Canon Medical Systems AiCE (Advanced intelligent Clear-IQ Engine)

by Canon Medical Systems  · Based in Japan → — Advanced intelligent Clear-IQ Engine (AiCE): Sharp, Clear and Distinct Images at Low Dose.
Neurology Nuclear Medicine Radiology

Contact for pricing
Regulatory Status Disclosed

Overview

Canon Medical Systems’ Advanced intelligent Clear-IQ Engine (AiCE) is a Deep Learning Reconstruction (DLR) technology designed to improve image quality across multiple diagnostic modalities. Utilizing a Deep Convolutional Neural Network (DCNN), AiCE is trained to differentiate signal from noise, allowing it to suppress image noise while preserving anatomical details, spatial resolution, and low-contrast detectability.

AiCE is built for radiologists, nuclear medicine physicians, and imaging technologists working in hospitals, imaging centers, and specialized clinics. The technology is integrated directly into Canon Medical’s diagnostic systems, including computed tomography (CT), magnetic resonance (MR), and positron emission tomography-computed tomography (PET-CT) platforms.

In a clinical workflow, AiCE operates seamlessly during the image reconstruction phase. By automating the denoising process, it fits into routine scanning protocols without requiring manual post-processing. This integration supports clinical operations by enabling high-resolution imaging and helping clinical teams manage scan times and radiation or tracer doses.

Notable Capabilities:

  • Multi-Modality Compatibility: Available across CT, MR, and PET-CT imaging systems.
  • Deep Learning Denoising: Restores low signal-to-noise ratio (SNR) data to match the properties of high-SNR acquisitions.
  • Workflow Automation: Reconstructs images quickly and automatically to maintain rapid patient throughput.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Deep Learning Reconstruction (DLR)
  • Deep Convolutional Neural Network (DCNN)
  • Noise reduction and signal boosting
  • Improved low-contrast detectability
  • Enhanced spatial resolution
  • Reduced radiation dose (for CT and PET)
  • Faster scan and reconstruction times
  • Improved Signal-to-Noise Ratio (SNR)
  • Natural image texture
  • Integrated and easy workflow

Use Cases

  • Routine CT imaging with dose reduction
  • High-resolution CT imaging
  • Advanced MRI for various anatomies (brain, knee, prostate, shoulders, joints, cardiac, pelvis, abdomen, spine)
  • High-quality PET imaging with improved SNR and quantification accuracy
  • Cardiac imaging with improved temporal resolution
  • Musculoskeletal (MSK) imaging

What Physicians Need to Know

DICOM Support & Standards
Canon Medical Systems products, including those with AiCE, are designed to comply with DICOM and IHE standards to facilitate integration within hospital networks. DICOM Conformance Statements are available to provide details on supported DICOM Service Classes, Information Objects, and Communication protocols.
PACS Integration Method
AiCE is integrated directly into the CT system's scan protocols, functioning as the reconstruction engine. This means it operates as an all-in-one solution with the local CT scanner, without requiring direct connection to remote supercomputers or cloud-based processing for reconstruction. This integration aims to streamline workflows and reduce the number of series radiologists need to open and close, potentially saving PACS data storage.
Reading Room Workflow Impact
AiCE seamlessly integrates into routine practice, delivering high-quality images without requiring extra steps from radiologists. It is built directly into scan protocols, meaning it's 'on all the time' and applied to every examination, simplifying workflow. The technology aims to provide clearer, more precise images, enhancing diagnostic confidence and potentially improving patient outcomes.
AI Model Architecture (deep learning approach)
AiCE utilizes a Deep Learning Reconstruction (DLR) technology based on deep convolutional neural networks (DCNN). The DCNN is trained in the factory using vast amounts of high-quality image data, including 'gold-standard' reference images acquired with high tube current and reconstructed with advanced Model-Based Iterative Reconstruction (MBIR). This training teaches the network to differentiate true signal from noise, suppress noise, and enhance signal.
Processing Speed (per study)
AiCE offers fast reconstruction speeds. For a Prime SP scanner, the reconstruction speed for a soft tissue reconstruction (216mm range) is 33 seconds, compared to 20 seconds with AIDR 3D. This difference is considered negligible in routine clinical practice and does not affect patient throughput. AiCE DLR is also noted to work at over five times the speed of MBIR reconstruction for Ultra-High Resolution CT.
FDA Clearance Pathway (510k/De Novo)
AiCE has received 510(k) clearance from the FDA for various modalities and systems. This includes clearance for CT systems like Aquilion Precision and Aquilion ONE / GENESIS Edition (October 2019), for the Vantage Galan 3T MR system (March 2020), and for the Cartesion Prime Digital PET/CT system (June 2021).
Supported Modalities (CT/MRI/X-ray/US)
AiCE is available across multiple modalities, including Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). It is integrated into a broad portfolio of Canon Medical's CT and MR scanners. AiCE for PET is available on the Cartesion Prime Digital PET/CT system.
RSNA/ACR Validation
Canon Medical has showcased AiCE at RSNA annual meetings, highlighting its expansion across modalities and clinical indications. AiCE has been compared to other deep learning reconstruction algorithms in studies that reference ACR CT accreditation phantoms for validation of image quality metrics.
Physician Tip

