Comprehensive protocols with excellent AI-enhanced image quality in less than 5 minutes.
Fast transformation of the detailed imaging data into clinical insights for confident triage and diagnosis.
See more clearly with high-definition imaging. Save time with multi-modality one room solution.
Risk assessment and preventative therapy guidance through identification of hemodynamic and carotid plaque features associated with rupture risk.
“For a 3D FLAIR, with no AiCE we were around 5 minutes 50 seconds. With AiCE, we are at 2 minutes 51 seconds. We saved a lot of time, thanks to the denoising, also the resolution is better. You see that the differentiation between white and gray matter is better and the signal of the white matter is more homogeneous.”
Dr. Benoît Doche de Laquintane, MD
Radiologist
Medical Imaging group IMAGIR
Bordeaux, France![]()
Learn more about Automation Platform More evidence for Healthcare IT
“We anticipate a faster and improved workflow in acute stroke imaging with the Automation Platform, as well as support for the radiologist in making the correct diagnosis and to aid the clinicians in treatment decision-making.”
Dr. Anton Meijer, MD, PhD
Neuroradiologist,
Radboud University Medical Center, Nijmegen, the Netherlands![]()
Learn more about Alphenix 4D CT Watch the workflow video More evidence for Angiography
“All those things which the device does that were impossible to see with conventional flat panel detectors become overly abundant.”
Adnan Siddiqui MD, PhD, FAHA
Vice- Chairman and Professor of Neurosurgery
SUNY University at Buffalo
Director, Neurosurgical Stroke Services, Kaleida Health
CEO&CMO, Jacobs Institute![]()
“SMI may be a useful tool in bed-side assessment of IPN with a significant advantage over CEUS of not requiring an intravenous contrast injection, allowing for easier use in routine clinical practice.”
Dr. Mahtab Zamani, MD
Neurology Resident
Oslo University Hospital Rikshospitalet, Norway![]()
Disclaimer
For the AI technology described in this website, deep learning technology is used in the design stage. The systems themselves do not have self-learning capabilities.
© Canon Medikal Sistemler Turkey A.Ş.
© Canon Medikal Sistemler Turkey A.Ş.