Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom

Abstract

Recent innovations in quantitative magnetic resonance imaging (MRI) measurement methods have led to improvements in accuracy, repeatability, and acquisition speed, and have prompted renewed interest to reevaluate the medical value of quantitative T1. The purpose of this study was to determine the bias and reproducibility of T1 measurements in a variety of MRI systems with an eye toward assessing the feasibility of applying diagnostic threshold T1 measurement across multiple clinical sites. We used the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom to assess variations of T1 measurements, using a slow, reference standard inversion recovery sequence and a rapid, commonly-available variable flip angle sequence, across MRI systems at 1.5 tesla (T) (two vendors, with number of MRI systems n = 9) and 3 T (three vendors, n = 18). We compared the T1 measurements from inversion recovery and variable flip angle scans to ISMRM/NIST phantom reference values using Analysis of Variance (ANOVA) to test for statistical differences between T1 measurements grouped according to MRI scanner manufacturers and/or static field strengths. The inversion recovery method had minor over- and under-estimations compared to the NMR-measured T1 values at both 1.5 T and 3 T. Variable flip angle measurements had substantially greater deviations from the NMR-measured T1 values than the inversion recovery measurements. At 3 T, the measured variable flip angle T1 for one vendor is significantly different than the other two vendors for most of the samples throughout the clinically relevant range of T1. There was no consistent pattern of discrepancy between vendors. We suggest establishing rigorous quality control procedures for validating quantitative MRI methods to promote confidence and stability in associated measurement techniques and to enable translation of diagnostic threshold from the research center to the entire clinical community.

Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI

“…The most important contribution of this article is the vendor-neutral solution it provides for multi-center reliability of qMRI by significantly reducing inter-vendor variability. This has been a plaguing issue for standardization of qMRI methods in validation 67,68 , multi-center clinical trials 69 , establishing protocols 17 , applied neuroimaging studies 70 , determining the range of parameters in disease typing 71,72 and health 16,40 , between scanner upgrades 51 and even in phantom studies 12,13 . By reducing such variabilities, VENUS approach can bring qMRI closer to teasing out the true biological variability in quantifying in-vivo tissue microstructure 73 .…”

“…Vendor-native systems set the transmit gain using their own prescan routine, which may lead to a systematic bias in quantitative mapping. In this work, we implemented prescan for G1 and S1-2 as described by 13 and configured RTHawk to use the same calibration measurements. Nevertheless, it is possible to make this step vendor-neutral as well.…”

“…As for the multicenter stability, MP2RAGE appears to be a promising T1 mapping method at 7T with a single vendor considered across 8 imaging sites 11 . On the other hand, substantial multicenter variability is reported for another popular whole-brain imaging method VFA-T1, both in-vivo and in the system phantom 12,13 . Several factors contribute to the variability of the VFA-T1 measurement, including B1 field inhomogeneity 8 , incomplete spoiling 14 , sequence parameters and bore temperature 13 , and uncontrolled magnetization transfer (MT) effects 15 .…”

“…On the other hand, substantial multicenter variability is reported for another popular whole-brain imaging method VFA-T1, both in-vivo and in the system phantom 12,13 . Several factors contribute to the variability of the VFA-T1 measurement, including B1 field inhomogeneity 8 , incomplete spoiling 14 , sequence parameters and bore temperature 13 , and uncontrolled magnetization transfer (MT) effects 15 . Because of all these diverse confounders of T1 stability, the healthy range of in-vivo T1 values at 3T remains elusive 16,17 .…”

“…For example, a simple yet powerful B1 correction framework for VFA-T1 has been established 18 , but B1 mapping sequences are typically not available in commercial systems or available sequences are different across vendors. This not only imposes a practical challenge in evaluating the reliability of VFA-T1 measurements across vendors 13,19 but the differences between vendor-native B1 mapping methods can aggravate the instability 20 . Another example is the spoiling gradient area and RF spoiling phase increment in the commercial implementations of 3D spoiled gradient-echo (3D-SPGR) sequences.…”