Skip to content

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

Sep 2026 · Magnetic Resonance Imaging · pp. 110787 · 0 citations · 27 references
Medicine

Abstract

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.

View source

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.