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
  • M. Bruun et al. Detecting frontotemporal dementia syndromes using MRI biomarkers. NeuroImage Clinical 22: 101711, 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 10, 509-518, 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 11: 435-449, 2016.
  • Lötjönen et al. Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer’s disease. NeuroImage 56: 185-196, 2011.
  • Koikkalainen et al. Multi-Template Tensor-Based Morphometry: Application to Analysis of Alzheimer’s Disease, NeuroImage 56: 1134-1144, 2011.
  • Wolz et al. Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer’s Disease. PLoS One 6(10):e25446, 2011. Epub 2011 Oct 13.
  • Lötjönen et al. Fast and robust multi-atlas segmentation of brain magnetic resonance images. NeuroImage 49: 2352-2365, 2010.

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.