ClariCT.AI is an AI-powered CT image denoising solution to provide increased image clarity in CT examinations with excessive image noise due to either low dose or large patient. It is trained to work in a vendor-agnostic way, to reduce noise and enhance image quality for low-dose and ultra-low-dose DICOM CT images. ClariCT.AI separates image noise selectively while enhancing underlying structures; thus, providing clarity restored images.
Product specifications Information source: Vendor
Last updated: Oct. 3, 2023
Product name ClariCT.AI
Company ClariPi Inc.
Subspeciality Neuro, Cardiac, MSK, Chest, Abdomen
Modality CT
Disease targeted N/A
Key-features CT Image denoising for any body part
Suggested use During
Data characteristics
Population No restrictions
Input CT, contrast or non-contrast
Input format DICOM
Output Enhanced series with ClariCT.AI tag
Output format DICOM
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), 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 10 - 60 seconds
Certified, Class IIa , MDD
510(k) cleared, Class II
Market presence
On market since 06-2019
Distribution channels Siemens Syngo.Via, Nuance PIN, Blackford, Eureka Clinical AI, deepcOS,
Countries present (clinical, non-research use) 44
Paying clinical customers (institutes) 45
Research/test users (institutes) 12
Pricing model Pay-per-use, Subscription, One-time license fee
Based on Number of installations, Number of analyses
Peer reviewed papers on performance

  • Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction (read)

  • Effect of a novel denoising technique on image quality and diagnostic accuracy in low-dose CT in patients with suspected appendicitis (read)

  • Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques (read)

  • Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions (read)

  • Application of Vendor-Neutral Iterative Reconstruction Technique to Pediatric Abdominal Computed Tomography (read)

  • Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction (read)

  • Impact of image denoising on image quality, quantitative parameters and sensitivity of ultra-low-dose volume perfusion CT imaging (read)

Non-peer reviewed papers on performance
Other relevant papers

  • SPIE conference proceedings 2020: Combined low-dose simulation and deep learning for CT denoising: application of ultra-low-dose cardiac CTA (read)