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Biomedical subjects

Milan Sonka

Publications and source records attributed to Milan Sonka.

At least 19 recordsLinked to original sources

Plaque development, vessel curvature, and wall shear stress in coronary arteries assessed by X-ray angiography and intravascular ultrasound.

The relationships among vascular geometry, hemodynamics, and plaque development in the coronary arteries are complex and not yet well understood. This paper reports a methodology for the quantitative analysis of in vivo coronary morphology and hemodynamics, with particular emphasis placed on the critical issues of image segmentation and the automated classification of disease severity. We were motivated by the observation that plaque more often developed at the inner curvature of a vessel, presumably due to the relatively lower wall shear stress at these locations. The presented studies are based on our validated methodology for the three-dimensional fusion of intravascular ultrasound (IVUS) and X-ray angiography, introducing a novel approach for IVUS segmentation that incorporates a robust, knowledge-based cost function and a fully optimal, three-dimensional segmentation algorithm. Our first study shows that circumferential plaque distribution depends on local vessel curvature in the majority of vessels. The second study analyzes the correlation between plaque distribution and wall shear stress in a set of 48 in vivo vessel segments. The results were conclusive for both studies, with a stronger correlation of circumferential plaque thickness with local curvature than with wall shear stress. The inverse relationship between local wall shear stress and plaque thickness was significantly more pronounced (p<0.025) in vessel cross sections exhibiting compensatory enlargement (positive remodeling) without luminal narrowing than when the full spectrum of disease severity was considered. The inverse relationship was no longer observed in vessels where less than 35% of vessel cross sections remained without luminal narrowing. The findings of this study confirm, in vivo, the hypothesis that relatively lower wall shear stress is associated with early plaque development.

Computer Simulation↗

MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies.

Our goal is to enhance the ability to differentiate normal lung from subtle pathologies via multidetector row CT (MDCT) by extending a two-dimensional (2-D) texturebased tissue classification [adaptive multiple feature method (AMFM)] to use three-dimensional (3-D) texture features. We performed MDCT on 34 humans and classified volumes of interest (VOIs) in the MDCT images into five categories: EC, emphysema in severe chronic obstructive pulmonary disease (COPD); MC, mild emphysema in mild COPD; NC, normal appearing lung in mild COPD; NN, normal appearing lung in normal nonsmokers; and NS, normal appearing lung in normal smokers. COPD severity was based upon pulmonary function tests (PFTs). Airways and vessels were excluded from VOIs; 24 3-D texture features were calculated; and a Bayesian classifier was used for discrimination. A leave-one-out method was employed for validation. Sensitivity of the four-class classification in the form of 3-D/2-D was: EC: 85%/71%, MC: 90%/82%; NC: 88%/50%; NN: 100%/60%. Sensitivity and specificity for NN using a two-class classification of NN and NS in the form of 3-D/2-D were: 99%/72% and 100%/75%, respectively. We conclude that 3-D AMFM analysis of lung parenchyma improves discrimination compared to 2-D AMFM of the same VOIs. Furthermore, our results suggest that the 3-D AMFM may provide a means of discriminating subtle differences between smokers and nonsmokers both with normal PFTs.

Algorithms↗

Automated contour detection in X-ray left ventricular angiograms using multiview active appearance models and dynamic programming.

This paper describes a new approach to the automated segmentation of X-ray left ventricular (LV) angiograms, based on active appearance models (AAMs) and dynamic programming. A coupling of shape and texture information between the end-diastolic (ED) and end-systolic (ES) frame was achieved by constructing a multiview AAM. Over-constraining of the model was compensated for by employing dynamic programming, integrating both intensity and motion features in the cost function. Two applications are compared: a semi-automatic method with manual model initialization, and a fully automatic algorithm. The first proved to be highly robust and accurate, demonstrating high clinical relevance. Based on experiments involving 70 patient data sets, the algorithm's success rate was 100% for ED and 99% for ES, with average unsigned border positioning errors of 0.68 mm for ED and 1.45 mm for ES. Calculated volumes were accurate and unbiased. The fully automatic algorithm, with intrinsically less user interaction was less robust, but showed a high potential, mostly due to a controlled gradient descent in updating the model parameters. The success rate of the fully automatic method was 91% for ED and 83% for ES, with average unsigned border positioning errors of 0.79 mm for ED and 1.55 mm for ES.

