Vessel surface reconstruction with a tubular deformable model

Customizable tubular model for n-furcating blood vessels and its application to 3D reconstruction of the cerebrovascular system

Understanding the 3D cerebral vascular network is one of the pressing issues impacting the diagnostics of various systemic disorders and is helpful in clinical therapeutic strategies. Unfortunately, the existing software in the radiological workstation does not meet the expectations of radiologists who require a computerized system for detailed, quantitative analysis of the human cerebrovascular system in 3D and a standardized geometric description of its components. In this study, we show a method that uses 3D image data from magnetic resonance imaging with contrast to create a geometrical reconstruction of the vessels and a parametric description of the reconstructed segments of the vessels. First, the method isolates the vascular system using controlled morphological growing and performs skeleton extraction and optimization. Then, around the optimized skeleton branches, it creates tubular objects optimized for quality and accuracy of matching with the originally isolated vascular data. Finally, it optimizes the joints on n-furcating vessel segments. As a result, the algorithm gives a complete description of shape, position in space, position relative to other segments, and other anatomical structures of each cerebrovascular system segment. Our method is highly customizable and in principle allows reconstructing vascular structures from any 2D or 3D data. The algorithm solves shortcomings of currently available methods including failures to reconstruct the vessel mesh in the proximity of junctions and is free of mesh collisions in high curvature vessels. It also introduces a number of optimizations in the vessel skeletonization leading to a more smooth and more accurate model of the vessel network. We have tested the method on 20 datasets from the public magnetic resonance angiography image database and show that the method allows for repeatable and robust segmentation of the vessel network and allows to compute vascular lateralization indices.

Shrinking tube mesh: combined mesh generation and smoothing for pathologic vessels

We present a mesh generation algorithm which is able to produce smooth meshes from point clouds derived from histological slices. In this work, the shrinking tube mesh generation is used on histologic images depicting pathologic vessels. Our mesh generation is modeled after the behaviour of a shrinking tube. A start shape is fitted iteratively to the point cloud. The presented algorithm was successfully used to generate meshes of the inner and outer contour from vessels in histologic images. While histologic slices have a high in-plane resolution, the large slice distance and deformations during tissue deformations are challenging for 3D model generation.

Accurate liver vessel segmentation via active contour model with dense vessel candidates

BACKGROUND AND OBJECTIVE

The purpose of this paper is to propose a fully automated liver vessel segmentation algorithm including portal vein and hepatic vein on contrast enhanced CTA images.

METHODS

First, points of a vessel candidate region are extracted from 3-dimensional (3D) CTA image. To generate accurate points, we reduce 3D segmentation problem to 2D problem by generating multiple maximum intensity (MI) images. After the segmentation of MI images, we back-project pixels to the original 3D domain. We call these voxels as vessel candidates (VCs). A large set of MI images can produce very dense and accurate VCs. Finally, for the accurate segmentation of a vessel region, we propose a newly designed active contour model (ACM) that uses the original image, vessel probability map from dense VCs, and the good prior of an initial contour.

RESULTS

We used 55 abdominal CTAs for a parameter study and a quantitative evaluation. We evaluated the performance of the proposed method comparing with other state-of-the-art ACMs for vascular images applied directly to the original data. The result showed that our method successfully segmented vascular structure 25%-122% more accurately than other methods without any extra false positive detection.

CONCLUSION

Our model can generate a smooth and accurate boundary of the vessel object and easily extract thin and weak peripheral branch vessels. The proposed approach can automatically segment a liver vessel without any manual interaction. The detailed result can aid further anatomical studies.

Three-dimensional morphometric analysis of the renal vasculature

Vascular topology and morphology are critical in the regulation of blood flow and the transport of small solutes, including oxygen, carbon dioxide, nitric oxide, and hydrogen sulfide. Renal vascular morphology is particularly challenging, since many arterial walls are partially wrapped by the walls of veins. In the absence of a precise characterization of three-dimensional branching vascular geometry, accurate computational modeling of the intrarenal transport of small diffusible molecules is impossible. An enormous manual effort was required to achieve a relatively precise characterization of rat renal vascular geometry, highlighting the need for an automated method for analysis of branched vasculature morphology to allow characterization of the renal vascular geometry of other species, including humans. We present a semisupervised method for three-dimensional morphometric analysis of renal vasculature images generated by computed tomography. We derive quantitative vascular attributes important to mass transport between arteries, veins, and the renal tissue and present methods for their computation for a three-dimensional vascular geometry. To validate the algorithm, we compare automated vascular estimates with subjective manual measurements for a portion of rabbit kidney. Although increased image resolution can improve outcomes, our results demonstrate that the method can quantify the morphological characteristics of artery-vein pairs, comparing favorably with manual measurements. Similar to the rat, we show that rabbit artery-vein pairs become less intimate along the course of the renal vasculature, but the total wrapped mass transfer coefficient increases and then decreases. This new method will facilitate new quantitative physiological models describing the transport of small molecules within the kidney.

3D reconstruction of elastin fibres in coronary adventitia

A 3D reconstruction of individual fibres in vascular tissue is necessary to understand the microstructure properties of the vessel wall.  The objective of this study is to determine the 3D microstructure of elastin fibres in the adventitia of coronary arteries.  Quantification of fibre geometry is challenging due to the complex interwoven structure of the fibres.  In particular, accurate linking of gaps remains a significant challenge, and complex features such as long gaps and interwoven fibres have not been adequately addressed by current fibre reconstruction algorithms.  We use a novel line Laplacian deformation method, which better deals with fibre shape uncertainty to reconstruct elastin fibres in the coronary adventitia of five swine. A cost function, based on entropy and Euler Spiral, was used in the shortest path search. We find that mean diameter of elastin fibres is 1.67 ± 1.42 μm and fibre orientation is clustered around two major angles of 8.9˚ and 81.8˚.  Comparing with CT-FIRE, we find that our method gives more accurate estimation of fibre width.  To our knowledge, the measurements obtained using our algorithm represent the first investigation focused on the reconstruction of full elastin fibre length.  Our data provide a foundation for a 3D microstructural model of the coronary adventitia to elucidate the structure-function relationship of elastin fibres.

