How stable is quantitative MRI? - Assessment of intra- and inter-scanner-model reproducibility using identical...

Quantitative MRI (qMRI) techniques allow assessing cerebral tissue properties. However, previous studies on the accuracy of quantitative T1 and T2 mapping reported a scanner model bias of up to 10% for T1 and up to 23% for T2. Such differences would render multi-centre qMRI studies difficult and raise fundamental questions about the general precision of qMRI. A problem in previous studies was that different methods were used for qMRI parameter mapping or for measuring the transmitted radio frequency field B1 which is critical for qMRI techniques requiring corrections for B1 non-uniformities.

The goal was to assess the intra- and inter-scanner reproducibility of qMRI data at 3 ​T, using two different scanner models from the same vendor with exactly the same multiparametric acquisition protocol.

Proton density (PD), T1, T2* and T2 mapping was performed on healthy subjects and on a phantom, performing each measurement twice for each of two scanner models. Although the scanners had different hardware and software versions, identical imaging sequences were used for PD, T1 and T2* mapping, adapting the codes of an existing protocol on the older system line by line to match the software version of the newer scanner. For T2-mapping, the respective manufacturer's sequence was used which depended on the software version. However, system-dependent corrections were carried out in this case. Reproducibility was assessed by average values in regions of interest.

Mean scan-rescan variations were not exceeding 2.14%, with average values of 1.23% and 1.56% for the new and old system, respectively. Inter-scanner model deviations were not exceeding 5.21% with average values of about 2.2-3.8% for PD, 2.5-3.0% for T2*, 1.6-3.1% for T1 and 3.3-5.2% for T2.

Provided that identical acquisition sequences are used, discrepancies between qMRI data acquired with different scanner models are low. The level of systematic differences reported in this work may help to interpret multi-centre data.