Algorithms↗

Optimal surface segmentation in volumetric images--a graph-theoretic approach.

Efficient segmentation of globally optimal surfaces representing object boundaries in volumetric data sets is important and challenging in many medical image analysis applications. We have developed an optimal surface detection method capable of simultaneously detecting multiple interacting surfaces, in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations. The method solves the surface segmentation problem by transforming it into computing a minimum s-t cut in a derived arc-weighted directed graph. The proposed algorithm has a low-order polynomial time complexity and is computationally efficient. It has been extensively validated on more than 300 computer-synthetic volumetric images, 72 CT-scanned data sets of different-sized plexiglas tubes, and tens of medical images spanning various imaging modalities. In all cases, the approach yielded highly accurate results. Our approach can be readily extended to higher-dimensional image segmentation.

Algorithms↗

Coronary arteries: imaging, reconstruction, and fluid dynamic analysis.

Atherosclerosis is the underlying cause of most cardiovascular-related deaths in industrialized nations. Determining the etiology of atherosclerosis and detecting lesions in the early stages of the disease for possible pharmacological or mechanical intervention have been challenges facing cardiovascular researchers. In addition to genetic and environmental factors, the formation and growth of atheroma have been linked to the complex fluid dynamics and mass transport in these arterial segments. This article reviews the current state of affairs in imaging modalities and image processing techniques that allow the visualization and morphologically realistic reconstruction of coronary arterial geometry to aid in the diagnosis and treatment of coronary artery disease (CAD). In addition, studies pertaining to our current understanding of the complex flow dynamics in the coronary arteries and the relationship between fluid-induced stresses on the initiation and growth of the atherosclerotic lesions are also reviewed. The article concludes with a brief discussion on possible future directions of research that will advance our knowledge of this challenging problem.

Animals↗

Remodeling characteristics of minimally diseased coronary arteries are consistent along the length of the artery.

Using a method that creates anatomically correct, 3-dimensional arterial reconstructions, 55 minimally diseased coronary arteries from 40 patients were studied. Homogenous remodeling characteristics along the entire length of the artery were observed in 48 arteries (87%). In the aggregate, arteries exhibited compensatory expansive remodeling. Individually, the full spectrum of compensatory expansive remodeling (60%), excessive expansive remodeling (21%), and constrictive remodeling (19%) was observed across arteries. Each artery was consistent in its remodeling characteristics from proximal to distal portions of the artery, and the remodeling pattern of each artery was independent within the same patient.

Adult↗

Quantitative analysis of pulmonary airway tree structures.

A method for computationally efficient skeletonization of three-dimensional tubular structures is reported. The method is specifically targeting skeletonization of vascular and airway tree structures in medical images but it is general and applicable to many other skeletonization tasks. The developed approach builds on the following novel concepts and properties: fast curve-thinning algorithm to increase computational speed, endpoint re-checking to avoid generation of spurious side branches, depth-and-length sensitive pruning, and exact tree-branch partitioning allowing branch volume and surface measurements. The method was validated in computer and physical phantoms and in vivo CT scans of human lungs. The validation studies demonstrated sub-voxel accuracy of branch point positioning, insensitivity to changes of object orientation, and high reproducibility of derived quantitative indices of the tubular structures offering a significant improvement over previously reported methods (p<<0.001).

Algorithms↗

Cell segmentation, tracking, and mitosis detection using temporal context.

