Abstract #0919

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

John Heerfordt^1,2, Robin Demesmaeker^2,3, Jérôme Yerly^1,4, Tobias Kober^1,2,5, Matthias Stuber^1,4, and Davide Piccini^1,2,5

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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