21 March 2016 Contour tracking and probabilistic segmentation of tissue phase mapping MRI
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Abstract
Many cardiovascular diseases manifest as an abnormal motion pattern of the heart muscle (myocardium). Local cardiac motion can be non-invasively quantified with magnetic resonance imaging (MRI), using methods such as tissue phase mapping (TPM), which directly measures the local myocardial velocities over time with high temporal and spatial resolution. The challenges for routine clinical use of TPM for the diagnosis and monitoring of cardiac function lie in providing a fast and accurate myocardium segmentation and a robust quantitative analysis of the velocity field. Both of these tasks are difficult to automate on routine clinical data because of the reduced contrast in the presence of noise. In this work, we propose to address these challenges with a segmentation approach that combines smooth, iterative contour displacement and probabilistic segmentation using particle tracing, based on the underlying velocity field. The proposed solution enabled the efficient and reproducible segmentation of TPM datasets from 27 patients and 14 volunteers, showing good potential for routine use in clinical studies. Our method allows for a more reliable quantitative analysis of local myocardial velocities, by giving a higher weight to velocity vectors corresponding to pixels more likely to belong to the myocardium. The accuracy of the contour propagation was evaluated on nine subjects, showing an average error smaller than the spatial resolution of the image data. Statistical analysis concluded that the difference between the segmented contours and the ground truths was not significantly higher than the variability between the manual ground truth segmentations.
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Teodora Chitiboi, Anja Hennemuth, Susanne Schnell, Varun Chowdhary, Amir Honarmand, Michael Markl, Lars Linsen, Horst Hahn, "Contour tracking and probabilistic segmentation of tissue phase mapping MRI", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978404 (21 March 2016); doi: 10.1117/12.2216921; https://doi.org/10.1117/12.2216921
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