Vendor-agnostic 3D multiparametric relaxometry improves cross-platform reproducibility.

Purpose: To address the unmet need for a cross-platform, multiparametric relaxometry technique to facilitate data harmonization across different sites.

Methods: A simultaneous T and T mapping technique, 3D quantification using an interleaved Look-Locker acquisition sequence with a T preparation pulse (3D-QALAS), was implemented using the open-source vendor-agnostic Pulseq platform. The technique was tested on four 3 T scanners from two vendors across two sites, evaluating cross-scanner, cross-software version, cross-site, and cross-vendor variability. The cross-vendor reproducibility was assessed using both the vendor-native and Pulseq-based implementations. A National Institute of Standards and Technology/International Society for Magnetic Resonance in Medicine system phantom and three human subjects were evaluated. The acquired T and T maps from the different 3D-QALAS runs were compared using linear regression, Bland-Altman plots, coefficient of variation (CV), and intraclass correlation coefficient (ICC).

Results: Pulseq-QALAS demonstrated high linearity (R = 0.994 for T, R = 0.999 for T) and correlation (ICC = 0.99 [0.98-0.99]) against temperature-corrected NMR reference values in the system phantom. Compared to vendor-native sequences, the Pulseq implementation showed significantly higher reproducibility in phantom T values (CV, 2.3% vs. 17%; p < 0.001), and improved T reproducibility (CV, 3.4% vs. 4.9%; p = 0.71, not significant). The Pulseq implementation reduced cross-vendor variability to a level comparable to cross-scanner (within-vendor) variability. In vivo, Pulseq-QALAS exhibited reduced cross-vendor variability, particularly for T values in gray matter with a twofold reduction in variability (CV, 2.3 vs. 5.9%; p < 0.001).

Conclusion: An identical implementation across different scanners and vendors, combined with consistent reconstruction and fitting pipelines, can improve relaxometry measurement reproducibility across platforms.