A Fast Semiautomatic Algorithm for Centerline-Based Vocal Tract Segmentation

Vocal tract morphology is an important factor in voice production. Its analysis has potential implications for educational matters as well as medical issues like voice therapy. The knowledge of the complex adjustments in the spatial geometry of the vocal tract during phonation is still limited. For a major part, this is due to difficulties in acquiring geometry data of the vocal tract in the process of voice production. In this study, a centerline-based segmentation method using active contours was introduced to extract the geometry data of the vocal tract obtained with MRI during sustained vowel phonation. The applied semiautomatic algorithm was found to be time- and interaction-efficient and allowed performing various three-dimensional measurements on the resulting model. The method is suitable for an improved detailed analysis of the vocal tract morphology during speech or singing which might give some insights into the underlying mechanical processes.

Coronary arteries segmentation based on the 3D discrete wavelet transform and 3D neutrosophic transform

PURPOSE

Most applications in the field of medical image processing require precise estimation. To improve the accuracy of segmentation, this study aimed to propose a novel segmentation method for coronary arteries to allow for the automatic and accurate detection of coronary pathologies.

METHODS

The proposed segmentation method included 2 parts. First, 3D region growing was applied to give the initial segmentation of coronary arteries. Next, the location of vessel information, HHH subband coefficients of the 3D DWT, was detected by the proposed vessel-texture discrimination algorithm. Based on the initial segmentation, 3D DWT integrated with the 3D neutrosophic transformation could accurately detect the coronary arteries.

RESULTS

Each subbranch of the segmented coronary arteries was segmented correctly by the proposed method. The obtained results are compared with those ground truth values obtained from the commercial software from GE Healthcare and the level-set method proposed by Yang et al., 2007. Results indicate that the proposed method is better in terms of efficiency analyzed.

CONCLUSION

Based on the initial segmentation of coronary arteries obtained from 3D region growing, one-level 3D DWT and 3D neutrosophic transformation can be applied to detect coronary pathologies accurately.

An effective and robust method for modeling multi-furcation liver vessel by using Gap Border Pairing

Shape-based 3D surface reconstructing methods for liver vessels have difficulties to tackle with limited contrast of medical images and the intrinsic complexity of multi-furcation parts. In this paper, we propose an effective and robust technique, called Gap Border Pairing (GBPa), to reconstruct surface of liver vessels with complicated multi-furcations. The proposed method starts from a tree-like skeleton which is extracted from segmented liver vessel volumes and preprocessed as a number of simplified smooth branching lines. Secondly, for each center point of any branching line, an optimized elliptic cross-section ring (contour) is generated by optimizedly fitting its actual cross-section outline based on its tangent vector. Thirdly, a tubular surface mesh is generated for each branching line by weaving all of its adjacent rings. Then for every multi-furcation part, a transitional regular mesh is effectively and regularly reconstructed by using GBP. An initial model is generated after reconstructing all multi-furcation parts. Finally, the model is refined by using just one time subdivision and its topologies can be re-maintained by grouping its facets according to the skeleton, providing high-level editability. Our method can be automatically implemented in parallel if the segmented vessel volume and corresponding skeletons are provided. The experimental results show that GBP model is accurate enough in terms of the boundary deviations between segmented volume and the model.

A variational surface deformation and subdivision-based modeling framework for noisy and small n-furcated tube-like structures

It is challenging to construct an accurate and smooth mesh for noisy and small n-furcated tube-like structures, such as arteries, veins, and pathological vessels, due to tiny vessel size, noise, n -furcations, and irregular shapes of pathological vessels. We propose a framework by dividing the modeling process into mesh construction and mesh refinement. In the first step, we focus on mesh topological correctness, and just create an initial rough mesh for the n-furcated tube-like structures. In the second step, we propose a variational surface deformation method to push the initial mesh to structure boundaries for positional accuracy improvement. By iteratively solving Euler-Lagrange equations derived from the minimization of the shell and distance energies, the initial mesh can be gradually pushed to the boundaries. A mesh dilation method is proposed to prevent the extremely deviated initial mesh moving toward wrong boundaries. We combine deformation and subdivision to propose a coarse-to-fine modeling framework for the improvement of efficiency and accuracy. Experiments show our method can construct an accurate and smooth mesh for noisy and small n-furcated tube-like structures, and it is useful in hemodynamics, quantitative measurement, and analysis of vessels.

Hemodynamics of cerebral aneurysms: computational analyses of aneurysm progress and treatment

The progression of a cerebral aneurysm involves degenerative arterial wall remodeling. Various hemodynamic parameters are suspected to be major mechanical factors related to the genesis and progression of vascular diseases. Flow alterations caused by the insertion of coils and stents for interventional aneurysm treatment may affect the aneurysm embolization process. Therefore, knowledge of hemodynamic parameters may provide physicians with an advanced understanding of aneurysm progression and rupture, as well as the effectiveness of endovascular treatments. Progress in medical imaging and information technology has enabled the prediction of flow fields in the patient-specific blood vessels using computational analysis. In this paper, recent computational hemodynamic studies on cerebral aneurysm initiation, progress, and rupture are reviewed. State-of-the-art computational aneurysmal flow analyses after coiling and stenting are also summarized. We expect the computational analysis of hemodynamics in cerebral aneurysms to provide valuable information for planning and follow-up decisions for treatment.

