Groupwise registration of cardiac perfusion MRI sequences using normalized mutual information in high dimension

Sameh Hamrouni; Nicolas Rougon; Françoise Prêteux

doi:10.1117/12.878088

9 March 2011 Groupwise registration of cardiac perfusion MRI sequences using normalized mutual information in high dimension

Sameh Hamrouni, Nicolas Rougon, Françoise Prêteux

Author Affiliations +

Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 796208 (2011) https://doi.org/10.1117/12.878088
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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.

Citation Download Citation

Sameh Hamrouni, Nicolas Rougon, and Françoise Prêteux "Groupwise registration of cardiac perfusion MRI sequences using normalized mutual information in high dimension", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796208 (9 March 2011); https://doi.org/10.1117/12.878088

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