Vendor source

Reviewed 15 Jul 2026

Completeness Comprehensive (81%)

ClariCT.AI

ClariPi Inc.

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

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Key Features

  • CT Image denoising for any body part

General Information

Product name

ClariCT.AI

Modality

CT

Main task

Not specified

Subspeciality

Multiple

Disease targeted

Not specified

Regulatory Information

CE Certification

Pathway:

MDD

Class:

Class IIa

Verified by Health AI Register

FDA Certification

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

Patient population age

Not specified

Input

CT, contrast or non-contrast

Input format

DICOM

Output

Enhanced series with ClariCT.AI tag

Output format

DICOM

Integration

Integration CIS (Clinical Information System), Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application

Deployment

Cloud-based, Locally on dedicated hardware, Locally virtualized (virtual machine, Docker)

Trigger for analysis

Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload

Processing time

10 - 60 seconds

Population

No restrictions

Market Presence

On market since

06-2019

Resellers

Not specified

Countries present

44

Paying clinical customers

45

Research/test users

12

Pricing Information

Pricing model

One-time license fee, Subscription

Based on

Number of analyses, Number of installations

Source: Vendor | First published: May 2, 2024 | Last updated: Jul 15, 2026