Specialized Imaging Biomarkers for Multiple Sclerosis

Visual Detection and Categorization of White Matter Lesions

The process of visually detecting, categorizing, and segmenting white matter lesions can be highly time-consuming and inconsistent among readers, yet it's essential in tracking neurological conditions like multiple sclerosis. To aid this process, the QMENTA platform offers a highly configurable viewer with various capabilities such as quality checking, ROI drawing, label editing, annotations, synchronized volumes, and visualization of dynamic sequences. By integrating web viewers with diverse options, the platform not only saves time but also promotes greater standardization, making it an invaluable tool for medical professionals.

Automated Lesion Deliniation

Demyelination, oedema, and gliosis prolong MRI T2 relaxation time, indicative of MS lesion presence in FLAIR images. Automated algorithms enable reproducible segmentation of lesions and reduce bias.⁽¹⁾

T1 Weighted Gadolinium-Enhanced (Gd+ T1) Lesion

Gadolinium-based contrast agents in T1-weighted MRI identify active Multiple Sclerosis (MS) lesions, key in diagnosing and monitoring MS by revealing Blood-Brain Barrier dysfunction and inflammation.^(6,7)

T2 Weighted Lesion Detection

Standard sequences like T2-weighted and FLAIR are used to identify MS lesions, resulting in a hyperintense appearance. These lesions are key in diagnosing MS and assessing therapeutic efficacy in clinical trials. ^(6,8)

Combined Unique Active (CUA) MRI Lesions

A CUA lesion refers to a new or changing gadolinium-enhancing (Gd+ T1) lesion or T2-weighted images, without duplication. It serves as a significant biomarker for relapsing and progressive MS trials.^(9,10)

Changes in Lesion Count and Lesion Volume

New and enlarging lesions are quantified over time, allowing calculation of lesion volume change, number of new or enlarging lesions. An automated algorithm aids in analyzing progression or remission.⁽²⁾

Detection of Central Vein lesions

Current MRI-based diagnostic criteria for MS are imperfect, leading to misdiagnosis in 1 in 5 cases. The central vein sign (CVS) and FLAIR*(a combination of FLAIR and T2*), extensively studied, may increase diagnostic specificity for MS. ⁽¹¹⁾

Quantitative Susceptibility Mapping (QSM)

A subset of lesions in MS, characterized by a paramagnetic rim from iron-laden microglia, negatively impacts clinical outcomes. Iron-sensitive techniques like gradient echo (GRE) MRI and quantitative susceptibility mapping (QSM) are crucial for detecting and monitoring these lesions.⁽¹²⁾

Changes in Brain Volume as detected by MRI

Patients with multiple sclerosis (MS) experience varying degrees of gray matter atrophy beyond normal aging. The QMENTA platform's tools enable quantification of region-specific atrophy and thickness, utilizing processing tools like FreeSurfer, ANTs, and SIENAX, allowing longitudinal assessment of gray matter atrophy.⁽⁵⁾

Diffusion Measures Around Lesions

Diffusion measurements around lesions have been associated with both remyelination and demyelination, providing a biomarker to gauge the impact of new therapies on MS.^(3,4)

Lesion Load Measures on Main Fiber Bundles

Tractography enables the spatial delineation of major white matter structures within the brain. By combining this information with lesion segmentation from T2-weighted hyperintensities, it is possible to derive structurally specific lesion load measures.

AI IMAGING BIOMARKERS

Explore our comprehensive range of specialized tools

All within one convenient and user-friendly interface

LEARN MORE

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. Image source: Caroline Köhler, et al. “Exploring individual multiple sclerosis lesion volume change over time: Development of an algorithm for the analyses of longitudinal quantitative MRI measures.” NeuroImage: Clinical (2019).
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.Image source: Huang SY, at al. “Characterization of Axonal Pathology Independent of Fiber Crossings in Multiple Sclerosis Using High-Gradient Diffusion MRI.”(2016)
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
6. Hemond, Christopher C., and Rohit Bakshi. "Magnetic resonance imaging in multiple sclerosis." Cold Spring Harbor perspectives in medicine 8, no. 5 (2018): a028969.
7. Coronado, Ivan, Refaat E. Gabr, and Ponnada A. Narayana. "Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis." Multiple Sclerosis Journal 27, no. 4 (2021): 519-527.
8. Kazancli, Erol, Vesna Prchkovska, Paulo Rodrigues, Pablo Villoslada, and Laura Igual. "Multiple Sclerosis Lesion Segmentation using Improved Convolutional Neural Networks." In VISIGRAPP (4: VISAPP), pp. 260-269. 2018.
9. Moccia, Marcello, Nicola de Stefano, and Frederik Barkhof. "Imaging outcome measures for progressive multiple sclerosis trials." Multiple Sclerosis Journal 23, no. 12 (2017): 1614-1626.
10. Giorgio, Antonio, Marco Battaglini, Giordano Gentile, Maria Laura Stromillo, Claudio Gasperini, Andra Visconti, Andrea Paolillo, and Nicola De Stefano. "Mapping the progressive treatment-related reduction of active MRI lesions in multiple sclerosis." Frontiers in Neurology 11 (2020): 1466. Image source: Giorgio, Antonio et al. “Mapping the Progressive Treatment-Related Reduction of Active MRI Lesions in Multiple Sclerosis.” Frontiers in Neurology (2020).
11. Ontaneda, D., P. Sati, P. Raza, M. Kilbane, E. Gombos, E. Alvarez, C. Azevedo et al. "Central vein sign: A diagnostic biomarker in multiple sclerosis (CAVS-MS) study protocol for a prospective multicenter trial." NeuroImage: Clinical 32 (2021): 102834. Source: Sati, P. et al. “The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative.” Nat Rev Neurol (2016). 12. Absinta M, Sati P, Masuzzo F, et al. Association of Chronic Active Multiple Sclerosis Lesions With Disability In Vivo. JAMA Neurol. 2019;76(12):1474–1483.

MS Imaging Biomarkers