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

Abstract

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 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.

Keywords: MR Fingerprinting; Multiple Sclerosis; Normal appearing white matter; Relaxometry; Splenium.

Fig. 1

Region of Interest segmentations within MRF maps: MRF T₁ relaxometry map demonstrating an active lesion in a patient with multiple sclerosis. a Depicts the lesion [1] in the parietal white matter and the corresponding ROI. The T₁ and T₂ values and the first order statistics were simultaneously obtained from this ROI. b shows the ROI over the frontal normal appearing white matter [2] and splenium [3]

Fig. 2

Active lesion with surrounding edema in a patient confirmed with multiple sclerosis: a depicts the lesion in the parietal white matter on MRF-based T₁ map; b shows the same lesion in the same clinical exam in a T₁ weighted sequence, c illustrates the typical high intensity on a T₂ weighted spin echo sequence, and d confirms peripheral enhancement after gadolinium injection in a T₁ weighted sequence.

Fig. 3

Frontal NAWM and Splenium classification for MS compared to control: Partial least squares discriminant analysis between patients (cases) and controls for splenium and frontal NAWM (normal appearing white matter) within all 18 MS patients and 18 controls

Fig. 4

T-distributed stochastic neighbor embedding clustering for all segmentations: these figures demonstrate strong clustering of the data under a T-SNE algorithm, and this allows apparently perfectly discrimination between lesions and non-lesions with T₁ (a) T₂ (b) and combining the two properties (c) features. NAWM: frontal normal appearing white matter; MS: Multiple Sclerosis. The data depicted in circles refers to patients with MS and the data on square to controls

Fig. 5

Box and whisker plots for differences in T1 (a) and T2 (b) mean relaxation times between the structures analyzed. The vertical lines depict the ranges, the light boxes the second quartile, the dark gray boxes the third quartile and the solid vertical line the median. For the lesions, the 25th percentile (T1; T2: 1240 ms; 59 ms), median (T1;T2: 1368 ms; 67 ms), 75 percentile (T1;T2: 1509 ms; 87 ms) were higher compared to all the anatomical structures. The T1 and T2 scales are on milliseconds (ms). F-NAWM: frontal appearing normal white matter

Fig. 6

Visualization of the top 5 features correlated with time-since-diagnosis. The correlation was the strongest for a T2 lesion mean (rho = 0.419, p = 0.015), b T1 lesion minimum (rho = 0.367, p = 0.033), c T2 lesion root mean squared (rho = 0.367, p = 0.033), d T1 splenium uniformity (rho = −0.354, p = 0.04) and e T2 lesion median (rho = 0.354, p = 0.040). Multiple lesions are collapsed to a singular value via arithmetic mean