A near-incompressible poly-affine motion model for cardiac function analysis

Kristin McLeod, Christof Seiler, Maxime Sermesant, Xavier Pennec

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


Understanding the motion of the heart through the cardiac cycle can give useful insight for a range of different pathologies. In particular, quantifying regional cardiac motion can help clinicians to better determine cardiac function by identifying regions of thickened, ischemic or infarcted tissue. In this work we propose a method for cardiac motion analysis to track the deformation of the left ventricle at a regional level. This method estimates the affine motion of distinct regions of the myocardium using a near incompressible non-rigid registration algorithm based on the demon’s optical flow approach. The global motion over the ventricle is computed by a smooth fusion of the deformation in each segment using an anatomically aware poly-affine model for the heart. We apply the proposed method to a data-set of 10 volunteers. The results indicate that we are able to extract reasonably realistic deformation fields parametrised by a significantly reduced number of parameters compared to voxel-wise methods, which better enables for statistical analyses of the motion.keywordsmotion modelcardiac motionbinary maskjacobian determinantleft ventricle segmentationthese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
Subtitle of host publicationThird International Workshop, STACOM 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 5, 2012, Revised Selected Papers
Number of pages10
ISBN (Electronic)9783642369612
ISBN (Print)9783642369605
Publication statusPublished - 2012
Externally publishedYes

Publication series

SeriesLecture Notes in Computer Science


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