Virtual Interventions for Image-based Blood Flow Computation

Image-based blood flow computation provides great promise for evaluation of vascular devices and assessment of surgical procedures. However, many previous studies employ idealized arterial and device models or only patient-specific models from the image data after device deployment, since the tools for model construction are unavailable or limited and tedious to use. Moreover, in contrast to retrospective studies from existing data, there is a pressing need for prospective analysis with the goal of surgical planning. Therefore, it is necessary to construct models with deployed devices in a fast, virtual and interactive fashion. The goal of this paper is to develop new geometric methods to deploy stents or stent grafts virtually to patient-specific geometric models constructed from a 3D segmentation of medical images. A triangular surface representing the vessel lumen boundary is extracted from the segmentation. The diseased portion is either clipped and replaced by the surface of a deployed device or rerouted in the case of a bypass graft. For diseased arteries close to bifurcations, bifurcated device models are generated. A method to map a 2D strut pattern on the surface of a device is also presented. We demonstrate three applications of our methods in personalized surgical planning for aortic aneurysms, aortic coarctation, and coronary artery stenosis using blood flow computation. Our approach enables prospective model construction and may help to expand the throughput required by routine clinical uses in the future.

Modeling n-furcated liver vessels from a 3-D segmented volume using hole-making and subdivision methods

It is difficult to build an accurate and smooth liver vessel model due to the tiny size, noise, and n-furcations of vessels. To overcome these problems, we propose an n-furcation vessel tree modeling method. In this method, given a segmented volume and a point indicating the root of the vessels, centerlines and cross-sectional contours of the vessels are extracted and organized as a tree first. Then, the tree is broken up into separate branches in descending order of length, and polygonal meshes of all the branches are separately constructed from the cross-sectional contours. Finally, all the meshes are combined sequentially using our hole-making approach. Holes are made on a coarse mesh, and a final fine mesh is generated using a subdivision method. The hole-making approach with the subdivision method provides good efficiency in mesh construction as well as great flexibilities in mesh editing. Experiments show that our method can automatically construct smooth mesh models for n-furcated vessels with mean absolute error of 0.92 voxel and mean relative error of 0.17. It is promising to be used in diagnosis, analysis, and surgery simulation of liver diseases, and is able to model tubular structures with tree topology.

A 2D driven 3D vessel segmentation algorithm for 3D digital subtraction angiography data

Cerebrovascular disease is among the leading causes of death in western industrial nations. 3D rotational angiography delivers indispensable information on vessel morphology and pathology. Physicians make use of this to analyze vessel geometry in detail, i.e. vessel diameters, location and size of aneurysms, to come up with a clinical decision. 3D segmentation is a crucial step in this pipeline. Although a lot of different methods are available nowadays, all of them lack a method to validate the results for the individual patient. Therefore, we propose a novel 2D digital subtraction angiography (DSA)-driven 3D vessel segmentation and validation framework. 2D DSA projections are clinically considered as gold standard when it comes to measurements of vessel diameter or the neck size of aneurysms. An ellipsoid vessel model is applied to deliver the initial 3D segmentation. To assess the accuracy of the 3D vessel segmentation, its forward projections are iteratively overlaid with the corresponding 2D DSA projections. Local vessel discrepancies are modeled by a global 2D/3D optimization function to adjust the 3D vessel segmentation toward the 2D vessel contours. Our framework has been evaluated on phantom data as well as on ten patient datasets. Three 2D DSA projections from varying viewing angles have been used for each dataset. The novel 2D driven 3D vessel segmentation approach shows superior results against state-of-the-art segmentations like region growing, i.e. an improvement of 7.2% points in precision and 5.8% points for the Dice coefficient. This method opens up future clinical applications requiring the greatest vessel accuracy, e.g. computational fluid dynamic modeling.

Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study

The purpose of this work was to characterize expert variation in segmentation of intracranial structures pertinent to radiation therapy, and to assess a registration-driven atlas-based segmentation algorithm in that context. Eight experts were recruited to segment the brainstem, optic chiasm, optic nerves, and eyes, of 20 patients who underwent therapy for large space-occupying tumors. Performance variability was assessed through three geometric measures: volume, Dice similarity coefficient, and Euclidean distance. In addition, two simulated ground truth segmentations were calculated via the simultaneous truth and performance level estimation algorithm and a novel application of probability maps. The experts and automatic system were found to generate structures of similar volume, though the experts exhibited higher variation with respect to tubular structures. No difference was found between the mean Dice similarity coefficient (DSC) of the automatic and expert delineations as a group at a 5% significance level over all cases and organs. The larger structures of the brainstem and eyes exhibited mean DSC of approximately 0.8-0.9, whereas the tubular chiasm and nerves were lower, approximately 0.4-0.5. Similarly low DSCs have been reported previously without the context of several experts and patient volumes. This study, however, provides evidence that experts are similarly challenged. The average maximum distances (maximum inside, maximum outside) from a simulated ground truth ranged from (-4.3, +5.4) mm for the automatic system to (-3.9, +7.5) mm for the experts considered as a group. Over all the structures in a rank of true positive rates at a 2 mm threshold from the simulated ground truth, the automatic system ranked second of the nine raters. This work underscores the need for large scale studies utilizing statistically robust numbers of patients and experts in evaluating quality of automatic algorithms.

