3-Dimensional Fluid and White Matter Suppression Magnetic Resonance Imaging Sequence Accelerated With Compressed Sensing Improves Multiple Sclerosis Cervical Spinal Cord Lesion Detection Compared With Standard 2-Dimensional Imaging

Objectives Fluid and white matter suppression (FLAWS) is a recently proposed magnetic resonance sequence derived from magnetization-prepared 2 rapid acquisition gradient-echo providing 2 coregistered datasets with white matter- and cerebrospinal fluid-suppressed signal, enabling synthetic imaging with amplified contrast. Although these features are high potential for brain multiple sclerosis (MS) imaging, spinal cord has never been evaluated with this sequence to date. The objective of this work was therefore to assess diagnostic performance and self-confidence provided by compressed-sensing (CS) 3-dimensional (3D) FLAWS for cervical MS lesion detection on a head scan that includes the cervical cord without changing standard procedures. Materials and Methods Prospective 3 T scans (MS first diagnosis or follow-up) acquired between 2019 and 2020 were retrospectively analyzed. All patients underwent 3D CS-FLAWS (duration: 5 minutes 40 seconds), axial T-2 turbo spin echo covering cervical spine from cervicomedullary junction to the same inferior level as FLAWS, and sagittal cervical T-2/short tau inversion recovery imaging. Two readers performed a 2-stage double-blind reading, followed by consensus reading. Wilcoxon tests were used to compare the number of detected spinal cord lesions and the reader's diagnostic self-confidence when using FLAWS versus the reference 2D T-2-weighted imaging. Results Fifty-eight patients were included (mean age, 40 +/- 13 years, 46 women, 7 +/- 6 years mean disease duration). The CS-FLAWS detected significantly more lesions than the reference T-2-weighted imaging (197 vs 152 detected lesions, P < 0.001), with a sensitivity of 98% (T-2-weighted imaging sensitivity: 90%) after consensual reading. Considering the subgroup of patients who underwent sagittal T2 + short tau inversion recovery imaging (Magnetic Resonance Imaging for Multiple Sclerosis subgroup), +250% lesions were detected with FLAWS (63 vs 25 lesions detected, P < 0.001). Mean reading self-confidence was significantly better with CS-FLAWS (median, 5 [interquartile range, 1] [no doubt for diagnosis] vs 4 [interquartile range, 1] [high confidence]; P < 0.001). Conclusions Imaging with CS-FLAWS provides an improved cervical spinal cord exploration for MS with increased self-confidence compared with conventional T-2-weighted imaging, in a clinically acceptable time.

Automated Detection and Segmentation of Multiple Sclerosis Lesions Using Ultra-High-Field MP2RAGE

Meritxell Bach Cuadra, Mario Joao Fartaria de Oliveira, Cristina Granziera, Tobias Kober

Objectives The aim of this study was to develop a new automated segmentation method of white matter (WM) and cortical multiple sclerosis (MS) lesions visible on magnetization-prepared 2 inversion-contrast rapid gradient echo (MP2RAGE) images acquired at 7 T MRI. Materials and Methods The proposed prototype (MSLAST [Multiple Sclerosis Lesion Analysis at Seven Tesla]) takes as input a single image contrast derived from the 7T MP2RAGE prototype sequence and is based on partial volume estimation and topological constraints. First, MSLAST performs a skull-strip of MP2RAGE images and computes tissue concentration maps for WM, gray matter (GM), and cerebrospinal fluid (CSF) using a partial volume model of tissues within each voxel. Second, MSLAST performs (1) connected-component analysis to GM and CSF concentration maps to classify small isolated components as MS lesions; (2) hole-filling in the WM concentration map to classify areas with low WM concentration surrounded by WM (ie, MS lesions); and (3) outlier rejection to the WM mask to improve the classification of small WM lesions. Third, MSLAST unifies the 3 maps obtained from 1, 2, and 3 processing steps to generate a global lesion mask. Results Quantitative and qualitative assessments were performed using MSLAST in 25 MS patients from 2 research centers. Overall, MSLAST detected a median of 71% of MS lesions, specifically 74% of WM and 58% of cortical lesions, when a minimum lesion size of 6 mu L was considered. The median false-positive rate was 40%. When a 15 mu L minimal lesions size was applied, which is the approximation of the minimal size recommended for 1.5/3 T images, the median detection rate was 80% for WM and 63% for cortical lesions, respectively, and the median false-positive rate was 33%. We observed high correlation between MSLAST and manual segmentations (Spearman rank correlation coefficient, rho = 0.91), although MSLAST underestimated the total lesion volume (average difference of 1.1 mL), especially in patients with high lesion loads. MSLAST also showed good scan-rescan repeatability within the same session with an average absolute volume difference and F1 score of 0.38 +/- 0.32 mL and 84%, respectively. Conclusions We propose a new methodology to facilitate the segmentation of WM and cortical MS lesions at 7 T MRI, our approach uses a single MP2RAGE scan and may be of special interest to clinicians and researchers.

