Scatter to volume registration for model-free respiratory motion estimation from dynamic MRIs

A new method is proposed to quantify the myocardial motion from both 2D C(ine)-MRI and T(agged)-MRI sequences. The tag pattern offers natural landmarks within the image that makes it possible to accurately quantify the motion within the myocardial wall. Therefore, several methods have been proposed for T-MRI. However, the lack of salient features within the cardiac wall in C-MRI hampers local motion estimation. Our method aims to ensure the local intensity and shape features invariance during motion through the iterative minimization of a cost function via a random walk scheme. The proposed approach is evaluated on realistic simulated C-MRI and T-MRI sequences. The results show more than 53% improvements on displacement estimation, and more than 24% on strain estimation for both C-MRI and T-MRI sequences, as compared to state-of-the-art cardiac motion estimators. Preliminary experiments on clinical data have shown a good ability of the proposed method to detect abnormal motion patterns related to pathology. If those results are confirmed on large databases, this would open up the possibility for more accurate diagnosis of cardiac function from standard C-MRI examinations and also the retrospective study of prior studies.

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A gradient-based optical-flow cardiac motion estimation method for cine and tagged MR images

Wang

Clarysse

Liu

et al. 2019

Medical Image Analysis

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Scatter to volume registration for model-free respiratory motion estimation from dynamic MRIs

Cited by 1 publication

References 26 publications

A gradient-based optical-flow cardiac motion estimation method for cine and tagged MR images

A gradient-based optical-flow cardiac motion estimation method for cine and tagged MR images

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