Simulation of blood flow in deformable vessels using subject-specific geometry and spatially varying wall properties

Simulation of blood flow using image-based models and computational fluid dynamics has found widespread application to quantifying hemodynamic factors relevant to the initiation and progression of cardiovascular diseases and for planning interventions. Methods for creating subject-specific geometric models from medical imaging data have improved substantially in the last decade but for many problems, still require significant user interaction. In addition, while fluid-structure interaction methods are being employed to model blood flow and vessel wall dynamics, tissue properties are often assumed to be uniform. In this paper, we propose a novel workflow for simulating blood flow using subject-specific geometry and spatially varying wall properties. The geometric model construction is based on 3D segmentation and geometric processing. Variable wall properties are assigned to the model based on combining centerline-based and surface-based methods. We finally demonstrate these new methods using an idealized cylindrical model and two subject-specific vascular models with thoracic and cerebral aneurysms.

An atlas-navigated optimal medial axis and deformable model algorithm (NOMAD) for the segmentation of the optic nerves and chiasm in MR and CT images

In recent years, radiation therapy has become the preferred treatment for many types of head and neck tumors. To plan the procedure, vital structures, including the optic nerves and chiasm, must be identified using CT/MR imagery. In this work we present a novel method for automatically localizing the optic nerves and chiasm using a tubular structure localization algorithm in which a statistical model and image registration are used to incorporate a priori local intensity and shape information. The method results in mean Dice coefficients of 0.8 when compared to manual segmentations over ten test cases. This suggests that our method is more accurate than existing techniques developed for the segmentation of these structures.

Segmentation and tracking of cytoskeletal filaments using open active contours

We use open active contours to quantify cytoskeletal structures imaged by fluorescence microscopy in two and three dimensions. We developed an interactive software tool for segmentation, tracking, and visualization of individual fibers. Open active contours are parametric curves that deform to minimize the sum of an external energy derived from the image and an internal bending and stretching energy. The external energy generates (i) forces that attract the contour toward the central bright line of a filament in the image, and (ii) forces that stretch the active contour toward the ends of bright ridges. Images of simulated semiflexible polymers with known bending and torsional rigidity are analyzed to validate the method. We apply our methods to quantify the conformations and dynamics of actin in two examples: actin filaments imaged by TIRF microscopy in vitro, and actin cables in fission yeast imaged by spinning disk confocal microscopy.

Quantitative characterization of the hemodynamic environment in ruptured and unruptured brain aneurysms

BACKGROUND AND PURPOSE

Hemodynamics are thought to play an important role in the mechanisms of aneurysm pathogenesis, progression, and rupture. The purpose of this study was to define quantitative measures related to qualitative flow characteristics previously analyzed and to investigate their relationship to aneurysm rupture.

MATERIALS AND METHODS

The hemodynamic environments in 210 cerebral aneurysms were analyzed by using image-based CFD under different flow conditions. Quantitative hemodynamic variables were defined and extracted from the simulation results. A statistical analysis of the relationship to the previous history of aneurysm rupture was performed, and the variability with flow conditions was assessed.

RESULTS

Ruptured aneurysms were more likely to have larger inflow concentrations, larger MWSS, larger shear concentrations, and lower viscous dissipation ratios than unruptured aneurysms. Areas under low WSS and measures of abnormally low shear force distributions of ruptured and unruptured aneurysms were not statistically different. Although the values of hemodynamic quantities changed with different flow conditions, the statistical differences or ratios between their mean values over the ruptured and unruptured groups were maintained, for both pulsatile and steady flows.

CONCLUSIONS

Concentrated inflow streams and WSS distributions with elevated levels of MWSS and low aneurysmal viscous dissipation are statistically associated with a clinical history of prior aneurysm rupture. In contrast, the area and total viscous shear force applied in the aneurysm region subjected to abnormally low WSS levels are not. This study highlights the potential for image-based CFD for investigating aneurysm-evolution mechanisms and for clinical assessment of aneurysm risks.

Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation

This paper presents robust 3-D algorithms to segment vasculature that is imaged by labeling laminae, rather than the lumenal volume. The signal is weak, sparse, noisy, nonuniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding and Hessian filtering based methods are not effective. The structure deviates from a tubular geometry, so tracing algorithms are not effective. We propose a four step approach. The first step detects candidate voxels using a robust hypothesis test based on a model that assumes Poisson noise and locally planar geometry. The second step performs an adaptive region growth to extract weakly labeled and fine vessels while rejecting spectral artifacts. To enable interactive visualization and estimation of features such as statistical confidence, local curvature, local thickness, and local normal, we perform the third step. In the third step, we construct an accurate mesh representation using marching tetrahedra, volume-preserving smoothing, and adaptive decimation algorithms. To enable topological analysis and efficient validation, we describe a method to estimate vessel centerlines using a ray casting and vote accumulation algorithm which forms the final step of our algorithm. Our algorithm lends itself to parallel processing, and yielded an 8 x speedup on a graphics processor (GPU). On synthetic data, our meshes had average error per face (EPF) values of (0.1-1.6) voxels per mesh face for peak signal-to-noise ratios from (110-28 dB). Separately, the error from decimating the mesh to less than 1% of its original size, the EPF was less than 1 voxel/face. When validated on real datasets, the average recall and precision values were found to be 94.66% and 94.84%, respectively.

Hemodynamics in Normal Cerebral Arteries: Qualitative Comparison of 4D Phase-Contrast Magnetic Resonance and Image-Based Computational Fluid Dynamics

Detailed knowledge of the hemodynamic conditions in normal cerebral arteries is important for a better understanding of the underlying mechanisms leading to the initiation and progression of cerebrovascular diseases. Information about the baseline values of hemodynamic variables such as wall shear stresses is necessary for comparison to pathological conditions such as in cerebral aneurysms or arterial stenoses. The purpose of this study was to compare the blood flow patterns in cerebral arteries of normal subjects determined by 4D phase-contrast magnetic resonance and image-based computational fluid dynamics techniques in order to assess their consistency and to highlight their differences. The goal was not to validate (or disprove) any of the two methodologies but rather to identify regions where disagreements are to be expected and to provide guidance when interpreting the data produced by each technique.

