Whole brain 3D MR fingerprinting in multiple sclerosis: a pilot study

Thomaz R. Mostardeiro, Ananya Panda, Norbert G. Campeau, Robert J. Witte, Nicholas B. Larson, Yi Sui, Aiming Lu, Kiaran P. McGee
2021 BMC Medical Imaging  
Background MR fingerprinting (MRF) is a novel imaging method proposed for the diagnosis of Multiple Sclerosis (MS). This study aims to determine if MR Fingerprinting (MRF) relaxometry can differentiate frontal normal appearing white matter (F-NAWM) and splenium in patients diagnosed with MS as compared to controls and to characterize the relaxometry of demyelinating plaques relative to the time of diagnosis. Methods Three-dimensional (3D) MRF data were acquired on a 3.0T MRI system resulting in
more » ... isotropic voxels (1 × 1 × 1 mm3) and a total acquisition time of 4 min 38 s. Data were collected on 18 subjects paired with 18 controls. Regions of interest were drawn over MRF-derived T1 relaxometry maps encompassing selected MS lesions, F-NAWM and splenium. T1 and T2 relaxometry features from those segmented areas were used to classify MS lesions from F-NAWM and splenium with T-distributed stochastic neighbor embedding algorithms. Partial least squares discriminant analysis was performed to discriminate NAWM and Splenium in MS compared with controls. Results Mean out-of-fold machine learning prediction accuracy for discriminant results between MS patients and controls for F-NAWM was 65 % (p = 0.21) and approached 90 % (p < 0.01) for the splenium. There was significant positive correlation between time since diagnosis and MS lesions mean T2 (p = 0.015), minimum T1 (p = 0.03) and negative correlation with splenium uniformity (p = 0.04). Perfect discrimination (AUC = 1) was achieved between selected features from MS lesions and F-NAWM. Conclusions 3D-MRF has the ability to differentiate between MS and controls based on relaxometry properties from the F-NAWM and splenium. Whole brain coverage allows the assessment of quantitative properties within lesions that provide chronological assessment of the time from MS diagnosis.
doi:10.1186/s12880-021-00620-5 pmid:34022832 pmcid:PMC8141188 fatcat:vsnvvhcf6jb25k3s6s5kokrb7e