structural analysis results of bronchi regions extracted from CT images. For each frame of an RB video, we find the most similar VB image to the input one from a training dataset (pre-generated VB image) and output the branch levels associated with the found image by using the eigenspace method. We have applied the proposed method to a pair of comprising a 3D CT image and real bronchoscopic video footage. The experimental results showed that the proposed method can identify branches for about 77.7% of the input frames.

We present a synchronous navigation module for CT colonography (CTC) reading. The need for such a system arises because most CTC protocols require a patient to be scanned in both supine and prone positions to increase sensitivity in detecting colonic polyps. However, existing clinical practices are limited to reading one scan at a time. Such limitation is due to the fact that building a reference system between scans for the highly flexible colon is a nontrivial task. The conventional centerline approach, generating only the longitudinal distance along the colon, falls short in providing the necessary orientation information to synchronize the virtual navigation cameras in both scanned positions. In this paper we describe a synchronous navigation system by using the teniae coli as anatomical references. Teniae coli are three parallel bands of longitudinal smooth muscle on the surface of the colon. They are morphologically distinguishable and form a piecewise triple helix structure from the appendix to the sigmoid colon. Because of these characteristics, they are ideal references to synchronize virtual cameras in both scanned positions. Our new navigation system consists of two side-by-side virtual colonoscopic view panels (for the supine and prone data sets respectively) and one single camera control unit (which controls both the supine and prone virtual cameras). The capability to examine the same colonic region simultaneously in both scanned images can raise an observer's confidence in polyp identification and potentially improve the performance of CT colonography.

Cardiac arrhythmias are a debilitating, potentially life threatening condition involving aberrant electrical activity in the heart which results in abnormal heart rhythm. Virtual cardioscopy can play an important role in minimally invasive treatment of cardiac arrhythmias. Second and third generation image-guidance systems are now available for the treatment of arrhythmias using RF ablation catheters. While these 3D tools provide useful information to the clinician, additional enhancements to the virtual cardioscopy display paradigm are critical for optimal therapy guidance. Based on input from clinical collaborators, several key visualization techniques have been developed to enhance the use of virtual cardioscopy during cardiac ablation procedures. We have identified, designed and incorporated several visual cues important to successful virtual cardioscopy. These features include the use of global reference maps, parametric mapping, and focused navigation and targeting using abnormal electro-physiologic activity. Our virtual cardioscopy system is designed for real-time use during RF cardiac ablation procedures. Several unique visualizations from our virtual cardioscopy system will be presented. Evaluation of the system with phantom and animal studies will be presented. This research is supported by grant EB002834 from the National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health.

This paper presents an improved method for virtually unfolding an organ and visualizing its entire luminal surface in only one view. Unfolded tract views can be very useful as they allow doctors to understand various kinds of information of the luminal surface intuitively, just like observing a pathological specimen. However, the previous method cannot correctly reproduce the luminal surface because elasticity for the organ walls is quite coarse defined. Thus, three improvements are proposed: (1) accurate elastic modeling using mass points and Kelvin-Voigt visco-elastic elements, (2) stable image deformation by the Newmark-β method, and (3) automatically directing organ walls to flat shapes by forces determined from their surface normals. Unfolded views generated by the proposed method from seventeen 3D CT image datasets are compared with those by the previous method, virtual endoscopic images, and pathological specimens. Several regions on the luminal surface, which could not be reproduced by the previous method, were accurately reproduced. Bending and concave parts of organ walls, which were difficult to unfold in the previous method, were satisfactorily flattened by introducing improved deformation processes. Computation time was reasonably reduced. Unfolded views of twelve cases were presented to doctors for surgical planning. The unfolded views generated by the proposed method were considered to have well reproduced all lesions as well as fold patterns observed in virtual endoscopic images.

We have evaluated the feasibility of polyp detection on simulated ultra low dose CT Colonography data by a computer aided polyp detection (CAD) algorithm. We compared the results of ultra low dose to normal dose data. Twenty-three extensively prepared patients were scanned in prone and supine position at 25 to 100 mAs (average 70 mAs) depending on their waist circumference. Noise was added and the scans were reconstructed at 6.25 and 1.39 mAs. To evaluate the performance of the CAD system, polyps detected by an experienced reviewer and confirmed at colonoscopy were used as ground truth. Curvature, concavity and sphericity of the colon surface were used to detect polyp candidates. Bilateral filtering was used to reduce noise. We present the results for 40 polyps of 6 mm or larger as measured during colonoscopy. The by-polyp sensitivity was 80% for medium size polyps (6-9 mm) and 97% for large polyps (10 mm or larger) at an average value of 5 false-positives per scan for normal dose data. The by-polyp sensitivity was 81% for medium size polyps and 85% for large size polyps at an average value of 5 false-positives per scan for low dose data (6.25 mAs). Finally for the ultra low dose data (1.39 mAs) we achieved a by-polyp sensitivity of 75% for medium size polyps and 97% for large polyps at an average value of 5 false-positives per scan. The conclusion of our study is that CAD for polyp detection is feasible on ultra low dose CT colonography data.