The Large Scale Digital Cell Analysis System (LSDCAS) developed at the University of Iowa provides capabilities for extended-time live cell image acquisition. This paper presents a new approach to quantitative analysis of live cell image data. By using time as an extra dimension, level set methods are employed to determine cell trajectories from 2D + time data sets. When identifying the cell trajectories, cell cluster separation and mitotic cell detection steps are performed. Each of the trajectories corresponds to the motion pattern of an individual cell in the data set. At each time frame, number of cells, cell locations, cell borders, cell areas, and cell states are determined and recorded. The proposed method can help solving cell analysis problems of general importance including cell pedigree analysis and cell tracking. The developed method was tested on cancer cell image sequences and its performance compared with manually-defined ground truth. The similarity Kappa Index is 0.84 for segmentation area and the signed border positioning segmentation error is 1.6 +/- 2.1 microm.

Algorithms↗

Computer-aided diagnosis via model-based shape analysis: automated classification of wall motion abnormalities in echocardiograms.

RATIONALE AND OBJECTIVE: Shape analysis of endocardial contour sequences from echocardiograms can provide classification of wall motion abnormalities (WMA). MATERIALS AND METHODS: We previously reported on active appearance motion models (AAMM) for automated detection of endocardial contours in sequences of echocardiograms. The shape analysis of AAMM renders eigenvariations of shape/motion, including typical normal and pathologic endocardial contraction patterns. A set of stress echocardiograms (single-beat four-chamber and two-chamber sequences with expert-verified endocardial contours) of 129 infarct patients was split randomly into training (n = 65) and testing (n = 64) sets. AAMMs were generated from the training set and AAMM shape coefficients (ASCs) were extracted for all sequences and statistically related to regional/global visual wall motion scoring (VWMS) and volumetric parameters. RESULTS: Linear regression showed clear correlations between ASCs and VWMS. Discriminant analysis showed good prediction by ASCs of both segmental (74% correctness) and global WMA (90% correctness). Volumetric parameters correlated poorly to regional VWMS. CONCLUSION: 1) ASCs show promising accuracy for automated WMA classification. 2) VWMS and endocardial border motion are closely related; with accurate automated border detection, automated WMA classification should be feasible. 3) ASC shape analysis allows contour set evaluation by direct comparison to clinical parameters.

Cardiac Volume↗

A real-time 3-dimensional digital Doppler method for measurement of flow rate and volume through mitral valve in children: a validation study compared with magnetic resonance imaging.

We developed and assessed a real-time 3-dimensional (3D) digital Doppler method for measurement of flow volumes through the mitral valve in children. A total of 13 children (aged 10.46 +/- 2.5 years; 8 boys/5 girls) were enrolled. An ultrasound system (Sonos 7500, Philips, Andover, Mass) was used to acquire raw 3D velocity data for flow measurement based on Gaussian control surface theorem [flow (mL/s) = mean velocity x flow area]. Stroke volume (SV) measured by real-time 3D digital Doppler with the control surface at the mitral valve annulus or orifice was compared with the SV by phase velocity cine magnetic resonance imaging (MRI) at the ascending aorta and by left ventricular volumetric MRI measurement. The best correlation and agreement were seen at the mitral valve orifice by real-time 3D digital Doppler compared with SV by phase velocity cine MRI at the ascending aorta (r = 0.92, mean difference = -5.2 +/- 12.0 mL) and SV by left ventricular volumetric MRI measurement (r = 0.94, mean difference = -0.2 +/- 10.3 mL).

Adolescent↗

Rapid full volume data acquisition by real-time 3-dimensional echocardiography for assessment of left ventricular indexes in children: a validation study compared with magnetic resonance imaging.

