Abstract #3847

Deep Learning-Based Liver Fat and R2* Mapping with Uncertainty Estimation using Self-Gated Free-Breathing Stack-of-Radial MRI

Shu-Fu Shih^1,2, Sevgi Gokce Kafali^1,2, Tess Armstrong¹, Xiaodong Zhong³, Kara L. Calkins⁴, and Holden H. Wu^1,2

¹Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States, ⁴Pediatrics, University of California, Los Angeles, Los Angeles, CA, United States

Self-gated free-breathing multi-echo stack-of-radial MRI quantifies liver fat and R₂*. However, data undersampling due to motion self-gating can degrade the image quality and quantification accuracy. Previous methods required either longer scan time or computationally expensive constrained reconstruction. In this work, a deep learning-based two-stage network was developed to suppress undersampling artifacts and rapidly generate quantitative fat and R₂* maps with a pixel-wise uncertainty map. The proposed method achieved accurate fat and R₂* mapping and reduced the computational time by two orders of magnitude versus constrained reconstruction. The uncertainty map can be used to detect regions with potential quantification errors.

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