Abstract
OBJECTIVE: To evaluate the impact of accelerated, deep learning-based reconstructed T1-weighted VIBE Dixon images on fat-signal fraction (FSF) quantification compared with standard protocols. METHODS: In this prospective single-center study, patients undergoing clinically indicated abdominal MRI underwent 3 T1-weighted VIBE acquisitions on a 1.5 T scanner: a standard sequence and 2 accelerated sequences ("fast" and "ultra-fast"). The accelerated scans employed higher CAIPIRINHA parallel imaging factors, partial Fourier sampling, and deep learning-based image reconstruction. Subsequently, whole-liver FSF was determined using a validated automated liver segmentation tool for in-phase and opposed-phase reconstructions. The quality of segmentation was assessed visually and by comparing liver volumes. Statistical analyses included calculation of mean absolute error and Spearman's correlation for FSF agreement. RESULTS: Between March 2025 and May 2025, 60 patients (mean age, 63.7 ± 13.9 y; 55% females) were enrolled. Acquisition times were 15 seconds for the standard sequence and 10 and 6 seconds for fast and ultra-fast sequences, respectively. The whole liver segmentations from the fast and ultra-fast sequences showed high correlations (ρ > 0.975, both P < 0.001) with minimal mean absolute error of 1.1% and 1.5% from the standard sequence. The liver fat quantification showed high concordance across protocols, too: median FSF was 2.3% (standard), 2.6% (fast), and 2.4% (ultra-fast), with a mean absolute error <0.6% from standard for both accelerated protocols (all ρ > 0.92, P < 0.001). CONCLUSIONS: Liver fat quantification using highly accelerated, deep learning-enhanced MRI sequences enables reliable assessment of liver fat content with a significant reduction in scan time in low fat-fraction ranges.