Abstract #1187

Efficient deep-learning-based reconstruction of Ferumoxytol-enhanced whole-heart 5D cardiac MRI

Kevin B. Borsos¹, Augustin C. Ogier¹, Christopher W. Roy¹, Ludovica Romanin^1,2, Matthias Stuber^1,3, Milan Prsa⁴, Thomas Küstner⁵, and Jérôme Yerly^1,3

¹Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland, ²Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland, ³Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, ⁴Woman-Mother-Child Department, Lausanne University Hospital, Lausanne, Switzerland, ⁵Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Free-running, cardiac MRI, Deep learning, 5D

Motivation: Free-running cardiac magnetic resonance (CMR) imaging offers several advantages over conventional breath-held imaging, however the required compressed sensing (CS) reconstruction of this high-dimensional data is time-consuming, limiting its widespread clinical adoption.

Goal(s): To develop a deep-learning-based reconstruction to rapidly obtain free-running CMR images of high quality.

Approach: A modified residual neural network (FreeNet) is trained in a supervised manner on CS images to rapidly reconstruct free-running CMR data.

Results: Our deep learning approach provides comparable 5D image quality to CS in less than one percent of the time required by CS.

Impact: FreeNet demonstrates potential for rapid inline reconstruction of motion-resolved free-running CMR images for the first time. Our work is a preliminary step towards addressing current roadblocks, bringing “single-click” free-breathing CMR to wider patient populations.

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