AiCE can significantly improve image quality by reducing noise and enhancing signal, potentially leading to increased diagnostic confidence and the ability to visualize subtle anatomical details. The technology is integrated into routine scan protocols, minimizing workflow disruption. Consider leveraging AiCE to potentially reduce radiation dose in CT while maintaining or improving image quality, and to achieve high-resolution MR images within routine scan times. The 'AiCE Challenge' suggests that 1.5T MR images with AiCE can be comparable to traditional 3T images, which might influence scanner utilization strategies.

Canon Medical Systems emphasizes that AiCE is a fully integrated solution within their CT, MR, and PET/CT systems, designed to work locally on the scanner without requiring external cloud connectivity or remote supercomputers for reconstruction. This on-device processing ensures seamless workflow and avoids dependency on external network speeds. The systems adhere to DICOM and IHE standards for broader hospital network compatibility.

Details

Category Radiology & Imaging AI
Pricing Contact for pricing
DeploymentIntegrated into Canon Medical Systems CT, MRI, and PET scanners
Compliance
BAA Available Yes AI-estimated
HIPAA Compliant Yes AI-estimated
FDA Status 1 AI-estimated

AiCE has received multiple 510(k) clearances from the U.S. Food and Drug Administration (FDA) for its application in CT, MRI, and PET systems. This includes clearance for ultra-high resolution CT, 1.5T and 3T MR systems for a wide range of clinical indications, and for PET/CT systems.

Integrations
EHR Not specified
Specialties Neurology, Nuclear Medicine, Radiology

What the Web Says

Canon Medical Systems' AiCE (Advanced intelligent Clear-IQ Engine) is generally well-received for its deep learning reconstruction technology, which significantly improves image quality by reducing noise and enhancing resolution across various modalities like CT and MRI. Physicians appreciate the clearer images, leading to more confident diagnoses and reduced need for repeat scans, while healthcare IT notes its integration benefits. Some discussions touch upon the initial investment and the learning curve for optimizing its use.

Overall: Positive

Strengths

  • Significantly improved image quality (reduced noise, enhanced resolution)
  • Faster scan times due to lower dose requirements without compromising quality
  • Enhanced diagnostic confidence for physicians
  • Potential for reduced radiation dose to patients (especially in CT)
  • Applicability across multiple imaging modalities (CT, MRI)
  • Improved workflow efficiency by reducing post-processing time

Limitations

  • Initial investment cost can be substantial
  • Requires compatible Canon Medical Systems hardware
  • Learning curve for optimizing parameters and fully leveraging its capabilities
  • Some users report a need for fine-tuning to achieve optimal results in specific clinical scenarios
  • Limited direct comparisons with competing AI reconstruction technologies from other vendors in some reviews
  • Potential for over-reliance on AI, requiring continued human oversight

Based on reviews from: AuntMinnie.com, Radiology Today, Healthcare IT News, MD Buyline (via various articles), Reddit (r/radiology, r/mri), Various medical journal articles and conference presentations

