icobrain cva


icobrain cva allows the automated quantitative assessment of tissue perfusion by:
- reporting the volume of core and perfusion lesion by quantifying Tmax abnormality and CBF abnormality together with the mismatch volume and ratio,
- providing an AIF/VOF graph with information on the correctness of the selected arterial input function and the quality of the report,
- providing insights into the perfusion state of the tissue through perfusion maps.
icobrain cva is a cloud-based solution, returning a report and perfusion maps into the PACS and e-mail inbox.
Product specifications Information source: Vendor
Last updated: March 12, 2021
Product name icobrain cva
Company icometrix
Subspeciality Neuro
Modality CT
Disease targeted stroke
Key-features volume of core and perfusion lesion, mismatch ratio, perfusion maps, AIF/VOF graph
Suggested use Before: flagging acute findings, During: perception aid (prompting all abnormalities/results/heatmaps)
Data characteristics
Population all with suspicion of stroke
Input CT perfusion
Input format DICOM
Output Color-coded segmentation overlays, concise reports with reference statistics.
Output format DICOM
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform
Deployment Cloud-based
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 1 - 10 minutes
Certified, Class I
510(k) cleared, Class II
Market presence
On market since 2020
Distribution channels Blackford, GE Edison marketplace, Nuance AI marketplace
Countries present (clinical, non-research use) 23
Paying clinical customers (institutes) > 300
Research/test users (institutes) > 100
Pricing model Subscription
Based on Number of analyses
Peer reviewed papers on performance
Non-peer reviewed papers on performance

  • AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning. (read)

Other relevant papers

  • Abstract: Deep Learning Based Prediction of Tissue Status From Native CT Perfusion Images (read)