LesionQuant
Overview
LesionQuant, developed by CorTechs Labs, is an artificial intelligence (AI) tool designed for the quantitative assessment of magnetic resonance imaging (MRI) scans, specifically in the context of multiple sclerosis (MS). It is intended for use by neurologists, neuroradiologists, and other clinicians involved in the diagnosis and monitoring of MS patients.
The tool integrates into clinical workflow by providing automated analysis of MRI data. It focuses on:
- Lesion Volume Measurement: Quantifying the total volume of lesions.
- Lesion Count: Identifying and counting individual lesions.
- Lesion Distribution: Analyzing the spatial arrangement of lesions within the brain.
- Brain Atrophy: Assessing changes in brain volume, which can be indicative of disease progression.
LesionQuant aims to supplement visual inspection of MRI scans by providing objective, quantitative measurements. This can potentially offer a more detailed and consistent evaluation of disease progression over time, aiding in treatment planning and patient management for individuals with multiple sclerosis.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Automated segmentation and volumetric analysis of brain structures
- Quantification of lesion volume, count, and distribution
- Comparison of current brain volumes and lesions to prior studies
- Identification and quantification of new, active, and resolving lesions
- Color-coded lesion visualization and lesion change visualization
- Brain atrophy assessment
- Normative reference data for comparison of patient brain volumes to healthy cohorts
- Customizable lesion detection settings and minimum lesion size threshold
- Compatibility with various MRI scanners (Siemens, GE, Philips, Toshiba, Hitachi)
- Comprehensive FLAIR lesion and brain structure volumetric reports
Use Cases
- Diagnosis and monitoring of multiple sclerosis
- Assessment of neurodegenerative disorders like Alzheimer's and dementia
- Evaluation of brain trauma
- Monitoring epilepsy
- Clinical treatment planning and monitoring of disease progression
- Quantitative analysis of brain lesions and atrophy in a clinical setting
What Physicians Need to Know
LesionQuant offers customizable lesion detection settings, allowing physicians to configure minimum lesion size (1 to 64 mmu00b3) and lesion separation thresholds (1 to 5 mm) to suit individual diagnostic needs and scanner protocols. Consistent scanner protocol settings and lesion settings are crucial for accurate longitudinal tracking of lesion changes. The software provides color-coded overlays to visualize lesion changes, distinguishing between new, active (enlarging/increasing intensity), stable, and resolving lesions. This can help monitor disease progression and patient response to treatment without the need for gadolinium contrast in some cases.
LesionQuant is designed for easy integration into existing PACS systems. It processes 3D T1 structural scans and 2D/3D FLAIR MR images. The system requires both the 3D T1 and FLAIR scans for a patient to have matching patient IDs and be acquired in the same session for processing to commence. Results, including color-coded FLAIR overlays, can be reviewed directly on PACS or DICOM viewers. Processing time for analysis is typically over 10 minutes.
Details
| Category | Neurology AI, Radiology & Imaging AI |
| Pricing | Unknown — Contact CorTechs Labs for pricing information. |
| Deployment | Cloud-based system |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
Unknown AI-estimated LesionQuant is a module of NeuroQuant, which received FDA 510(k) clearance for quantitative brain image analysis. It assists physicians in clinical evaluation, treatment planning, and monitoring disease progression by combining T2 FLAIR with 3D T1 scans for quantitative lesion analysis. |
| Integrations | |
| EHR | Not specified |
| Specialties | Neurology, Radiology |
What the Web Says
LesionQuant is an FDA-cleared and CE-marked AI-powered software module that assists physicians in quantifying and tracking brain lesions and atrophy, particularly useful in conditions like Multiple Sclerosis (MS) and dementia. It provides automated FLAIR lesion and brain volume measurements, compares them to normative data, and visualizes changes over time. The software aims to offer more accurate and reproducible assessments compared to traditional visual inspection or manual segmentation, which can be subjective.
Overall: PositiveStrengths
- Provides fast, accurate, and reproducible automated FLAIR lesion and brain volume measurements.
- Helps in proficiently assessing disease activity in MS by identifying new and enlarging T2 FLAIR lesions.
- Offers visualization of lesion changes and brain structure changes when prior studies are available.
- Can aid in the assessment of patients with dementia by determining atrophy in specific brain regions, especially in early stages.
- Reduces inter- and intra-rater variability often associated with manual lesion evaluation.
- Allows for user-configured lesion segmentation parameters, such as minimum lesion size and separation, for personalized analysis.
Limitations
- Requires both 3D T1 and 2D/3D FLAIR MRI series for processing, and patient IDs must match with both series acquired in the same session.
- Some users on Reddit express general skepticism about AI tools for MRI analysis, citing concerns about accuracy in distinguishing normal anatomical differences from lesions and potential privacy issues with web uploads.
- One Reddit user reported that an AI analysis (NeuroQuant, which LesionQuant is a module of) showed lesions that their neurologist hadn't mentioned, leading to confusion.
- Subtle differences in structural volumes might occur when compared to T1-only segmentations due to the additional T2 FLAIR data used by LesionQuant.
- It is critical that scanner protocol settings and lesion settings remain consistent between time points for accurate detection of subtle lesion changes.
Based on reviews from: Cortechs.ai, Neurology, InventUM, PubMed, medRxiv, Reddit
Last updated: 2026-09-11
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