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

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.