Vendor source
Reviewed 15 Jul 2026
Completeness Comprehensive (81%)
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.
Supported by 11 papers





General Information
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Company
Modality
Main task
Not specified
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Disease targeted
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Regulatory Information
Pathway:
MDD
Class:
Class IIa
Verified by Health AI Register
Pathway:
510(k) cleared
Class:
Class II
Verified by Health AI Register
Other certifications
Not specified
Evidence & Research
Peer-Reviewed Papers
Deep-learning denoising for ultrahigh-resolution photon-counting detector CT: phantom and in vivo evaluation of non-calcified coronary plaques
The International Journal of Cardiovascular Imaging · Jul 2026
Deep Learning Denoising Algorithm for Improved Assessment of Coronary Arteries in Transcatheter Aortic Valve Implantation CT Imaging
Academic Radiology · Nov 2025
Diagnostic performance of lumbar spine CT using deep learning denoising to evaluate disc herniation and spinal stenosis
European Radiology · Jun 2025
The Impact of AI on Metal Artifacts in CBCT Oral Cavity Imaging
Diagnostics · Jun 2024
Noise-Optimized CBCT Imaging of Temporomandibular Joints—The Impact of AI on Image Quality
Journal of Clinical Medicine · Mar 2024
Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions
European Radiology · Nov 2021
Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques
European Radiology · Jan 2021
Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction
Korean Journal of Radiology · Jul 2020
Application of Vendor-Neutral Iterative Reconstruction Technique to Pediatric Abdominal Computed Tomography
Korean Journal of Radiology · Aug 2019
Effect of a novel denoising technique on image quality and diagnostic accuracy in low-dose CT in patients with suspected appendicitis
European Journal of Radiology · May 2019
Impact of image denoising on image quality, quantitative parameters and sensitivity of ultra-low-dose volume perfusion CT imaging
European Radiology · May 2015
Technical Specifications
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Source: Vendor | First published: May 2, 2024 | Last updated: Jul 15, 2026