LIPPINCOTT WILLIAMS & WILKINS2019

Longitudinal analysis of white matter and cortical lesions in multiple sclerosis

Meritxell Bach Cuadra, Mario Joao Fartaria de Oliveira, Cristina Granziera, Tobias Kober

Purpose: The goals of this study were to assess the performance of a novel lesion segmentation tool for longitudinal analyses, as well as to validate the generated lesion progression map between two time points using conventional and non-conventional MR sequences. Material and methods: The lesion segmentation approach was evaluated with (LeMan-PV) and without (LeMan) the partial volume framework using "conventional" and "non-conventional" MR imaging in a two-year follow-up prospective study of 32 early RAMS patients. Manual segmentations of new, enlarged, shrunken, and stable lesions were used to evaluate the performance of the method variants. The true positive rate was estimated for those lesion evolutions in both white matter and cortex. The number of false positives was compared with two strategies for longitudinal analyses. New lesion tissue volume estimation was evaluated using Bland-Altman plots. Wilcoxon signed-rank test was used to evaluate the different setups. Results: The best median of the true positive rate was obtained using LeMan-PV with non-conventional sequences (P < .05): 87%, 87%, 100%, 83%, for new, enlarged, shrunken, and stable WM lesions, and 50%, 60%, 50%, 80%, for new, enlarged, shrunken, and stable cortical lesions, respectively. Most of the missed lesions were below the mean lesion size in each category. Lesion progression maps presented a median of 0 false positives (range:0-9) and the partial volume framework improved the volume estimation of new lesion tissue. Conclusion: LeMan-PV exhibited the best performance in the detection of new, enlarged, shrunken and stable WM lesions. The method showed lower performance in the detection of cortical lesions, likely due to their low occurrence, small size and low contrast with respect to surrounding tissues. The proposed lesion progression map might be useful in clinical trials or clinical routine.

2019

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis

Meritxell Bach Cuadra, Mario Joao Fartaria de Oliveira, João Pedro Forjaco Jorge, Cristina Granziera, Tobias Kober, Francesco La Rosa, Reto Meuli, Jonas Richiardi

The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limited by inter-rater variability and the expenditure of time associated with manual assessment. We describe a deep learning-based prototype for automated assessment of the CVS in white matter MS lesions using data from three different imaging centers. We retrospectively analyzed data from 3 T magnetic resonance images acquired on four scanners from two different vendors, including adults with MS (n = 42), MS mimics (n = 33, encompassing 12 distinct neurological diseases mimicking MS) and uncertain diagnosis (n = 5). Brain white matter lesions were manually segmented on FLAIR* images. Perivenular assessment was performed according to consensus guidelines and used as ground truth, yielding 539 CVS-positive (CVS+) and 448 CVS-negative (CVS-) lesions. A 3D convolutional neural network ("CVSnet") was designed and trained on 47 datasets, keeping 33 for testing. FLAIR* lesion patches of CVS+/CVS- lesions were used for training and validation (n = 375/298) and for testing (n = 164/150). Performance was evaluated lesion-wise and subject-wise and compared with a state-of-the-art vesselness filtering approach through McNemar's test. The proposed CVSnet approached human performance, with lesion-wise median balanced accuracy of 81%, and subject-wise balanced accuracy of 89% on the validation set, and 91% on the test set. The process of CVS assessment, in previously manually segmented lesions, was similar to 600-fold faster using the proposed CVSnet compared with human visual assessment (test set: 4 seconds vs. 40 minutes). On the validation and test sets, the lesion-wise performance outperformed the vesselness filter method (P < 0.001). The proposed deep learning prototype shows promising performance in differentiating MS from its mimics. Our approach was evaluated using data from different hospitals, enabling larger multicenter trials to evaluate the benefit of introducing the CVS marker into MS diagnostic criteria.

2020

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis

Meritxell Bach Cuadra, Mario Joao Fartaria de Oliveira, João Pedro Forjaco Jorge, Cristina Granziera, Tobias Kober, Francesco La Rosa, Reto Meuli, Jonas Richiardi

2020

Automated Detection and Segmentation of Multiple Sclerosis Lesions Using Ultra-High-Field MP2RAGE

Meritxell Bach Cuadra, Mario Joao Fartaria de Oliveira, Cristina Granziera, Tobias Kober

LIPPINCOTT WILLIAMS & WILKINS2019

Longitudinal analysis of white matter and cortical lesions in multiple sclerosis

Meritxell Bach Cuadra, Mario Joao Fartaria de Oliveira, Cristina Granziera, Tobias Kober

2019