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Updated: Aug 18, 2026
Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol
Published on: September 7, 2018
Deep Learning-Accelerated MRI Assessment of Hepatic Proton Density Fat Fraction: Agreement With Conventional
Seo Yeon Youn1, Bohyun Kim1, Hyun-Soo Lee2
1Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Background:
Deep learning reconstruction can shorten breath-hold MRI for liver proton density fat fraction (DL-PDFF), but agreement with conventional PDFF (Conv-PDFF) and the impact of measurement approach (regional vs. whole-liver segmentation) remain unclear.
Purpose:
To evaluate linearity and agreement of DL-PDFF with Conv-PDFF and MR spectroscopy PDFF (MRS-PDFF), and reconstruction-dependent whole-liver distribution metrics.
Study Type:
Retrospective.
Population:
Fat phantom three vials per nominal fat mass fraction (27.86%, 18.32%, and 9.12%); 96 adults (53 males; median age, 64).
Field Strength/Sequence:
3 T; Chemical shift-encoded multi-echo gradient-echo; acquisition time, 10 s (DL-PDFF; acceleration factor [AF], 6) and 14 s (Conv-PDFF; AF, 4).
Assessment:
Central region of interest (ROI) for phantom PDFF; In vivo, single ROI by radiology technologists, multiple ROIs by a resident and a medical student, and automated whole-liver segmentation, yielding voxel-based histograms and residual fitting error; inter-observer agreement.
Statistical Tests:
Wilcoxon signed-rank tests, linear regression (slope/intercept/R2), Bland-Altman bias and 95% limits of agreement (LoA) with proportional-bias slope (β), Lin's concordance correlation coefficient (CCC), Hodges-Lehmann method, and Spearman (ρ). Two-sided p < 0.05.
Results:
DL-PDFF versus Conv-PDFF showed excellent phantom linearity (R2 = 1.00). In patients, within the same approach: single ROI (bias, -0.24%; widest LoA, -2.76 to 2.28; CCC, 0.94); multiple ROIs (bias, -0.002; LoA, -1.04 to 1.04; CCC, 0.99); and whole-liver segmentation (bias, 0.12; LoA, -0.54 to 0.78; CCC, 0.99). Cross-method comparison (DL-PDFF segmentation vs. Conv-PDFF multiple ROIs) showed larger bias (0.57; LoA, -0.77 to 1.91; CCC, 0.97). Against MRS-PDFF, bias was similar (-0.05 to -0.02; LoA, -3.00 to 2.96). For DL-PDFF, whole-liver SD (Hodges-Lehmann difference, 0.55%; ρ = 0.95) and residual fitting error significantly higher (median, 2.80 vs. 1.92). Inter-observer agreement was high (CCC, 0.99).
Data Conclusion:
DL-PDFF demonstrated high agreement with Conv-PDFF while reducing acquisition time. However, measurement approach (segmentation vs. ROI) contributes larger systematic differences relevant to longitudinal and cross-study comparisons.
Evidence Level:
3.
Stage Of Technical Efficacy:
2.
