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Abstract #0187

SIMBA 2.0: An enhanced SImilarity-driven Multi-dimensional Binning Algorithm for free-running ferumoxytol-enhanced whole-heart MRI

John Heerfordt1,2, Aurélien Bustin2,3,4, Ludovica Romanin1,2, Estelle Tenisch2, Milan Prsa5, Tobias Rutz6, Christopher W. Roy2, Jérôme Yerly2,7, Juerg Schwitter6,8, Matthias Stuber2,7, and Davide Piccini1,2
1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 3IHU LIRYC, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac-Bordeaux, France, 4Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, CHU de Bordeaux, Pessac, France, 5Division of Pediatric Cardiology, Department Woman-Mother-Child, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 6Division of Cardiology, Cardiovascular Department, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 7CIBM Center for Biomedical Imaging, Lausanne, Switzerland, 8Cardiac MR Center, Lausanne University Hospital, Lausanne, Switzerland

A SImilarity-driven Multi-dimensional Binning Algorithm (SIMBA) was recently proposed for fast reconstruction of motion-consistent clusters for free-running whole-heart MRA acquisitions. Originally, only the most populated cluster was used for the reconstruction of a motion-suppressed image. In this work we investigated whether the redundancy of information among the clusters can be exploited to improve image quality. Specifically, an adapted XD-GRASP reconstruction and a multidimensional patch-based low-rank denoising algorithm were compared. Four different reconstructions were quantitatively evaluated and compared using ferumoxytol-enhanced free-running datasets from 10 pediatric and adult CHD patients. Information sharing resulted in significantly sharper anatomical features and increased image quality.

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