Tailored magnetic resonance fingerprinting for simultaneous non-synthetic and quantitative imaging: A repeatability study.

Purpose: The goals of this study include: (a) generating tailored magnetic resonance fingerprinting (TMRF) based non-synthetic imaging; (b) assessing the repeatability of TMRF and deep learning-based mapping of in vitro ISMRM/NIST phantom and in vivo brain data of healthy human subjects.

Methods: We have acquired qualitative images obtained from the vendor-supplied gold standard (GS), MRF (synthetic), and TMRF (non-synthetic) on one representative healthy human brain. We also acquired 30 datasets on the ISMRM/NIST phantom for the in vitro repeatability study on a GE Discovery 3T MR750w scanner using the TMRF sequence. We compared T and T maps generated from 30 ISMRM/NIST phantom datasets to the spin-echo (SE) based GS method as part of the in vitro repeatability study. R-squared coefficient of determination in a simple linear regression and Bland-Altman analysis were computed for 30 datasets of ISMRM/NIST phantom to assess the accuracy of in vitro quantitative TMRF data. The repeatability of T and T estimates by TMRF was evaluated by calculating the standard deviation (SD) divided by the average of 30 datasets for each sphere, respectively. We acquired 10 volunteers for the in vivo repeatability study on the same scanner using the same TMRF sequence. These volunteers were imaged five times with two runs per repetition, resulting in 100 in vivo datasets. Five contrasts, T and T maps of 10 human volunteers acquired over five repetitions, were evaluated in the in vivo repeatability study. We computed the intraclass correlation coefficient (ICC) of the signal-to-noise ratio (SNR), signal intensities, T and T relaxation times in white matter (WM), and gray matter (GM).

Results: The synthetic images generated from MRF show partial volume and flow artifacts compared to non-synthetic images obtained from TMRF images and the GS. In vitro studies show that TMRF estimates have less than 5% variations except sphere 14 in the T array (6.36%). TMRF and SE relaxometry measurements were strongly correlated; R values were 0.9958 and 0.9789 for T and T estimates, respectively. Based on the ICC values, SNR, mean intensity values, and relaxation times of WM and GM for the in vivo studies were consistent. T and T values of WM and GM were similar to previously published values. The mean ± SD of T and T for WM for ten subjects and five repeats are 992 ± 41 ms and 99 ± 6 ms, while the corresponding values for T and T for GM are 1598 ± 73 ms and 152 ± 14 ms.

Conclusion: TMRF and deep learning-based reconstruction produce repeatable, non-synthetic multi-contrast images, and parametric maps simultaneously.