Abstract #3088

Deep Learning Image Reconstruction of Accelerated 3D Cartesian Phase-Cycled bSSFP for Quantitative Mapping

Melina Gilsing^1,2,3, Berk C Açikgöz^1,2,4, Li Feng⁵, Christoph Gräni⁶, Jessica AM Bastiaansen^1,2, and Eva S Peper^1,2,3

¹Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland, ²Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland, ³Center for Artificial Intelligence in Medicine (CAIM), University of Bern, Bern, Switzerland, ⁴Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland, ⁵Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, ⁶Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland

Synopsis

Keywords: Joint, AI/ML Image Reconstruction, Fat Fraction Mapping, Phase-cycled bSSFP, quantitative Imaging

Motivation: Phase-cycled bSSFP is valuable for quantitative mapping but requires acceleration methods with long reconstruction times.

Goal(s): To explore deep learning networks for image reconstruction of accelerated phase-cycled bSSFP data.

Approach: A U-Net and an end-to-end variational network were implemented and optimized on retrospectively and prospectively undersampled 3D Cartesian phase-cycled bSSFP data. Elliptical signal profiles and fat fraction maps were evaluated and compared against the ground truth and a compressed sensing reconstruction.

Results: The end-to-end variational network performed similarly well as the compressed sensing algorithm on up to 8-times accelerated scans within 20s reconstruction time. Elliptical signal properties and fat fraction maps were well-preserved.

Impact: The need for accelerated acquisitions in phase-cycled bSSFP significantly prolongs reconstruction times, necessitating efficient reconstruction techniques. This study demonstrates that deep learning achieves a feasible 20s reconstruction time while maintaining accurate fat fraction measurements in the knee of three volunteers.

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