OBJECTIVE: We sought to assess the feasibility, accuracy, and reproducibility of a rapid full volume acquisition strategy using real-time (RT) 3-dimensional (3D) echocardiography (3DE) for measurement of left ventricular (LV) volumes, mass, stroke volume (SV), and ejection fraction (EF) in children. METHODS: A total of 19 healthy children (mean 10.6 +/- 2.8 years, 11 male and 9 female) were prospectively enrolled in this study. RT 3DE was performed using an ultrasound system to acquire full volume 3D dataset from the apical window with electrocardiographic triggering in 8 s/dataset. The images were processed offline using software. The LV endocardial and epicardial borders were traced manually to derive LV end-systolic volume, end-diastolic volume, mass, SV, and EF. Magnetic resonance imaging (MRI) studies were performed on a 1.5-T scanner using a breath hold 2-dimensional cine-FIESTA (fast imaging employing steady-state acquisition) sequence. RESULTS: All RT 3DE and MRI data were acquired successfully for analysis. Measurements of LV end-systolic volume, end-diastolic volume, mass, SV, and EF by RT 3DE correlated well by Pearson regression ( r = 0.86-0.97, P < .001) and agreed well by Bland-Altman analysis with MRI. The interobserver and intraobserver variability of RT 3DE measurements were less than 5%. CONCLUSIONS: This prospective study demonstrated that RT 3DE measurements of LV end-systolic volume, end-diastolic volume, mass, SV, and EF in children using rapid full volume acquisition strategy are feasible, accurate, and reproducible and are comparable with MRI measurements.

Adolescent↗

Multiview active appearance models for simultaneous segmentation of cardiac 2- and 4-chamber long-axis magnetic resonance images.

RATIONALE AND OBJECTIVES: Long-axis cardiac magnetic resonance (MR) views enable a rapid, online evaluation of cardiac function from only 2 views. In this article, we aimed to evaluate a model-based method for the simultaneous detection of 2- and 4-chamber endocardial and epicardial contours in end-diastolic and end-systolic phases of MR images. METHODS: We introduce multiview Active Appearance Models for the automated segmentation of long-axis cardiac MR images of the left ventricle. Two modes of initialization were used to test the accuracy of the model with minimal user interaction and the best-obtainable accuracy with this model. The segmentation was initialized by annotating 2 points in the base and one in the apex. We tested the method's performance by comparing the point-to-curve errors, ejection fractions, and biplane area-length volumes calculated with the automatically detected contours to those calculated from contours that were annotated manually by experts. Leave-one-out experiments were performed with 2- and 4-chamber long axis MR images of 59 subjects in end-diastolic and end-systolic phases. RESULTS: When initializing in all 4 frames, 97% of the segmentations were successful, and the standard deviation in the volume-errors with respect to the average manually identified volume was 9.0% for the end-diastolic volumes and 15% for the end-systolic volumes. When the method was initialized in the end-systolic frames only, 92% of the segmentations were successful, and the standard deviation in the errors in the volumes with respect to the average manually identified volume was 13.3% for the end-diastolic volumes and 16.7% for the end-systolic volumes. Bland-Altman plots showed that the errors were distributed randomly around 0, and by using a paired t test comparing manual and computer-determined volumes, we were able to detect that the volume differences were not significant. Simultaneous detection of the endocardial and epicardial contours in 2- and 4-chamber views and end-diastolic and end-systolic phases for one subject takes approximately 3 seconds. CONCLUSIONS: The accuracy of the reported method is comparable with the interobserver variability for initialization in all frames and slightly worse than the interobserver variability with initialization in the end-systolic frames only. However, in both cases the errors were not significant. Initialization in end-systolic frames only leads to a statistically insignificantly lower model accuracy; however, it requires only half the user interaction. Therefore, we can conclude that this method enables rapid analysis of the cardiac left ventricular function with little user interaction.

Diastole↗

Robust active appearance models and their application to medical image analysis.

Active appearance models (AAMs) have been successfully used for a variety of segmentation tasks in medical image analysis. However, gross disturbances of objects can occur in routine clinical setting caused by pathological changes or medical interventions. This poses a problem for AAM-based segmentation, since the method is inherently not robust. In this paper, a novel robust AAM (RAAM) matching algorithm is presented. Compared to previous approaches, no assumptions are made regarding the kind of gray-value disturbance and/or the expected magnitude of residuals during matching. The method consists of two main stages. First, initial residuals are analyzed by means of a mean-shift-based mode detection step. Second, an objective function is utilized for the selection of a mode combination not representing the gross outliers. We demonstrate the robustness of the method in a variety of examples with different noise conditions. The RAAM performance is quantitatively demonstrated in two substantially different applications, diaphragm segmentation and rheumatoid arthritis assessment. In all cases, the robust method shows an excellent behavior, with the new method tolerating up to 50% object area covered by gross gray-level disturbances.

