State-of-the-art Imaging Biomarkers
for Multiple Sclerosis
QMENTA has amassed the most advanced and widely used imaging biomarkers on its platform to enable experts to measure the efficacy of treatments under trial or research in a single environment.
Visual detection, categorization, and segmentation of white matter lesions is extremely time consuming and variable between human readers.Determining the location and spread of lesions throughout the brain is critical to diagnose and monitor disease progression.
Lesion Volume & Lesion Count
Demyelination, oedema and gliosis extend MRI T2 relaxation time. Hyperintensities in fluid-attenuated inversion recovery (FLAIR) images indicate MS lesion presence. Automated algorithms segment lesions reproducibly, reducing inter-rater bias. In large projects such as clinical trials, hundreds of datasets can be processed simultaneously using parallel computing, saving time and money.(1)
Gray Matter Volume & Thickness Quantification
Patients with the main clinical phenotypes of multiple sclerosis (MS) manifest varying degrees of gray matter atrophy beyond that of normal aging(5). The QMENTA platform’s gray matter volumetry tools allow for quantification of region-specific gray matter volume for each patient timepoint using processing tools such as FreeSurfer, ANTs and SIENAX. The platform’s data management allows longitudinal assessment of gray matter atrophy.
Longitudinal Lesion Activity
Lesion volume change, number of new lesions, number of enlarging lesions can be calculated between timepoints using an automated algorithm to assist in analysis of progression or remission.(2)
Diffusion Measures Around Lesions
Diffusion measurements around lesions have been linked to re-myelination and demyelination, offering a biomarker to measure the effect of new therapies on MS.(3,4)
Lesion Load Measures on Main Fiber Bundles
Tractography allows the spatial delineation of major white matter structures within the brain, combining this information with lesion segmentation from T2 weighted hyperintensities allows the derivation of structurally specific lesion load measures.
1. Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., ... & Hemmer, B. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage, 59(4), 3774-3783.
2. Egger, Christine, et al. "MRI FLAIR lesion segmentation in multiple sclerosis: Does automated segmentation hold up with manual annotation?." NeuroImage: Clinical 13 (2017): 264-270.
3. Klistorner, Alexander, et al. "Decoding diffusivity in multiple sclerosis: analysis of optic radiation lesional and non-lesional white matter." PLoS One 10.3 (2015): e0122114.
4. Klistorner, Alexander, et al. "Diffusivity in multiple sclerosis lesions: At the cutting edge?." NeuroImage: Clinical 12 (2016): 219-226.
5. Rocca, M. A. et al. (2017). Brain MRI atrophy quantification in MS. Neurology, 88(4), 403–413. https://doi.org/10.1212/WNL.0000000000003542