Abstract #0187

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

John Heerfordt^1,2, Aurélien Bustin^2,3,4, Ludovica Romanin^1,2, Estelle Tenisch², Milan Prsa⁵, Tobias Rutz⁶, Christopher W. Roy², Jérôme Yerly^2,7, Juerg Schwitter^6,8, Matthias Stuber^2,7, and Davide Piccini^1,2

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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