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Proceedings Paper

Groupwise registration of cardiac perfusion MRI sequences using normalized mutual information in high dimension
Author(s): Sameh Hamrouni; Nicolas Rougon; Françoise Prêteux
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Paper Abstract

In perfusion MRI (p-MRI) exams, short-axis (SA) image sequences are captured at multiple slice levels along the long-axis of the heart during the transit of a vascular contrast agent (Gd-DTPA) through the cardiac chambers and muscle. Compensating cardio-thoracic motions is a requirement for enabling computer-aided quantitative assessment of myocardial ischaemia from contrast-enhanced p-MRI sequences. The classical paradigm consists of registering each sequence frame on a reference image using some intensity-based matching criterion. In this paper, we introduce a novel unsupervised method for the spatio-temporal groupwise registration of cardiac p-MRI exams based on normalized mutual information (NMI) between high-dimensional feature distributions. Here, local contrast enhancement curves are used as a dense set of spatio-temporal features, and statistically matched through variational optimization to a target feature distribution derived from a registered reference template. The hard issue of probability density estimation in high-dimensional state spaces is bypassed by using consistent geometric entropy estimators, allowing NMI to be computed directly from feature samples. Specifically, a computationally efficient kth-nearest neighbor (kNN) estimation framework is retained, leading to closed-form expressions for the gradient flow of NMI over finite- and infinite-dimensional motion spaces. This approach is applied to the groupwise alignment of cardiac p-MRI exams using a free-form Deformation (FFD) model for cardio-thoracic motions. Experiments on simulated and natural datasets suggest its accuracy and robustness for registering p-MRI exams comprising more than 30 frames.

Paper Details

Date Published: 9 March 2011
PDF: 14 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796208 (9 March 2011); doi: 10.1117/12.878088
Show Author Affiliations
Sameh Hamrouni, TELECOM SudParis, CNRS UMR (France)
Nicolas Rougon, TELECOM SudParis, CNRS UMR (France)
Françoise Prêteux, Mines ParisTech (France)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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