Automatic segmentation of the facial nerve and chorda tympani in CT images using spatially dependent feature values

In cochlear implant surgery, an electrode array is permanently implanted in the cochlea to stimulate the auditory nerve and allow deaf people to hear. A minimally invasive surgical technique has recently been proposed-percutaneous cochlear access-in which a single hole is drilled from the skull surface to the cochlea. For the method to be feasible, a safe and effective drilling trajectory must be determined using a preoperative CT. Segmentation of the structures of the ear would improve trajectory planning safety and efficiency and enable the possibility of automated planning. Two important structures of the ear, the facial nerve and the chorda tympani, are difficult to segment with traditional methods because of their size (diameters as small as 1.0 and 0.3 mm, respectively), the lack of contrast with adjacent structures, and large interpatient variations. A multipart, model-based segmentation algorithm is presented in this article that accomplishes automatic segmentation of the facial nerve and chorda tympani. Segmentation results are presented for ten test ears and are compared to manually segmented surfaces. The results show that the maximum error in structure wall localization is approximately 2 voxels for the facial nerve and the chorda, demonstrating that the method the authors propose is robust and accurate.

Probabilistic vessel axis tracing and its application to vessel segmentation with stream surfaces and minimum cost paths

We propose a novel framework to segment vessels on their cross-sections. It starts with a probabilistic vessel axis tracing in a gray-scale three-dimensional angiogram, followed by vessel boundary delineation on cross-sections derived from the extracted axis. It promotes a more intuitive delineation of vessel boundaries which are mostly round on the cross-sections. The prior probability density function of the axis tracer's formulation permits seamless integration of user guidance to produce continuous traces through regions that contain furcations, diseased portions, kissing vessels (vessels in close proximity to each other) and thin vessels. The contour that outlines the vessel boundary in a 3-D space is determined as the minimum cost path on a weighted directed acyclic graph derived from each cross-section. The user can place anchor points to force the contour to pass through. The contours obtained are tiled to approximate the vessel boundary surface. Since we use stream surfaces generated w.r.t. the traced axis as cross-sections, non-intersecting adjacent cross-sections are guaranteed. Therefore, the tiling can be achieved by joining vertices of adjacent contours. The vessel boundary surface is then deformed under constrained movements on the cross-sections and is voxelized to produce the final vascular segmentation. Experimental results on synthetic and clinical data have shown that the vessel axes extracted by our tracer are continuous and less jittered as compared with the other two trace-based algorithms. Furthermore, the segmentation algorithm with cross-sections are robust to noise and can delineate vessel boundaries that have level of variability similar to those obtained manually.

Model-based automated extraction of microtubules from electron tomography volume

We propose a model-based automated approach to extracting microtubules from noisy electron tomography volume. Our approach consists of volume enhancement, microtubule localization, and boundary segmentation to exploit the unique geometric and photometric properties of microtubules. The enhancement starts with an anisotropic invariant wavelet transform to enhance the microtubules globally, followed by a three-dimensional (3-D) tube-enhancing filter based on Weingarten matrix to further accentuate the tubular structures locally. The enhancement ends with a modified coherence-enhancing diffusion to complete the interruptions along the microtubules. The microtubules are then localized with a centerline extraction algorithm adapted for tubular objects. To perform segmentation, we novelly modify and extend active shape model method. We first use 3-D local surface enhancement to characterize the microtubule boundary and improve shape searching by relating the boundary strength with the weight matrix of the searching error. We then integrate the active shape model with Kalman filtering to utilize the longitudinal smoothness along the microtubules. The segmentation improved in this way is robust against missing boundaries and outliers that are often present in the tomography volume. Experimental results demonstrate that our automated method produces results close to those by manual process and uses only a fraction of the time of the latter.

Automated extraction of fine features of kinetochore microtubules and plus-ends from electron tomography volume

Kinetochore microtubules (KMTs) and the associated plus-ends have been areas of intense investigation in both cell biology and molecular medicine. Though electron tomography opens up new possibilities in understanding their function by imaging their high-resolution structures, the interpretation of the acquired data remains an obstacle because of the complex and cluttered cellular environment. As a result, practical segmentation of the electron tomography data has been dominated by manual operation, which is time consuming and subjective. In this paper, we propose a model-based automated approach to extracting KMTs and the associated plus-ends with a coarse-to-fine scale scheme consisting of volume preprocessing, microtubule segmentation and plus-end tracing. In volume preprocessing, we first apply an anisotropic invariant wavelet transform and a tube-enhancing filter to enhance the microtubules at coarse level for localization. This is followed with a surface-enhancing filter to accentuate the fine microtubule boundary features. The microtubule body is then segmented using a modified active shape model method. Starting from the segmented microtubule body, the plus-ends are extracted with a probabilistic tracing method improved with rectangular window based feature detection and the integration of multiple cues. Experimental results demonstrate that our automated method produces results comparable to manual segmentation but using only a fraction of the manual segmentation time.

