Abstract #4624

Test-Retest Reliability, Agreement, and Bias of Deep Learning Based Reconstructions for PDFF and R2* Quantification

Hung Phi Do¹, Jitka Starekova², Vadim Malis³, Won Bae³, Dawn Berkeley¹, Brian Tymkiw¹, Wissam AlGhuraibawi¹, Scott B Reeder^2,4,5,6,7, Jean H Brittain⁸, Mo Kadbi¹, and Diego Hernando^2,4

¹Canon Medical Systems USA, Inc., Tustin, CA, United States, ²Radiology, University of Wisconsin-Madison, Madison, WI, United States, ³Radiology, University of California San Diego, San Diego, CA, United States, ⁴Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ⁵Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁶Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁷Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁸Calimetrix, Madison, WI, United States

Synopsis

Keywords: Liver, Body

Motivation: Deep Learning Reconstruction (DLR) has been used routinely in clinical setting for qualitative weighted images. It is imperative to evaluate DLR for quantitative imaging prior to widespread clinical adoption.

Goal(s): To assess the test-retest reliability of PDFF and R2* values calculated from DL-reconstructed images compared to those from the conventional reconstruction (CONV).

Approach: A commercial PDFF/R2* phantom was imaged twice, with repositioning between acquisitions. Each scan was reconstructed with CONV and DLRs, which were used to calculate PDFF and R2* maps.

Results: Excellent test-retest reliability for all three reconstructions with R²>0.99 and minimal bias (<0.58% for PDFF and <3.67 s^-1 for R2*).

Impact: SNR, resolution, and scan-time of quantitative MRI may benefit from DLR similarly as for qualitative MRI. This study showed that DLR has excellent test-retest reliability for PDFF/R2* quantification with minimal bias, providing foundational evidence for wider clinical adoption.

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