Performance of Computed Tomographic Colonography (CTC) Computer Aided Detection (CTC CAD) depends sensitively on a set of features chosen to characterize a polyp candidate. Most of the features are derived from some shape related characteristics which are calculated at the points located within the boundaries of a polyp candidate. This approach ignores information from the part of the colonic wall stretching beyond the limits of a polyp candidate. We found that almost 90% of small and medium size polyps missed by our CAD program were located on haustral folds. This suggests that two different classifiers (for detections on a fold and not on a fold) could be used which are better tuned to the local characteristics. Therefore, we developed an automated method to verify independently if a given polyp candidate is located on a fold. This is done by checking the intensity profile along normals originating from the points on the colonic wall which are close to but outside of a patch marking a polyp candidate.

We introduce an intuitive measure of computer aided detection (CAD) system performance that can handle simultaneous variation of multiple parameters. On the example of CAD of colon polyps we demonstrate how this measure was used to find the optimal parameters and make improvements to the "water-plane" algorithm that finds initial polyp candidates on the colon wall. In particular, we improved the merging of overlapping clusters to only create fused clusters if they shared at least 50% of their vertices and adjusted size and thickness filter criteria to retain more true positive detections. The system, containing all optimizations, improved significantly over the original system that found initial detections based only on colon surface curvature. This improvement was measured by both free response operating curve (FROC) analysis and our new performance measure.

Virtual colonoscopy is becoming a more prevalent way to diagnose colon cancer. One of the critical elements in detecting cancerous polyps using virtual colonoscopy, especially in conjunction with computer-aided detection of polyps, is that the colon be sufficiently distended. We have developed an automatic method to determine from a CT scan what percentage of the colon is distended by 1cm or larger and compared our method with a radiologist's assessment of quality of the scan with respect to successful colon polyp detection. A radiologist grouped 41 CT virtual colonoscopy scans into three groups according to the degree of colonic distention, "well", "medium", and "poor". We also employed a subvoxel accurate centerline algorithm and a subvoxel accurate distance transform to each dataset to measure the colon distention along the centerline. To summarize the colonic distention with a single value relevant for polyp detection, the distention score, we recorded the percentage of centerline positions in which the colon distention was 1cm or larger. We then compared the radiologist's assessment and the computed results. The sorting of all datasets according to the distention score agreed with the radiologist's assessment. The "poor" cases had a mean and standard deviation score of 78.4% ± 5.2%, the "medium" cases measured 88.7% ± 1.9%, and the "well" cases 98.8% ± 1.5%. All categories were shown to be significantly different from each other using unpaired two sample t-tests. The presented colonic distention score is an accurate method for assessing the quality of colonic distention for CT colonography.

Recently, virtual colonoscopy (VC) has received attention as a new colon diagnostic method based on the development of imaging devices. VC is considered a less invasive inspection and reduces diagnosing time. However, because the colon has many folds and its shape is long and convoluted, a physician has to repeatedly change viewpoints and viewing directions many times. We previously proposed a new computer aided diagnosis system for the colon that provided virtual unfolded (VU) and virtual endoscopic (VE) views of the colon. This system enables physicians to observe a large area of the colonic wall on a VU view synchronized with a VE view. Thus a suspicious area on a VU view can be observed in more detail on a VE view. We generated VU views by controlling the ray directions of volume rendering. This method had a problem: rays intersected at the curved areas of the colon because the rays were cast perpendicular to the central path of the colon. Ray intersections caused spurious holes in the VU views. In this paper, we present a method that reduces ray intersections by employing a model that allocates springs between planes perpendicular to the central path. Then plane directions are modified by the spring forces to minimize the total length of the springs. In a drawing process, a VU view is generated by casting rays along the planes. We applied the method to abdominal CT images. Experimental results showed that the method can reduce spurious holes on VU views more than the previous method.

