Deformation Modelling Based on PLSR for Cardiac Magnetic Resonance Perfusion Imaging

Authors:
Jianxin Gao;Nick Ablitt;Andrew Elkington;Guang-Zhong Yang
Affiliations:
-;-;-;-
Venue:
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Year:
2002

Citing 1
Cited 1

Motion Decoupling and Registration for 3D Magnetic Resonance Myocardial Perfusion Imaging

ICCS '02 Proceedings of the International Conference on Computational Science-Part III

Fully automated registration of first-pass myocardial perfusion MRI using independent component analysis

IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging

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Abstract

This paper introduces a novel approach to deformation modelling based on partial-least-squares-regression and its application to the registration of first-pass magnetic resonance perfusion image sequences. The method relies on the extraction of intrinsic correlations between the latent factors of both the input and output signals for deriving a simple yet accurate deformation model. The strength of the technique has been demonstrated with both numerically simulated data sets and a myocardial perfusion study which involves nine patients with known coronary artery disease. The method represents a step forward for the commonly used principal component analysis for salient motion feature extraction. The proposed technique should be applicable to studies that involve deformation prediction such as motion adaptive radiotherapy and imaging, where the deformation of the target organ can be predicted from externally measurable signals.