Abstract #0433

Uncertainty-Aware Physics-Driven Deep Learning Network for Fat and R₂* Quantification in Self-Gated Free-Breathing Stack-of-Radial MRI

Shu-Fu Shih^1,2, Sevgi Gokce Kafali^1,2, Kara L. Calkins³, and Holden H. Wu^1,2

¹Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States, ²Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States, ³Department of Pediatrics, University of California Los Angeles, Los Angeles, CA, United States

Synopsis

MRI noninvasively quantifies liver fat and iron in terms of proton-density fat fraction (PDFF) and R₂*. While conventional Cartesian-based methods require breath-holding, recent self-gated free-breathing radial techniques have shown accurate and repeatable PDFF and R₂* mapping. However, data oversampling or computationally expensive reconstruction is required to reduce radial undersampling artifacts due to self-gating. This work developed an uncertainty-aware physics-driven deep learning network (UP-Net) that accurately and rapidly quantifies PDFF and R₂* using data from self-gated free-breathing stack-of-radial MRI. UP-Net used an MRI physics loss term to guide quantitative mapping, and also provided uncertainty estimation for each quantitative parameter.

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Uncertainty-Aware Physics-Driven Deep Learning Network for Fat and R2* Quantification in Self-Gated Free-Breathing Stack-of-Radial MRI

Synopsis

Uncertainty-Aware Physics-Driven Deep Learning Network for Fat and R₂* Quantification in Self-Gated Free-Breathing Stack-of-Radial MRI