Algorithms↗

Matching and anatomical labeling of human airway tree.

Matching of corresponding branchpoints between two human airway trees, as well as assigning anatomical names to the segments and branchpoints of the human airway tree, are of significant interest for clinical applications and physiological studies. In the past, these tasks were often performed manually due to the lack of automated algorithms that can tolerate false branches and anatomical variability typical for in vivo trees. In this paper, we present algorithms that perform both matching of branchpoints and anatomical labeling of in vivo trees without any human intervention and within a short computing time. No hand-pruning of false branches is required. The results from the automated methods show a high degree of accuracy when validated against reference data provided by human experts. 92.9% of the verifiable branchpoint matches found by the computer agree with experts' results. For anatomical labeling, 97.1% of the automatically assigned segment labels were found to be correct.

Algorithms↗

Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans.

The segmentation of the human airway tree from volumetric computed tomography (CT) images builds an important step for many clinical applications and for physiological studies. Previously proposed algorithms suffer from one or several problems: leaking into the surrounding lung parenchyma, the need for the user to manually adjust parameters, excessive runtime. Low-dose CT scans are increasingly utilized in lung screening studies, but segmenting them with traditional airway segmentation algorithms often yields less than satisfying results. In this paper, a new airway segmentation method based on fuzzy connectivity is presented. Small adaptive regions of interest are used that follow the airway branches as they are segmented. This has several advantages. It makes it possible to detect leaks early and avoid them, the segmentation algorithm can automatically adapt to changing image parameters, and the computing time is kept within moderate values. The new method is robust in the sense that it works on various types of scans (low-dose and regular dose, normal subjects and diseased subjects) without the need for the user to manually adjust any parameters. Comparison with a commonly used region-grow segmentation algorithm shows that the newly proposed method retrieves a significantly higher count of airway branches. A method that conducts accurate cross-sectional airway measurements on airways is presented as an additional processing step. Measurements are conducted in the original gray-level volume. Validation on a phantom shows that subvoxel accuracy is achieved for all airway sizes and airway orientations.

Algorithms↗

Segmentation and quantitative analysis of intrathoracic airway trees from computed tomography images.

The segmentation of the human airway tree from volumetric multidetector-row computed tomography images is an important prerequisite for many clinical applications and physiologic studies. We present a new airway segmentation method based on fuzzy connectivity. Small adaptive regions of interest are used that follow the airway branches as they are segmented. This method works on various types of scans (low dose and regular dose, normal subjects and diseased subjects) without the need for the user to manually adjust any parameters. Comparison with a commonly used region-growing segmentation algorithm shows that this method retrieves a significantly higher count of airway branches. In an additional processing step, this method provides accurate cross-sectional airway measurements that are conducted in the original gray-level volume. Validation on a phantom shows that subvoxel accuracy is achieved for all airway sizes and airway orientations. The utility of the reported method is demonstrated in a comparative analysis of normal and cystic fibrosis airway trees.

Algorithms↗

Coronary plaque analysis by multimodality fusion.

Imaging of the coronary arteries is usually performed by X-ray contrast angiography or intravascular ultrasound (IVUS). Angiography provides information about the vessel lumen and its geometry. IVUS offers more detailed information that also includes the vascular wall. The chapter describes these two imaging modalities and their geometrically correct fusion yielding a 3-D and/or 4-D representation of the coronary geometry and morphology. The image-derived information is used for assessment of coronary function and plaque severity, blood flow related indices are determined using computational fluid dynamics. Detailed description of the methodology is followed by validation and clinical studies.

Coronary Angiography↗