Patient-specific computational modeling of cerebral aneurysms with multiple avenues of flow from 3D rotational angiography images

RATIONALE AND OBJECTIVES

Previous studies of aneurysm flow dynamics based on three-dimensional (3D) rotational angiography (RA) images were limited to aneurysms with a single route of blood inflow. However, aneurysms of the circle of Willis frequently involve locations with more than one source of inflow, such as aneurysms of the anterior communicating artery. The highest resolution images of cerebral vessels are from RA images, but this technique is limited to visualizing only one route of inflow at a time, leaving a significant limitation in the application of 3DRA image sets for clinical studies of patient-specific computational fluid dynamics (CFD) simulations. In this report, subject-specific models of cerebral aneurysms with multiple avenues of flow are constructed from RA images by using a novel combination of image co-registration and surface merging techniques.

MATERIALS AND METHODS

RA images are obtained by means of contrast injection in each vessel that provides inflow to the aneurysm. Anatomic models are constructed independently of each of these vascular trees and fused together into a single model. The model is used to construct a finite element grid for CFD simulations of hemodynamics.

RESULTS

Three examples of patient-specific models are presented: an anterior communicating artery aneurysm, a basilar tip aneurysm, and a model of an entire circle of Willis with five coincident aneurysms. The method is evaluated with a numeric phantom of an aneurysm in the anterior communicating artery.

CONCLUSION

These examples show that this new technique can be used to create merged network numeric models for CFD modeling. Furthermore, intra-aneurysmal flow patterns are influenced strongly by merging of the two inflow streams. This effect decreases as distance from the merging streams increases.

Coronary vessel trees from 3D imagery: a topological approach

We propose a simple method for reconstructing vascular trees from 3D images. Our algorithm extracts persistent maxima of the intensity on all axis-aligned 2D slices of the input image. The maxima concentrate along 1D intensity ridges, in particular along blood vessels. We build a forest connecting the persistent maxima with short edges. The forest tends to approximate the blood vessels present in the image, but also contains numerous spurious features and often fails to connect segments belonging to one vessel in low contrast areas. We improve the forest by applying simple geometric filters that trim short branches, fill gaps in blood vessels and remove spurious branches from the vascular tree to be extracted. Experiments show that our technique can be applied to extract coronary trees from heart CT scans.

Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning

Endovascular treatment plays an important role in the minimally invasive treatment of patients with vascular diseases, a major cause of morbidity and mortality worldwide. Given a segmentation of an angiography, quantitative analysis of abnormal structures can aid radiologists in choosing appropriate treatments and apparatuses. However, effective quantitation is only attainable if the abnormalities are identified from the vasculature. To achieve this, a novel method is developed, which works on the simpler shape of normal vessels to identify different vascular abnormalities (viz. stenotic atherosclerotic plaque, and saccular and fusiform aneurysmal lumens) in an indirect fashion, instead of directly manipulating the complex-shaped abnormalities. The proposed method has been tested on three synthetic and 17 clinical data sets. Comparisons with two related works are also conducted. Experimental results show that our method can produce satisfactory identification of the abnormalities and approximations of the ideal post-treatment vessel lumens. In addition, it can help increase the repeatability of the measurement of clinical parameters significantly.

Magnetic resonance angiography: From anatomical knowledge modeling to vessel segmentation

Magnetic resonance angiography (MRA) has become a common way to study cerebral vascular structures. Indeed, it enables to obtain information on flowing blood in a totally non-invasive and non-irradiant fashion. MRA exams are generally performed for three main applications: detection of vascular pathologies, neurosurgery planning, and vascular landmark detection for brain functional analysis. This large field of applications justifies the necessity to provide efficient vessel segmentation tools. Several methods have been proposed during the last fifteen years. However, the obtained results are still not fully satisfying. A solution to improve brain vessel segmentation from MRA data could consist in integrating high-level a priori knowledge in the segmentation process. A preliminary attempt to integrate such knowledge is proposed here. It is composed of two methods devoted to phase contrast MRA (PC MRA) data. The first method is a cerebral vascular atlas creation process, composed of three steps: knowledge extraction, registration, and data fusion. Knowledge extraction is performed using a vessel size determination algorithm based on skeletonization, while a topology preserving non-rigid registration method is used to fuse the information into the atlas. The second method is a segmentation process involving adaptive sets of gray-level hit-or-miss operators. It uses anatomical knowledge modeled by the cerebral vascular atlas to adapt the parameters of these operators (number, size, and orientation) to the searched vascular structures. These two methods have been tested by creating an atlas from a 18 MRA database, and by using it to segment 30 MRA images, comparing the results to those obtained from a region-growing segmentation method.

Region-growing segmentation of brain vessels: an atlas-based automatic approach

PURPOSE

To propose an atlas-based method that uses both phase and magnitude images to integrate anatomical information in order to improve the segmentation of blood vessels in cerebral phase-contrast magnetic resonance angiography (PC-MRA).

MATERIAL AND METHODS

An atlas of the whole head was developed to store the anatomical information. The atlas divides a magnitude image into several vascular areas, each of which has specific vessel properties. It can be applied to any magnitude image of an entire or nearly entire head by deformable matching, which helps to segment blood vessels from the associated phase image. The segmentation method used afterwards consists of a topology-preserving, region-growing algorithm that uses adaptive threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels that are selected according to their grayscale value and the variation of values in their neighborhood. The topology preservation is guaranteed because only simple points are selected during the growing process.

RESULTS

The method was performed on 40 PC-MRA images of the brain. The results were validated using maximum-intensity projection (MIP) and three-dimensional surface rendering visualization, and compared with results obtained with two non-atlas-based methods.

CONCLUSION

The results show that the proposed method significantly improves the segmentation of cerebral vascular structures from PC-MRA. These experiments tend to prove that the use of vascular atlases is an effective way to optimize vessel segmentation of cerebral images.