The rectum can distend to accommodate stool, and contracts in response to distention during defecation. Rectal motor dysfunctions are implicated in the pathophysiology of functional defecation disorders and fecal incontinence. These rectal motor functions can be studied by intra-luminal measurements of pressure by manometry, or combined with volume during rectal balloon distention. Pressure-volume (p-v) relationships provide a global index of rectal mechanical properties. However, balloon distention alone does not measure luminal radius or wall thickness, which are necessary to compute wall tension and stress respectively. It has been suggested that the elastic modulus, which is the linear slope of the stress-strain relationship, is a more accurate measure of wall stiffness. Also, measurements of compliance may not reflect differences in rectal diameter between subjects prior to inflation, and imaging is necessary to determine if, as has been suggested, rectal pressure-volume relationships are affected by extra-rectal structures. We have developed a technique to measure rectal stress:strain relationships in humans, by simultaneous magnetic resonance imaging (MRI) during rectal balloon distention. After a conditioning distention, a rectal balloon was distended with water from 0 to 400 ml in 50 ml steps, and imaged at each step with MRI. The fluid filled balloon was segmented from each volume, the phase-ordered binary volumes were transformed into a geometric characterization of the inflated rectal surface. Taken together with measurements of balloon pressure and of rectal wall thickness, this model of the rectal surface was used to calculate regional values of curvature, tension, strain, and stress for the rectum. In summary, this technique has the unique ability to non-invasively measure the rectal stress:strain relationship and also determine if rectal expansion is limited by extra-rectal structures. This functional information allows the direct clinical analysis of rectal motor function and offers the potential for characterizing abnormal mechanical properties of the rectal wall in disease.

We developed a cryomicrotome/imaging system that provides high resolution, high sensitivity block-face images of whole mice or excised organs, and applied it to a variety of biological applications. With this cryo-imaging system, we sectioned cryo-preserved tissues at 2-40 μm thickness and acquired high resolution brightfield and fluorescence images with microscopic in-plane resolution (as good as 1.2 μm). Brightfield images of normal and pathological anatomy show exquisite detail, especially in the abdominal cavity. Multi-planar reformatting and 3D renderings allow one to interrogate 3D structures. In this report, we present brightfield images of mouse anatomy, as well as 3D renderings of organs. For BPK mice model of polycystic kidney disease, we compared brightfield cryo-images and kidney volumes to MRI. The color images provided greater contrast and resolution of cysts as compared to in vivo MRI. We note that color cryo-images are closer to what a researcher sees in dissection, making it easier for them to interpret image data. The combination of field of view, depth of field, ultra high resolution and color/fluorescence contrast enables cryo-image volumes to provide details that cannot be found through in vivo imaging or other ex vivo optical imaging approaches. We believe that this novel imaging system will have applications that include identification of mouse phenotypes, characterization of diseases like blood vessel disease, kidney disease, and cancer, assessment of drug and gene therapy delivery and efficacy and validation of other imaging modalities.

Purpose: To evaluate photoacoustic CT spectroscopy (PCT-S) and dynamic contrast-enhanced CT (DCE-CT) ability to measure parameters - oxygen saturation and vascular physiology - associated with the intra-tumor oxygenation status. Material and Methods: Breast (VEGF165 enhance MCF-7) and ovarian (SKOV3x) cancer cells were implanted into the fat pads and flanks of immune deficient mice and allowed to grow to a diameter of 8-15 mm. CT was used to determine physiological parameters by acquiring a sequence of scans over a 10 minute period after an i.v. injection of a radio-opaque contrast agent (Isovue). These time-dependent contrast-enhanced curves were fit to a two-compartmental model determining tumor perfusion, fractional plasma volume, permeability-surface area produce, and fractional interstitial volume on a voxel-by-voxel basis. After which, the tumors were imaged using photoacoustic CT (Optosonics, Inc., Indianapolis, IN 46202). The near infrared spectra (700-910 nm) within the vasculature was fit to linear combination of measured oxy- and deoxy-hemoglobin blood samples to obtain oxygen saturation levels (SaO₂). Results: The PCT-S scanner was first calibrated using different samples of oxygenated blood, from which a statistical error ranging from 2.5-6.5% was measured and a plot of the hemoglobin dissociation curve was consistent with empirical formula. In vivo determination of tumor vasculature SaO₂ levels were measurably tracked, and spatially correlated to the periphery of the tumor. Tumor depend variations in SaO₂ - 0.32 (ovarian) and 0.60 (breast) - and in vascular physiology - perfusion, 1.03 and 0.063 mL/min/mL, and fractional plasma volume, 0.20 and 0.07 - were observed. Conclusion: Combined, PCT-S and CED-CT has the potential to measure intra-tumor levels of tumor oxygen saturation and vascular physiology, key parameters associated with hypoxia.