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Sundar H, Litt H, Shen D. Estimating Myocardial Motion by 4D Image Warping. Pattern Recognit. 2009;42(11):2514-2526doi: 10.1016/j.patcog.2009.04.022.
Sundar, H., Litt, H., & Shen, D. (2009). Estimating Myocardial Motion by 4D Image Warping. Pattern recognition, 42(11), 2514-2526. https://doi.org/10.1016/j.patcog.2009.04.022
Sundar, Hari, et al. "Estimating Myocardial Motion by 4D Image Warping." Pattern recognition vol. 42,11 (2009): 2514-2526. doi: https://doi.org/10.1016/j.patcog.2009.04.022
Sundar H, Litt H, Shen D. Estimating Myocardial Motion by 4D Image Warping. Pattern Recognit. 2009 Nov 01;42(11):2514-2526. doi: 10.1016/j.patcog.2009.04.022. PMID: 20379351; PMCID: PMC2849737.
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Pattern Recognit. 2009 Nov 01;42(11):2514-2526. doi: 10.1016/j.patcog.2009.04.022.

Estimating Myocardial Motion by 4D Image Warping.

Pattern recognition

Hari Sundar, Harold Litt, Dinggang Shen

Affiliations

  1. Section for Biomedical Image Analysis, University of Pennsylvania School of Medicine, Philadelphia, PA.

PMID: 20379351 PMCID: PMC2849737 DOI: 10.1016/j.patcog.2009.04.022

Abstract

A method for spatio-temporally smooth and consistent estimation of cardiac motion from MR cine sequences is proposed. Myocardial motion is estimated within a 4-dimensional (4D) registration framework, in which all 3D images obtained at different cardiac phases are simultaneously registered. This facilitates spatio-temporally consistent estimation of motion as opposed to other registration-based algorithms which estimate the motion by sequentially registering one frame to another. To facilitate image matching, an attribute vector (AV) is constructed for each point in the image, and is intended to serve as a "morphological signature" of that point. The AV includes intensity, boundary, and geometric moment invariants (GMIs). Hierarchical registration of two image sequences is achieved by using the most distinctive points for initial registration of two sequences and gradually adding less-distinctive points to refine the registration. Experimental results on real data demonstrate good performance of the proposed method for cardiac image registration and motion estimation. The motion estimation is validated via comparisons with motion estimates obtained from MR images with myocardial tagging.

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