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Abstract #0919

Automated Coil Ranking using a Neural Network for Image Quality Assessment: An Explorative Study in Coronary MRI

John Heerfordt1,2, Robin Demesmaeker2,3, Jérôme Yerly1,4, Tobias Kober1,2,5, Matthias Stuber1,4, and Davide Piccini1,2,5

1Department of Radiology, University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland, 2Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 3Institute of Bioengineering/Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, 5LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

A novel method that uses a neural network to rank individual coil elements of phased arrays based on their image quality is proposed. With a ranking of the coil elements, the specific subset of coils that leads to the best image reconstruction can be selected. Alternatively, the contribution of coils with high levels of artifacts and noise to the final image can be reduced. We show that both selection and weighting of coil elements can reduce the level of image artifacts while maintaining a high signal intensity in the region of the examined organ.

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