Title — Groupwise registration of cardiac perfusion MRI sequences using mutual information in high dimension
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 work, we present an unsupervised method for the spatio-temporal groupwise registration of cardiac p-MRI exams based on mutual information (MI) 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 MI to be computed directly from feature samples.
Biography — Sameh Hamrouni received her MSc in computer vision from National computer science engineering school (Tunis) in 2008. She joined Institut Telecom SudParis in 2009 for a PhD in image processing where she studied spatio-temporal variational approach for quantitative analysis of myocardial perfusion in MRI, supervised by Nicolas Rougon and Françoise Prêteux. She joined Université Paris Descartes/LIPADE team in 2013 working on image processing projects. Since 2015, Sameh joined GE healthcare as an image quality engineer (Buc, France). Her research interests include image processing and medical physics.