Last updated: 2026-10-02

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Press & Coverage

MDPI
Performance of Commercial Deep Learning-Based Radiation Dose Optimization Software in Pediatric Radiology: A Systematic Review
This systematic review evaluates the performance of commercial deep learning-based image reconstruction (DLIR) software, including Canon Medical Systems' AiCE, for radiation dose optimization in pediatric radiology, noting AiCE's consistent performance in dose reduction and image quality improvement for abdominal CT.
2026-08
ResearchGate (Journal of Thoracic Imaging)
Artificial Intelligence Iterative Reconstruction for Dose Reduction in Pediatric Chest CT: A Clinical Assessment via Below 3 Years Patients With Congenital Heart Disease
This study assesses the use of deep learning reconstruction (DLR), including Canon Medical Systems' AiCE, for dose reduction in pediatric chest CT for patients with congenital heart disease, comparing it with iterative reconstruction (IR) methods.
2025-02
Taylor & Francis Online (Annals of the ICRP)
Tomographic image reconstruction from projections using generative AI
This research article discusses the use of generative AI for tomographic image reconstruction, highlighting Canon Medical Systems' AiCE as one of two FDA-approved deep learning-based reconstruction algorithms designed to reduce noise and increase diagnostic confidence in CT images.
2025-04
Hospimedica.com
Global Medical MRI Market Set for $14.3B by 2031 Amid Helium-Free & Ultra-High-Field Shift
This article discusses trends in the global medical MRI market, mentioning Canon Medical Systems and its AiCE reconstruction technology as a driver of adoption in secondary and community healthcare tiers.
2026-09
PMC (European Radiology Experimental)
Validation of a convolutional neural network for the automated creation of curved planar reconstruction images along the main pancreatic duct
This study validates a convolutional neural network for automated creation of curved planar reconstruction images, noting that CT images were reconstructed using Canon Medical Systems' AiCE deep learning reconstruction, which improves image quality and reduces noise.
2022-09
MDPI
Enhancement of 18F-Fluorodeoxyglucose PET Image Quality by Deep-Learning-Based Image Reconstruction Using Advanced Intelligent Clear-IQ Engine in Semiconductor-Based PET/CT Scanners
This paper investigates the enhancement of 18F-Fluorodeoxyglucose PET image quality using Canon Medical Systems' AiCE deep-learning-based image reconstruction in semiconductor-based PET/CT scanners, demonstrating improved delineation, reduced image noise, and better overall image quality.
2022-10
DAIC - Diagnostic and Interventional Cardiology
New Technologies Take Cardiac CT to the Next Level
This article highlights new technologies in cardiac CT, including Canon Medical Systems' AiCE deep learning reconstruction software, which gained FDA clearance in spring 2019 and uses AI algorithms to improve image quality and reduce noise.
2020-06
Imaging Technology News
Trends in Computed Tomography | Imaging Technology News
This article discusses trends in computed tomography, noting that Canon Medical Systems' AiCE deep learning reconstruction software, cleared by the FDA in spring 2019, uses AI to enhance image resolution and reduce noise.
2019-10

Videos

Product demos, reviews, and walkthroughs for Canon Medical Systems AiCE (Advanced intelligent Clear-IQ Engine).

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Frequently Asked Questions

AiCE is primarily integrated into Canon Medical Systems' CT scanners, including the Aquilion ONE / PRISM Edition and the Aquilion Precision. It's designed to enhance image quality by reducing noise and improving spatial resolution across a wide range of CT applications, such as neuro, cardiac, and body imaging.
Canon Medical Systems states that AiCE is a clinically cleared medical device, implying it has met regulatory requirements for safety and efficacy. Regarding data privacy, AI processing typically occurs on anonymized image data within the scanner, and ethical considerations like algorithmic bias are addressed through rigorous validation and testing processes to ensure consistent and accurate results across diverse patient populations.
AiCE utilizes a deep learning reconstruction approach, trained on a vast dataset of high-quality images to differentiate true signal from noise. This allows for significant noise reduction and improved image clarity at lower radiation doses compared to traditional iterative reconstruction methods. Competitors offer similar AI-powered solutions, but the specific algorithms, training data, and resulting image characteristics can vary.
AiCE is generally integrated into newer Canon Medical Systems CT scanners, such as the Aquilion ONE / PRISM Edition and Aquilion Precision. While specific pricing details are typically part of a broader system acquisition, it's often included as a standard feature or an optional upgrade package. Maintenance costs would be part of the overall service agreement for the CT system.
While AiCE significantly improves image quality, like all AI algorithms, it may have limitations in extremely challenging cases, such as highly motion-corrupted studies or in the presence of severe artifacts. Canon Medical Systems continuously refines AiCE, but physicians should always exercise clinical judgment and be aware that even advanced AI may not perfectly compensate for all imaging challenges.
Canon Medical Systems typically provides comprehensive training and ongoing support for their imaging systems, including features like AiCE. This often includes on-site training, educational materials, and access to clinical application specialists to help optimize image acquisition protocols and leverage the full capabilities of the technology.
AiCE primarily functions within the Canon CT scanner itself, generating enhanced images that are then sent to PACS in standard DICOM format. This ensures seamless integration with existing PACS and hospital IT systems without requiring significant changes to workflow or data management. The improved image quality can potentially reduce review times and enhance diagnostic confidence.

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Suggest an Edit → | Last Verified: 2026-07-01 | First Added: 2026-07-01
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