cNeuro® cMRI
Fully automated solution for quantifying MR brain scans.
cNeuro® cMRI is a tool for quantitative assessment of brain images providing clinical decision support in neurological disorders.
HIGHLIGHTS
- Cloud-based tool running in a standard browser.
- Quantitative information about volumes of brain
structures, and computed MTA & GCA. - Quantitative assessment of vascular burden including
computed Fazekas. - Statistical comparison with a large reference database
of cognitively normal subjects. - Interactive viewing of images and results.
- Easy reporting.
MRI brain imaging is a critical tool in assessing patients with neurodegenerative diseases. Using visual assessment alone, it is difficult to identify subtle changes in brain atrophy and to identify patterns of atrophy that may be associated with different types of dementia. This can be overcome by the use of quantitative information. cMRI assists in accurate and consistent quantitative evaluation of MRI brain scans and presents the results in a report that is designed for referring physicians.
FEATURES
- Cloud-based – software runs in a standard web browser.
- Computation of volumes of 102 cortical and 31 subcortical regions from T1 images.
- Computation of vascular burden from FLAIR images.
- Computation of MTA, GCA (global & lobes) and Fazekas (EU only).
- Computation of relative atrophy between anterior and posterior regions.
- Interactive review of images with the ability to toggle overlays on/off for easy assessment of segmentation results.
- Visualization of gray-matter concentration map supporting assessment of brain atrophy.
- Quantitative information corrected for age, gender and head size.
- Quantitative results presented in tabular form or as age and gender corrected plots.
- Summary report for streamlined communication with patients and referring physicians.
Structural segmentation from T1 images
Vascular changes based on FLAIR images
List of publications
SELECTED PUBLICATIONS RELEVANT TO cMRI
- K. Hänninen et al. Thalamic atrophy predicts 5-year disability progression in multiple sclerosis. Frontiers in Neurology, 2020.
- https://doi.org/10.3389/fneur.2020.00606L. Gjerum et al. Addition of FDG-PET in diagnosing dementia using a data-driven decision model. NeuroImage Clinical, 2020. https://doi.org/10.1016/j.nicl.2020.102267
- H. Jokinen et al. Global burden of small vessel disease-related brain changes on MRI predicts cognitive and functional decline. Stroke, 2020. https://doi.org/10.1161/STROKEAHA.119.026170
- K. Hänninen et al. Thalamic atrophy without whole brain atrophy is associated with absence of 2-year NEDA in multiple sclerosis. Frontiers in Neurology, 2019. https://doi.org/10.3389/fneur.2019.00459
- M. Bruun et al. Detecting frontotemporal dementia syndromes using MRI biomarkers. NeuroImage Clinical, 2019. https://doi.org/10.1016/j.nicl.2019.101711
- Koikkalainen et al. Automatically computed rating scales from MRI for patients with cognitive disorders. European Radiology, 2019. https://doi.org/10.1007/s00330-019-06067-1
- Rhodius-Meester et al. Computer-assisted prediction of clinical progression in the earliest stages of AD. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 2018. https://www.sciencedirect.com/science/article/pii/S2352872918300666
- Bruun et al. Evaluating combinations of diagnostic tests to discriminate different dementia types. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 2018. https://doi.org/10.1016/j.dadm.2018.07.003
- Koikkalainen et al. Differential diagnosis of neurodegenerative diseases using structural MRI data. NeuroImage Clinical, 2016. https://doi.org/10.1016/j.nicl.2016.02.019
- Lötjönen et al. Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer’s disease. NeuroImage 56, 2011. https://doi.org/10.1016/j.neuroimage.2011.01.062
- Lötjönen et al. Fast and robust multi-atlas segmentation of brain magnetic resonance images. NeuroImage, 2010. https://doi.org/10.1016/j.neuroimage.2009.10.026
Security
All data transfer uses SSL encryption. Stored data are encrypted and anonymized.
More information is available in a separate security statement.
System Requirements
Supported web browsers:
- Google Chrome 61 or later
- Firefox 56 or later
- Internet Explorer 11 or later.
Recommended display resolution 1680 x 1050 or higher.
Regulatory Compliance
CE marked and FDA 510(k) cleared.
Indications for Use
cNeuro cMRI is intended for automatic labeling, quantification and visualization of segmentable brain structures from a set of MR images. The software is intended to automate the current manual process of identifying, labeling and quantifying the segmentable brain structures identified on MR images. The intended user profile covers medical professionals who work with medical imaging. The intended operational environment is an office-like environment with a computer.