BMAT: An open-source BIDS managing and analysis tool

Magnetic Resonance Imaging (MRI) is an established technique to study in vivo neurological disorders such as Multiple Sclerosis (MS). To avoid errors on MRI data organization and automated processing, a standard called Brain Imaging Data Structure (BIDS) has been recently proposed. The BIDS standard eases data sharing and processing within or between centers by providing guidelines for their description and organization. However, the transformation from the complex unstructured non-open file data formats coming directly from the MRI scanner to a correct BIDS structure can be cumbersome and time consuming. This hinders a wider adoption of the BIDS format across different study centers. To solve this problem and ease the day-to-day use of BIDS for the neuroimaging scientific community, we present the BIDS Managing and Analysis Tool (BMAT). The BMAT software is a complete and easy-to-use local open-source neuroimaging analysis tool with a graphical user interface (GUI) that uses the BIDS format to organize and process brain MRI data for MS imaging research studies. BMAT provides the possibility to translate data from MRI scanners to the BIDS structure, create and manage BIDS datasets as well as develop and run automated processing pipelines, and is faster than its competitor. BMAT software propose the possibility to download useful analysis apps, especially applied to MS research with lesion segmentation and processing of imaging contrasts for novel disease biomarkers such as the central vein sign and the paramagnetic rim lesions

FLAIR3Phase: a new synthetic MRI contrast for paramagnetic rim lesions detection in multiple sclerosis

Chronic active multiple sclerosis (MS) lesions are visible on susceptibility-based MRI as paramagnetic rim lesions (PRL); these lesions are characterized by severe tissue damage and strongly correlate with disease severity. PRL assessment is usually performed manually on unwrapped-filtered phase images. However, PRL assessment is associated with high intra/inter-rater variability due to the poor visibility of PRL and to the lack of standardized imaging protocols/guidelines for their detection. Here, we propose a new synthetic contrast called FLAIR3Phase to augment PRL assessment workflow and to reduce rater variability

ADVANCED DIFFUSION IMAGING METHODS TO ASSESS THE MICROSTRUCTURE OF MULTIPLE SCLEROSIS LESIONS

Paramagnetic Rim Lesions (PRL) in Multiple Sclerosis (MS) are associated with increased disability and axonal damage. We examined PRL microstructure using Track Density Imaging (TDI) and four diffusion MRI (dMRI) models, including Microstructure Fingerprinting (MF). When compared to non-PRL, PRL showed larger size, higher T1-values, lower neurite density index and fibre volume fraction on dMRI (p < 0.001) and reduced track density on TDI (p < 0.0001), suggesting impaired axonal density/integrity in PRL. Results obtained with the novel MF model are in line with those obtained with other dMRI models, indicating its potential for studying MS lesion pathology

ADVANCED DIFFUSION IMAGING METHODS TO ASSESS THE MICROSTRUCTURE OF MULTIPLE SCLEROSIS LESIONS

Paramagnetic Rim Lesions (PRL) in Multiple Sclerosis (MS) are associated with increased disability and axonal damage. In this work, we explored PRL's microstructure using Track Density Imaging (TDI) and four diffusion MRI (dMRI) models, including Microstructure Fingerprinting (MF). When compared to non-PRL, PRL showed larger size, higher T1-values, lower neurite density index and fibre volume fraction on dMRI (p < 0.001) and reduced track density on TDI (p < 0.0001), suggesting impaired axonal density/integrity in PRL. Results obtained with the novel MF model are in line with those obtained with other dMRI models, indicating its potential for studying MS lesion pathology

Shrinking multiple sclerosis lesions are characterized by a more destructive phenotype than expanding lesions

Motivation Slowly expanding lesions (SEL) have gained significant attention as a biomarker of chronic active multiple sclerosis (MS) lesions, however, one study1 suggests that all MS lesions tend to shrink over a long period of time. Goal To investigate the microstructure of expanding lesions (EL), shrinking lesions (SL), and stable lesions. Approach EL and SL were computed using deformation-based volumetric MRI and microstructure was investigated using quantitative T1 and multi-shell diffusion MRI. Results SL showed a more destructive phenotype at baseline when compared to EL, while stable lesions were considerably less destructive

