Quantitative MR imaging is becoming more feasible to be used in clinical work since new approaches have been proposed in order to substantially accelerate the acquisition and due to the possibility of synthetically deriving weighted images from the parametric maps. However, their applicability has to be thoroughly validated in order to be included in clinical practice. In this pilot study, we acquired Magnetic Resonance Image Compilation scans to obtain T1, T2 and PD maps in 14 glioma patients. Abnormal tissue was segmented based on conventional images and using a deep learning segmentation technique to define regions of interest (ROIs). The quantitative T1, T2 and PD values inside ROIs were analyzed using the mean, the standard deviation, the skewness and the kurtosis and compared to the quantitative T1, T2 and PD values found in normal white matter. We found significant differences in pre-contrast T1 and T2 values between abnormal tissue and healthy tissue, as well as between T1w-enhancing and non-enhancing regions. ROC analysis was used to evaluate the potential of quantitative T1 and T2 values for voxel-wise classification of abnormal/normal tissue (AUC = 0.95) and of T1w enhancement/non-enhancement (AUC = 0.85). A cross-validated ROC analysis found high sensitivity (73%) and specificity (73%) with AUCs up to 0.68 on the a priori distinction between abnormal tissue with and without T1w-enhancement. These results suggest that normal tissue, abnormal tissue, and tissue with T1w-enhancement are distinguishable by their pre-contrast quantitative values but further investigation is needed.

Our purpose is to evaluate bias and repeatability of the quantitative MRI sequences QRAPMASTER, based on steady-state imaging, and variable Flip Angle MRF (MRF-VFA), based on the transient response. Both techniques are assessed with a standardized phantom and five volunteers on 1.5 T and 3 T clinical scanners. All scans were repeated eight times in consecutive weeks. In the phantom, the mean bias±95% confidence interval for T1 values with QRAPMASTER was 10 ± 10% on 1.5 T and 4 ± 13% on 3.0 T. The mean bias for T1 values with MRF-vFA was 21 ± 17% on 1.5 T and 9 ± 9% on 3.0 T. For T2 values the mean bias with QRAPMASTER was 12 ± 3% on 1.5 T and 23 ± 1% on 3.0 T. For T2 values the mean bias with MRF-vFA was 17 ± 1% on 1.5 T and 19 ± 2% on 3.0 T. QRAPMASTER estimated lower T1 and T2 values than MRF-vFA. Repeatability was good with low coefficients of variation (CoV). Mean CoV ± 95% confidence interval for T1 were 3.2 ± 0.4% on 1.5 T and 4.5 ± 0.8% on 3.0 T with QRAPMASTER and 2.7% ± 0.2% on 1.5 T and 2.5 ± 0.2% on 3.0 T with MRF-vFA. For T2 were 3.3 ± 1.9% on 1.5 T and 3.2 ± 0.6% on 3.0 T with QRAPMASTER and 2.0 ± 0.4% on 1.5 T and 5.7 ± 1.0% on 3.0 T with MRF-vFA. The in-vivo T1 and T2 are in the range of values previously reported by other authors. The in-vivo mean CoV ± 95% confidence interval in gray matter were for T1 1.7 ± 0.2% using QRAPMASTER and 0.7 ± 0.5% using MRF-vFA and for T2 were 0.9 ± 0.4% using QRAPMASTER and 2.4 ± 0.5% using MRF-vFA. In white matter were for T1 0.9 ± 0.3% using QRAPMASTER and 1.3 ± 1.1% using MRF-vFA and for T2 were 0.7 ± 0.4% using QRAPMASTER and 2.4 ± 0.4% using MRF-vFA. A GLM analysis showed that the variations in T1 and T2 mainly depend on the field strength and the subject, but not on the follow-up repetition in different days. This confirms the high repeatability of QRAPMASTER and MRF-vFA. In summary, QRAPMASTER and MRF-vFA on both systems were highly repeatable with moderate accuracy, providing results comparable to standard references. While repeatability was similar for both methods, QRAPMASTER was more accurate. QRAPMASTER is a tested commercial product but MRF-vFA is 4.77 times faster, which would ease the inclusion of quantitative relaxometry.

MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estimation for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmentations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL- and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray matter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter.