20 March 2015 Fast left ventricle tracking in CMR images using localized anatomical affine optical flow
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Abstract
In daily cardiology practice, assessment of left ventricular (LV) global function using non-invasive imaging remains central for the diagnosis and follow-up of patients with cardiovascular diseases. Despite the different methodologies currently accessible for LV segmentation in cardiac magnetic resonance (CMR) images, a fast and complete LV delineation is still limitedly available for routine use.

In this study, a localized anatomically constrained affine optical flow method is proposed for fast and automatic LV tracking throughout the full cardiac cycle in short-axis CMR images. Starting from an automatically delineated LV in the end-diastolic frame, the endocardial and epicardial boundaries are propagated by estimating the motion between adjacent cardiac phases using optical flow. In order to reduce the computational burden, the motion is only estimated in an anatomical region of interest around the tracked boundaries and subsequently integrated into a local affine motion model. Such localized estimation enables to capture complex motion patterns, while still being spatially consistent.

The method was validated on 45 CMR datasets taken from the 2009 MICCAI LV segmentation challenge. The proposed approach proved to be robust and efficient, with an average distance error of 2.1 mm and a correlation with reference ejection fraction of 0.98 (1.9 ± 4.5%). Moreover, it showed to be fast, taking 5 seconds for the tracking of a full 4D dataset (30 ms per image). Overall, a novel fast, robust and accurate LV tracking methodology was proposed, enabling accurate assessment of relevant global function cardiac indices, such as volumes and ejection fraction
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Sandro Queirós, João L. Vilaça, Pedro Morais, Jaime C. Fonseca, Jan D’hooge, Daniel Barbosa, "Fast left ventricle tracking in CMR images using localized anatomical affine optical flow", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941306 (20 March 2015); doi: 10.1117/12.2082017; https://doi.org/10.1117/12.2082017
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