A multi-compartment fingerprinting model for non-invasive tumor cell characterization via diffusion MRI

Brain tumor tissue characteristics are important for treatment planning but are nowadays often recovered via invasive biopsy analysis. This work attempts to characterize the microstructural properties of tumor cells using diffusion MRI and Monte Carlo simulations to build a dictionary composed of several fingerprints, combining both a representation of axonal fibers and tumor cells. We demonstrate the use of our method on in-vivo brain data as a reliable estimation of tumor cell properties

Tractography of the subcortical U-fibers using a position-dependent maximum angle

Short association fibers in the subcortical white matter, also known as U-fibers, represent the connections between neighboring gyri. Due to the geometry of the cortical folds and the sharp turns along the cortical surface, tractography of U-fibers remains a challenge since increasing the maximum angle between tractography steps also increases the occurence of false positive streamlines. We propose to replace the fixed maximum angle value in tractography algorithms by an angular map, allowing higher angles at the interface between grey and white matter. This enables a more accurate tracking of U-fibers, while keeping a low number of false positive streamlines

SYNTHETIC DIR/PSIR CONTRASTS COMPUTED WITH CLINICAL SEQUENCES FOR CORTICAL LESION ASSESSMENT

Cortical lesions (CL) serve as a valuable biomarker for im- proving the differential diagnosis[1] of multiple sclerosis (MS) and are associated with disease severity[2]. However, detecting CL in vivo remains challenging, particularly in clinical settings, due to their small size and their tendency to affect the more superficial, less myelinated layers of the cortex[3]. While specialized 3T MRI sequences such as dou- ble inversion recovery (DIR) and phase-sensitive inversion recovery (PSIR) have improved CL detection compared to using conventional sequences like fluid-attenuated inversion recovery (FLAIR) or magnetization-prepared rapid gradient echo (MPRAGE)[2], their use in clinical settings is hindered by their long acquisition time. In this study, we aim to propose synthetic DIR (sDIR) and synthetic PSIR (sPSIR) contrasts computed with clinical sequences commonly found in MS protocols to improve CL detection in clinical settings. The use of sDIR and sPSIR allowed to significantly (p < 0.01) increase the number of CL detected (8.237 ± 10.559) when compared to using FLAIR and MPRAGE sequences (4.5±5.658). No statistical difference (p = 0.538) was found between the number of CL detected using sDIR and sPSIR when compared to using advanced DIR and PSIR sequences (respectively, 4.667 ± 6.03 vs. 3.533 ± 3.16). In conclusion, this work proposes a simple and explainable method for generating synthetic contrasts using clinical se- quences commonly found in MS protocols, achieving similar CL detection rates as using advanced research DIR and PSIR sequences. This approach holds promises for expanding the utilization of CL as a valuable biomarker of both MS diagno- sis and prognosis in routine clinical practice

Towards Integration of the Central Vein Sign, Cortical Lesions and Paramagnetic Rim Lesions into Multiple Sclerosis Diagnosis: Insights from Explainable Machine Learning

Current differential diagnosis dissemination in space (DIS) McDonald criteria (1) tend to favor sensitivity over specificity (2). This leads to early detection of the disease (3) but also increases the risk of incorrectly diagnosing patients with MS-mimicking diseases (4). New advanced MRI biomarkers - the central vein sign (CVS), cortical lesions (CLs), and paramagnetic rim lesions (PRLs) show promising high specificity for MS. However, the full-count assessment of these advanced biomarkers is time-consuming and often incompatible with clinical practice. Objectives Using machine learning (ML), we investigate: • the incorporation of CVS, CLs, and PRLs to improve MS differential diagnosis sensitivity-specificity trade-off • the diagnostic performance of more practical, simplified advanced biomarker assessments Finally, the publication of an online tool allows the interaction with trained ML models. Conclusions • CVS emerges as the most diagnostically powerful biomarker • ML models combining CVS, CL, and PRL show the highest MS diagnostic performance and clearly outperform the current MS DIS diagnostic criteria. • Simplified assessments are competitive against full-count assessments, considerably reducing the time burden associated with image analysis