Vessel tree reconstruction in thoracic CT scans with application to nodule detection

Vessel tree reconstruction in volumetric data is a necessary prerequisite in various medical imaging applications. Specifically, when considering the application of automated lung nodule detection in thoracic computed tomography (CT) scans, vessel trees can be used to resolve local ambiguities based on global considerations and so improve the performance of nodule detection algorithms. In this study, a novel approach to vessel tree reconstruction and its application to nodule detection in thoracic CT scans was developed by using correlation-based enhancement filters and a fuzzy shape representation of the data. The proposed correlation-based enhancement filters depend on first-order partial derivatives and so are less sensitive to noise compared with Hessian-based filters. Additionally, multiple sets of eigenvalues are used so that a distinction between nodules and vessel junctions becomes possible. The proposed fuzzy shape representation is based on regulated morphological operations that are less sensitive to noise. Consequently, the vessel tree reconstruction algorithm can accommodate vessel bifurcation and discontinuities. A quantitative performance evaluation of the enhancement filters and of the vessel tree reconstruction algorithm was performed. Moreover, the proposed vessel tree reconstruction algorithm reduced the number of false positives generated by an existing nodule detection algorithm by 38%.

A relational-tubular (ReTu) deformable model for vasculature quantification of zebrafish embryo from microangiography image series

Embryonic cardiovascular system plays a vital role in embryonic development of human and animal. In this work, we introduce a novel deformable model, which we called Relational-tubular (ReTu) deformable model for segmenting and quantifying the embryonic vasculature of zebrafish embryo from microangiography image series. Particularly, to incorporate additional constraints on the spatial relationships among vessel branches, we introduce a new energy term called relation energy into the model energy function. This energy item acts as a repulsion force between neighboring vessels during the deformation to encourage them to move towards their respective volume data. Using the ReTu deformable model, the deformation process is an iterative two-stage procedure: vascular axis deformation and vascular surface deformation. The efficiency and robustness of this approach are demonstrated by experiments which show that satisfactory quantifications of the vasculature can be obtained after 3-4 iterations.

Quantifying 3-D vascular structures in MRA images using hybrid PDE and geometric deformable models

The aim of this paper is to present a hybrid approach to accurate quantification of vascular structures from magnetic resonance angiography (MRA) images using level set methods and deformable geometric models constructed with 3-D Delaunay triangulation. Multiple scale filtering based on the analysis of local intensity structure using the Hessian matrix is used to effectively enhance vessel structures with various diameters. The level set method is then applied to automatically segment vessels enhanced by the filtering with a speed function derived from enhanced MRA images. Since the goal of this paper is to obtain highly accurate vessel borders, suitable for use in fluid flow simulations, in a subsequent step, the vessel surface determined by the level set method is triangulated using 3-D Delaunay triangulation and the resulting surface is used as a parametric deformable model. Energy minimization is then performed within a variational setting with a first-order internal energy; the external energy is derived from 3-D image gradients. Using the proposed method, vessels are accurately segmented from MRA data.

Reconstruction and quantification of the carotid artery bifurcation from 3-D ultrasound images

Three-dimensional (3-D) ultrasound is a relatively new technique, which is well suited to imaging superficial blood vessels, and potentially provides a useful, noninvasive method for generating anatomically realistic 3-D models of the peripheral vasculature. Such models are essential for accurate simulation of blood flow using computational fluid dynamics (CFD), but may also be used to quantify atherosclerotic plaque more comprehensively than routine clinical methods. In this paper, we present a spline-based method for reconstructing the normal and diseased carotid artery bifurcation from images acquired using a freehand 3-D ultrasound system. The vessel wall (intima-media interface) and lumen surfaces are represented by a geometric model defined using smoothing splines. Using this coupled wall-lumen model, we demonstrate how plaque may be analyzed automatically to provide a comprehensive set of quantitative measures of size and shape, including established clinical measures, such as degree of (diameter) stenosis. The geometric accuracy of 3-D ultrasound reconstruction is assessed using pulsatile phantoms of the carotid bifurcation, and we conclude by demonstrating the in vivo application of the algorithms outlined to 3-D ultrasound scans from a series of patient carotid arteries.

Estimation of the differential pressure at renal artery stenoses

Atherosclerotic disease of the renal artery can lead to reduction in arterial caliber and ultimately to conditions including renovascular hypertension. Renal artery stenosis is conventionally assessed, using angiography, according to the severity of the stenosis. However, the severity of a stenosis is not a reliable indicator of functional significance, or associated differential pressure, of a stenosis. A methodology is proposed for estimation of the renal artery differential pressure (RADP) from MR imaging. Realistic computational fluid dynamics (CFD) models are constructed from MR angiography (MRA) and phase-contrast (PC) MR. The CFD model is constructed in a semiautomated manner from the MR images using the Isosurface Deformable Model (IDM) for surface reconstruction and a Marching Front algorithm for construction of the volumetric CFD mesh. Validation of RADP estimation was performed in a realistic physical flow-through model. Under steady flow, the CFD estimate of the differential pressure across a stenosis in the physical flow-through model differed by an average of 5.5 mmHg from transducer measurements of the pressure differential, for differential pressures less than 60 mmHg. These results demonstrate that accurate estimates of differential pressure at stenoses may be possible based only on structural and flow images.

