Abstract #2128

Automated Image Quality Assessment in 5D Whole-Heart MRI Aimed at Guiding Readers of High-Dimensional Dynamic Imaging

John Heerfordt^1,2, Lorenzo Di Sopra¹, 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 (UNIL), Lausanne, Switzerland, ²Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, ³Center for Neuroprosthetics/Institutes of Bioengineering and Electrical Engineering, 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 neural network trained to assess the quality of whole-heart coronary MRA images acquired with a respiratory self-navigated ECG-triggered bSSFP sequence was tested on images from a similar, but continuous non-ECG-triggered counterpart. Since cardiac and respiratory motion-resolved reconstructions of such acquisitions oftentimes consist of up to 150 individual 3D volumes, it is desirable to be able to automatically identify the volume with highest image quality for initial display to the reader. We found that the best image quality according to the neural network agreed with human visual assessment and was found in volumes corresponding to cardiac resting phases at end-expiration.

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