Background In vivo characterization of mitral valve dynamics relies on image analysis algorithms that accurately reconstruct valve morphology and motion from clinical images. The goal of such algorithms is to provide ...
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Background In vivo characterization of mitral valve dynamics relies on image analysis algorithms that accurately reconstruct valve morphology and motion from clinical images. The goal of such algorithms is to provide patient-specific descriptions of both competent and regurgitant mitral valves, which can be used as input to biomechanical analyses and provide insights into the pathophysiology of diseases like ischemic mitral regurgitation (IMR). Objective The goal is to generate accurate image-based representations of valve dynamics that visually and quantitatively capture normal and pathological valve function. Methods We present a novel framework for 4d segmentation and geometric modeling of the mitral valve in real-time 3d echocardiography (rt-3dE), an imaging modality used for pre-operative surgical planning of mitral interventions. The framework integrates groupwise multi-atlas label fusion and template-based medial modeling with Kalman filtering to generate quantitatively descriptive and temporally consistent models of valve dynamics. Results The algorithm is evaluated on rt-3dE data series from 28 patients: 14 with normal mitral valve morphology and 14 with severe IMR. In these 28 data series that total 613 individual 3dE images, each 3d mitral valve segmentation is validated against manual tracing, and temporal consistency between segmentations is demonstrated. Conclusions Automated4d image analysis allows for reliable non-invasive modeling of the mitral valve over the cardiac cycle for comparison of annular and leaflet dynamics in pathological and normal mitral valves. Future studies can apply this algorithm to cardiovascular mechanics applications, including patient-specific strain estimation, fluiddynamics simulation, inverse finite element analysis, and risk stratification for surgical treatment.
4d PC-MRI enables the noninvasive measurement of time-resolved, three-dimensional blood flow data that allow quantification of the hemodynamics. Stroke volumes are essential to assess the cardiac function and evolutio...
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4d PC-MRI enables the noninvasive measurement of time-resolved, three-dimensional blood flow data that allow quantification of the hemodynamics. Stroke volumes are essential to assess the cardiac function and evolution of different cardiovascular diseases. The calculation depends on the wall position and vessel orientation, which both change during the cardiac cycle due to the heart muscle contraction and the pumped blood. However, current systems for the quantitative 4d PC-MRI data analysis neglect the dynamic character and instead employ a static 3d vessel approximation. We quantify differences between stroke volumes in the aorta obtained with and without consideration of its dynamics. We describe a method that uses the approximating 3dsegmentation to automatically initialize segmentation algorithms that require regions inside and outside the vessel for each temporal position. This enables the use of graph cuts to obtain 4d segmentations, extract vessel surfaces including centerlines for each temporal position andderive motion information. The stroke volume quantification is compared using measuring planes in static (3d) vessels, planes with fixed angulation inside dynamic vessels (this corresponds to the common 2d PC-MRI) and moving planes inside dynamic vessels. Seven datasets with different pathologies such as aneurysms and coarctations were evaluated in close collaboration with radiologists. Compared to the experts' manual stroke volume estimations, motion-aware quantification performs, on average, 1.57 % better than calculations without motion consideration. The mean difference between stroke volumes obtained with the different methods is 7.82 %. Automatically obtained4d segmentations overlap by 85.75 % with manually generated ones. Incorporating motion information in the stroke volume quantification yields slight but not statistically significant improvements. The presented method is feasible for the clinical routine, since computation times are low and e
This paper proposes a new energy minimization method called multiplicative intrinsic component optimization (MICO) for joint bias field estimation andsegmentation of magnetic resonance (MR) images. The proposed metho...
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This paper proposes a new energy minimization method called multiplicative intrinsic component optimization (MICO) for joint bias field estimation andsegmentation of magnetic resonance (MR) images. The proposed method takes full advantage of the decomposition of MR images into two multiplicative components, namely, the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity, and their respective spatial properties. Bias field estimation and tissue segmentation are simultaneously achieved by an energy minimization process aimed to optimize the estimates of the two multiplicative components of an MR image. The bias field is iteratively optimized by using efficient matrix computations, which are verified to be numerically stable by matrix analysis. More importantly, the energy in our formulation is convex in each of its variables, which leads to the robustness of the proposed energy minimization algorithm. The MICO formulation can be naturally extended to 3d/4d tissue segmentation with spatial/sptatiotemporal regularization. Quantitative evaluations and comparisons with some popular softwares have demonstrated superior performance of MICO in terms of robustness and accuracy. (C) 2014 Published by Elsevier Inc.
We present an efficient and scalable algorithm for segmenting 3d RGBd point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach. Our algorithm processes a m...
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ISBN:
(纸本)9781479951178
We present an efficient and scalable algorithm for segmenting 3d RGBd point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach. Our algorithm processes a moving window over several point clouds to group similar regions over a graph, resulting in an initial over-segmentation. These regions are then merged to yield a dendrogram using agglomerative clustering via a minimum spanning tree algorithm. Bipartite graph matching at a given level of the hierarchical tree yields the final segmentation of the point clouds by maintaining region identities over arbitrarily long periods of time. We show that a multistage segmentation with depth then color yields better results than a linear combination of depth and color. due to its incremental processing, our algorithm can process videos of any length and in a streaming pipeline. The algorithm's ability to produce robust, efficient segmentation is demonstrated with numerous experimental results on challenging sequences from our own as well as public RGBddata sets.
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