Shu-Fu Shih^{1,2}, Sevgi Gokce Kafali^{1,2}, Kara L. Calkins^{3}, and Holden H. Wu^{1,2}

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

MRI
noninvasively quantifies liver fat and iron in terms of proton-density fat
fraction (PDFF) and R_{2}*. While conventional Cartesian-based methods
require breath-holding, recent self-gated free-breathing radial techniques have
shown accurate and repeatable PDFF and R_{2}* 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 R_{2}* 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.

Deep learning (DL)-based methods can rapidly reconstruct images from undersampled data

In this work, we developed a new uncertainty-aware physics-driven deep learning network (UP-Net) that accurately quantifies PDFF and R

We performed step-by-step training (

Since fully-sampled self-gated FB Radial images are not available, after 40% self-gating (2.5-fold undersampling) [3] we performed CS reconstruction by solving

Reference CS+GC took 15 min/slice on an Intel Xeon E5-2660 CPU. UP-Net required 26 hours for training, and took 81 msec/slice for DL inference on an NVIDIA v100 GPU.

The MRI physics loss term was essential to ensure accuracy for parameter mapping during DL training. The use of uncertainty estimation in DL-based quantitative MRI is still a nascent direction. Compared with recent work

There are limitations in this work. First, diagnostic quality of reconstructed image/maps were not assessed by radiologists. Second, the current uncertainty loss term only captures aleatoric (data) uncertainty. Other types of uncertainty (e.g., model uncertainty) can be explored in the future.

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DOI: https://doi.org/10.58530/2022/0433