Acceleration-dependent variability in simultaneous T1/T2 mapping with a hybrid (mixed) sequence: a phantom comparison of SENSE, compressed sensing, and deep learning reconstructions.

BACKGROUND/PURPOSE: Simultaneous T1/T2 mapping with hybrid (mixed) sequences has been proposed, but clinical use is limited by long acquisition times. Accelerated reconstruction-parallel imaging (PI; SENSE), compressed sensing (CS), and deep learning (DL)-allows shorter scans but may alter quantitative values and image appearance. This phantom study compared SENSE, CS, and DL across acceleration factors and examined how quantitative deviation relates to image-similarity metrics. METHODS: Using non-PI reference T1/T2 maps, we reconstructed hybrid maps with SENSE, CS, and DL at nominal reduction factors of 2-10. Circular ROIs were placed at the centers of nine phantom rods on the reference T1 map and identically propagated. Absolute percentage deviation of mean ROI T1/T2 values quantified agreement with the reference; PSNR and SSIM quantified similarity to the reference. Between-method differences at each reduction factor were tested with Friedman and, when significant, Bonferroni-corrected Wilcoxon signed-rank tests. RESULTS: Reference T1/T2 values ranged from 527 to 2138 ms and 55-572 ms, respectively. Acceleration effects were method- and metric-dependent: SENSE deviations increased at higher acceleration, whereas CS and DL preserved smaller deviations. At 10×, median T1/T2 deviations were 27.57%/31.47% for SENSE, versus 1.55%/1.40% for CS and 4.24%/2.15% for DL. Image-similarity rankings were not consistently concordant with quantitative-deviation rankings, and several factor-metric combinations showed significant between-method differences (p < 0.05). CONCLUSION: In accelerated hybrid T1/T2 mapping, quantitative deviation and PSNR/SSIM can diverge, so single-metric evaluation may be insufficient. A combined framework using quantitative deviation, PSNR/SSIM, and representative images can prioritize candidate acceleration and reconstruction conditions for subsequent task-specific in vivo validation, but does not by itself establish clinical acceptability.