In vivo evolution of biopsy-proven inflammatory demyelination quantified by R2t* mapping

Evaluating brain damage in multiple sclerosis with simultaneous multi-angular-relaxometry of tissue

OBJECTIVE

Multiple sclerosis (MS) is a common demyelinating central nervous system disease. MRI methods that can quantify myelin loss are needed for trials of putative remyelinating agents. Quantitative magnetization transfer MRI introduced the macromolecule proton fraction (MPF), which correlates with myelin concentration. We developed an alternative approach, Simultaneous-Multi-Angular-Relaxometry-of-Tissue (SMART) MRI, to generate MPF. Our objective was to test SMART-derived MPF metric as a potential imaging biomarker of demyelination.

METHODS

Twenty healthy control (HC), 11 relapsing-remitting MS (RRMS), 22 progressive MS (PMS), and one subject with a biopsied tumefactive demyelinating lesion were scanned at 3T using SMART MRI. SMART-derived MPF metric was determined in normal-appearing cortical gray matter (NAGM), normal-appearing subcortical white matter (NAWM), and demyelinating lesions. MPF metric was evaluated for correlations with physical and cognitive test scores. Comparisons were made between HC and MS and between MS subtypes. Furthermore, correlations were determined between MPF and neuropathology in the biopsied person.

RESULTS

SMART-derived MPF in NAGM and NAWM were lower in MS than HC (p < 0.001). MPF in NAGM, NAWM and lesions differentiated RRMS from PMS (p < 0.01, p < 0.001, p < 0.001, respectively), whereas lesion volumes did not. MPF in NAGM, NAWM and lesions correlated with the Expanded Disability Status Scale (p < 0.01, p < 0.001, p < 0.001, respectively) and nine-hole peg test (p < 0.001, p < 0.001, p < 0.01, respectively). MPF was lower in the histopathologically confirmed inflammatory demyelinating lesion than the contralateral NAWM and increased in the biopsied lesion over time, mirroring improved clinical performance.

INTERPRETATION

SMART-derived MPF metric holds potential as a quantitative imaging biomarker of demyelination and remyelination.

Stronger Microstructural Damage Revealed in Multiple Sclerosis Lesions With Central Vein Sign by Quantitative Gradient Echo MRI

Background

Multiple sclerosis (MS) lesions typically form around a central vein that can be visualized with FLAIR* MRI, creating the central vein sign (CVS) which may reflect lesion pathophysiology. Herein we used gradient echo plural contrast imaging (GEPCI) MRI to simultaneously visualize CVS and measure tissue damage in MS lesions. We examined CVS in relation to tissue integrity in white matter (WM) lesions and among MS subtypes.

Objective

We aimed to determine if CVS positive lesions were specific to MS subtype, if CVS can be detected consistently among readers using the GEPCI method, and if there were differences in tissue damage in lesions with vs without CVS.

Subjects and Methods

Thirty relapsing-remitting MS (RRMS) subjects and 38 primary and secondary progressive MS (PMS) subjects were scanned with GEPCI protocol at 3T. GEPCI T2*-SWI images were generated to visualize CVS. Two investigators independently evaluated WM lesions for CVS and measured lesion volumes. To estimate tissue damage severity, total lesion volume, and mean lesion volume, R2t*-based tissue damage score (TDS) of individual lesions and tissue damage load (TDL) were measured for CVS+, CVS-, and confluent lesions. Spearman correlations were made between MRI and clinical data. One-way ANCOVA with age and sex as covariates was used to compare measurements of CVS+ vs CVS- lesions in each individual.

Results

398 of 548 lesions meeting inclusion criteria showed CVS. Most patients had ≥40% CVS+ lesions. CVS+ lesions were present in similar proportion among MS subtypes. Interobserver agreement was high for CVS detection. CVS+ and confluent lesions had higher average and total volumes vs CVS- lesions. CVS+ and confluent lesions had more tissue damage than CVS- lesions based on TDL and mean TDS.

Conclusion

CVS occurred in RRMS and PMS in similar proportions. CVS+ lesions had greater tissue damage and larger size than CVS- lesions.

Learning-based motion artifact removal networks for quantitative R 2 ∗ mapping

PURPOSE

To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and B 0 -inhomogeneity-corrected R 2 ∗ maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data.

METHODS

We train two convolutional neural networks (CNNs) to correct motion artifacts for high-quality estimation of quantitative B 0 -inhomogeneity-corrected R 2 ∗ maps from mGRE sequences. The first CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high-quality motion-free quantitative R 2 ∗ (and any other mGRE-enabled) maps using the standard voxel-wise analysis or machine learning-based analysis. The second CNN, LEARN-BIO, is trained to directly generate motion- and B 0 -inhomogeneity-corrected quantitative R 2 ∗ maps from motion-corrupted magnitude-only mGRE images by taking advantage of the biophysical model describing the mGRE signal decay.

RESULTS

We show that both CNNs trained on synthetic MR images are capable of suppressing motion artifacts while preserving details in the predicted quantitative R 2 ∗ maps. Significant reduction of motion artifacts on experimental in vivo motion-corrupted data has also been achieved by using our trained models.

CONCLUSION

Both LEARN-IMG and LEARN-BIO can enable the computation of high-quality motion- and B 0 -inhomogeneity-corrected R 2 ∗ maps. LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of R 2 ∗ maps, while LEARN-BIO directly performs motion- and B 0 -inhomogeneity-corrected R 2 ∗ estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN-BIO is an advantage that can lead to a broader clinical application.

Tissue damage detected by quantitative gradient echo MRI correlates with clinical progression in non-relapsing progressive MS

BACKGROUND

Imaging biomarkers of progressive multiple sclerosis (MS) are needed. Quantitative gradient recalled echo (qGRE) magnetic resonance imaging (MRI) evaluates microstructural tissue damage in MS.

OBJECTIVE

To evaluate qGRE-derived R2t* as an imaging biomarker of MS progression compared with atrophy and lesion burden.

METHODS

Twenty-three non-relapsing progressive MS (PMS), 22 relapsing-remitting MS (RRMS), and 18 healthy control participants underwent standard MS physical and cognitive neurological assessments and imaging with qGRE, FLAIR, and MPRAGE at 3T. PMS subjects were tested clinically and imaged every 9 months over 45 months. Imaging measures included lesion burden, atrophy, and R2t* in cortical gray matter (GM), deep GM, and normal-appearing white matter (NAWM). Longitudinal analysis of clinical performance and imaging biomarkers in PMS subjects was conducted via linear models with subject as repeated, within-subject factor. Relationship between imaging biomarkers and clinical scores was assessed by Spearman rank correlation.

RESULTS

R2t* reductions correlated with neurological impairment cross-sectionally and longitudinally. PMS patients with clinically defined disease progression (N = 13) showed faster decrease of R2t* in NAWM and deep GM compared with the clinically stable PMS group (N = 10). Importantly, tissue damage measured by R2t* outperformed lesion burden and atrophy as a biomarker of progression during the study period.

CONCLUSION

qGRE-derived R2t* is a potential imaging biomarker of MS progression.