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Unsupervised estimation of myocardial displacement from tagged MR sequences using nonrigid registration
Author(s) -
LedesmaCarbayo Maria J.,
Derbyshire J. Andrew,
Sampath Smita,
Santos Andrés,
Desco Manuel,
McVeigh Elliot R.
Publication year - 2008
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.21444
Subject(s) - subpixel rendering , pixel , artificial intelligence , computer science , computer vision , segmentation , displacement (psychology) , robustness (evolution) , image registration , root mean square , smoothness , pattern recognition (psychology) , mathematics , image (mathematics) , physics , mathematical analysis , psychology , biochemistry , chemistry , quantum mechanics , psychotherapist , gene
We propose a fully automatic cardiac motion estimation technique that uses nonrigid registration between temporally adjacent images to compute the myocardial displacement field from tagged MR sequences using as inputs (sources) both horizontally and vertically tagged images. We present a new multisource nonrigid registration algorithm employing a semilocal deformation model that provides controlled smoothness. The method requires no segmentation. We apply a multiresolution optimization strategy for better speed and robustness. The accuracy of the algorithm is assessed on experimental data (animal model) and healthy volunteer data by calculating the root mean square (RMS) difference in position between the estimated tag trajectories and manual tracings outlined by an expert. For the ∼20000 tag lines analyzed (45 slices over 20–40 time frames), the RMS difference between the automatic tag trajectories and the manually segmented tag trajectories was 0.51 pixels (0.25 mm) for the animal data and 0.49 pixels (0.49 mm) for the human volunteer data. The RMS difference in the separation between adjacent tag lines (RMS_TS) was also assessed, resulting in an RMS_TS of 0.40 pixels (0.19 mm) in the experimental data and 0.52 pixels (0.56 mm) in the volunteer data. These results confirm the subpixel accuracy achieved using the proposed methodology. Magn Reson Med 2007. © 2007 Wiley‐Liss, Inc.