Visualisation of 4-D colour and power Doppler data

Mechanically scanned, cardiac-gated, 4-D colour Doppler and power Doppler data were acquired and rendered using multiplanar slice display, surface-fitting (S-F) and direct volume rendering (DVR), with interactive viewpoint control or tool command language (Tcl)-scripted generation of animation sequences across the cardiac cycle. S-F applied the marching cubes algorithm within a YCbCr colour space to extract time-varying Doppler regions; supplementally generated surfaces enclosed all regions across the full cardiac cycle, partially to alleviate the problem of incomplete vessel filling. DVR used integrated intensity, maximum intensity and front-to-back projection; the latter applied an opacity function to the Cb, Cr and Y colour channels to control the appearance of the blood flow signal relative to the background B-mode image. Our most detailed display was a hybrid approach that combined multiplanar slicing and DVR.

Isosurfaces as deformable models for magnetic resonance angiography

Vascular disease produces changes in lumenal shape evident in magnetic resonance angiography (MRA). However, quantification of vascular shape from MRA is problematic due to image artifacts. Prior deformable models for vascular surface reconstruction primarily resolve problems of initialization of the surface mesh. However, initialization can be obtained in a trivial manner for MRA using isosurfaces. We propose a methodology for deforming the isosurface to conform to the boundaries of objects in the image with minimal a priori assumptions of object shape. As in conventional methods, external forces attract the surface toward edges in the image. However, smoothing is produced by a moment that aligns the normals of adjacent surface triangles. Notably, the moment produces no translational motion of surface triangles. The deformable isosurface was applied to a digital phantom of a stenotic artery, to MRA of three renal arteries with atherosclerotic disease and MRA of one carotid artery with atherosclerotic disease. Results of the surface reconstruction from the deformable model were compared with conventional X-ray angiography for the renal arteries. Measurement of the degree of stenosis of the renal arteries was within 12% +/- 6%. The deformable model provided improvements over the isosurface in all cases in terms of measurement of the degree of stenosis or improving the surface smoothness.

Estimation of bolus dispersion effects in perfusion MRI using image-based computational fluid dynamics

Bolus tracking magnetic resonance imaging (MRI) is a powerful technique for measuring perfusion, and is playing an increasing role in the investigation of acute stroke. However, limitations have been reported when assessing patients with steno-occlusive disease. The presence of a steno-occlusive disease in the artery may cause bolus dispersion, which has been shown to introduce significant errors in cerebral blood flow (CBF) quantification. Bolus dispersion is commonly described by a vascular transport function, but the function that properly characterizes the dispersion is unknown. A novel method to quantify bolus dispersion errors on perfusion measurements is presented. A realistic patient-specific model is constructed from anatomical and physiologic MR data, and the arterial blood flow pattern and the transport of the bolus of contrast agent are computed using finite element analysis. The methodology presented was used also to evaluate the accuracy of three simple vascular models. The methodology was tested on MR data from two normal subjects and two subjects with mild carotid artery stenosis. The estimated CBF errors were of the order of 15% to 20%. However, the presence of stenosis did not necessarily introduce larger dispersion (not only the geometrical model but also the particular physiologic conditions influence the degree of bolus dispersion). The method described will contribute to a better understanding of errors introduced by dispersion effects, to the assessment and validation of vascular models, and to the development of new methods for the correction of dispersion errors in CBF quantification.

Computational geometry for patient-specific reconstruction and meshing of blood vessels from MR and CT angiography

Investigation of three-dimensional (3-D) geometry and fluid-dynamics in human arteries is an important issue in vascular disease characterization and assessment. Thanks to recent advances in magnetic resonance (MR) and computed tomography (CT), it is now possible to address the problem of patient-specific modeling of blood vessels, in order to take into account interindividual anatomic variability of vasculature. Generation of models suitable for computational fluid dynamics is still commonly performed by semiautomatic procedures, in general based on operator-dependent tasks, which cannot be easily extended to a significant number of clinical cases. In this paper, we overcome these limitations making use of computational geometry techniques. In particular, 3-D modeling was carried out by means of 3-D level sets approach. Model editing was also implemented ensuring harmonic mean curvature vectors distribution on the surface, and model geometric analysis was performed with a novel approach, based on solving Eikonal equation on Voronoi diagram. This approach provides calculation of central paths, maximum inscribed sphere estimation and geometric characterization of the surface. Generation of adaptive-thickness boundary layer finite elements is finally presented. The use of the techniques presented here makes it possible to introduce patient-specific modeling of blood vessels at clinical level.

Blood flow modeling in carotid arteries with computational fluid dynamics and MR imaging

RATIONALE AND OBJECTIVES

The authors' goal was to develop a noninvasive method for detailed assessment of blood flow patterns from direct in vivo measurements of vessel anatomy and flow rates.

MATERIALS AND METHODS

The authors developed a method to construct realistic patient-specific finite element models of blood flow in carotid arteries. Anatomic models are reconstructed from contrast material-enhanced magnetic resonance (MR) angiographic images with a tubular deformable model along each arterial branch. A surface-merging algorithm is used to create a watertight model of the carotid bifurcation for subsequent finite element grid generation, and a fully implicit scheme is used to solve the incompressible Navier-Stokes equations on unstructured grids. Physiologic boundary conditions are derived from cine phase-contrast MR flow velocity measurements at two locations below and above the bifurcation. Vessel wall compliance is incorporated by means of fluid-solid interaction algorithms.

RESULTS

The method was tested on imaging data from a healthy subject and a patient with mild stenosis. Finite element grids were successfully generated, and pulsatile blood flow calculations were performed. Computed and measured velocity profiles show good agreement. Flow patterns and wall shear stress distributions were visualized.

CONCLUSIONS

Patient-specific computational fluid dynamics modeling based on MR images can be performed robustly and efficiently. Preliminary validation studies in a physical flow-through model suggest that the model is accurate. This method can be used to characterize blood flow patterns in healthy and diseased arteries and may eventually help physicians to supplement imaging-based diagnosis and predict and evaluate the outcome of interventional procedures.