This device is indicated to acquire and display cross-sectional volumes of the whole the head, with the capability to image whole organs in a single rotation. Whole organs include, but are not limited to brain, heart, pancreas, etc.

The Aquilion ONE has the capability to provide volume sets of the entire organ. These volume sets can be used to perform specialized studies, using indicated software, of the whole organ by a trained and qualified physician.

FIRST is an iterative reconstruction algorithm intended to reduce exposure dose and improve high contrast spatial resolution for abdomen, pelvis, chest, cardiac, extremities and head applications.

AiCE is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung and cardiac applications.

Device Description

Aguilion ONE (TSX-305A/6) V8.9 with AiCE is a whole body multi-slice helical CT scanner, consisting of a gantry, couch and a console used for data processing and display. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician. This system is based upon the technology and materials of previously marketed Canon CT systems. In addition, the subject device incorporates the latest reconstruction technology, AiCE (Advanced intelligent Clear-IQ Engine), intended to reduce image noise and improve image quality by utilizing Deep Convolutional Neural Network methods. These methods can more fully explore the statistical properties of the signal and noise. By learning to differentiate structure from noise, the algorithm produces fast, high quality CT reconstruction.

AI/ML Overview

Acceptance Criteria and Study Proving Device Performance: Canon Medical Systems Corporation Aquilion ONE (TSX-305A/6) V8.9 with AiCE

This document outlines the acceptance criteria and the study conducted to prove that the Aquilion ONE (TSX-305A/6) V8.9 with AiCE (Advanced intelligent Clear-IQ Engine) CT system meets its performance claims, as detailed in the provided FDA 510(k) summary (K183046).

The AiCE algorithm is a noise reduction algorithm that utilizes Deep Convolutional Neural Network methods to improve image quality and reduce image noise for abdomen, pelvis, lung, and cardiac applications. The primary goal of the study was to demonstrate that the AiCE system provides improved image quality, reduced noise, and better low-contrast detectability compared to the predicate device and other reconstruction methods, while maintaining diagnostic quality.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Aquilion ONE (TSX-305A/6) V8.9 with AiCE are based on the comparison of its image quality metrics against established benchmarks, primarily the predicate device (Aquilion ONE (TSX-305A/3) V8.3 with FIRST 2.1) and other reconstruction methods (AIDR 3D, Filtered Back Projection).

Metric / Claim Category	Acceptance Criteria (Implicit/Explicit)	Reported Device Performance (with AiCE)
Image Quality (General)	Images must be of diagnostic quality for intended applications (abdomen, pelvis, lung, cardiac).	Representative abdomen/pelvis, lung, and cardiac diagnostic images, reviewed by an American Board Certified Radiologist, were obtained and confirmed to be of diagnostic quality with AiCE reconstruction.
Quantitative Spatial Resolution	Improved quantitative spatial resolution over AIDR 3D.	Improvement claim of 7.4 lp/cm at 10% of the MTF for AiCE, relative to 5.7 lp/cm at 10% of the MTF when using AIDR 3D/STANDARD. This represents a significant improvement.
Low Contrast Detectability (LCD)	Improved low-contrast detectability over AIDR 3D. Demonstrate specific LCD performance for routine scanning.	A 12.2% improvement in low contrast detectability compared to AIDR 3D (at the same dose). Demonstrated low-contrast detectability of 1.5 mm at 0.3% contrast with 22.6mGy.
Noise Reduction	Improved quantitative noise reduction over AIDR 3D.	A 29.2% noise reduction (at the same dose) compared to AIDR 3D.
Dose Reduction	Demonstrate significant dose reduction capabilities.	A dose reduction of 79.6-82.4% compared to filtered back projection (for body applications, based on model observer evaluation).
Noise Texture/Appearance	Noise appearance/texture produced by AiCE should be similar to or better than standard filtered backprojection, and less artificial than other iterative reconstructions.	A phantom study determined that the noise appearance/texture produced by the AiCE reconstruction is more similar to standard filtered backprojection than texture produced by the FIRST reconstruction. This implies a more desirable, less "plastic" appearance often associated with strong iterative reconstructions.
General CT Image Metrics	Performance must be substantially equivalent to or better than the predicate device across various standard CT image quality metrics: Contrast-to-Noise Ratios (CNR), CT Number Accuracy, Uniformity, Slice Sensitivity Profile (SSP), Modulation Transfer Function (MTF)-Wire, Modulation Transfer Function (MTF)-Edge, Standard Deviation of Noise (SD), Noise Power Spectra (NPS), and Pediatric water phantom.	AiCE was demonstrated to be "substantially equivalent to the predicate device as demonstrated by the results of the above testing" across these metrics. While specific numbers are not provided for each, the overall statement indicates compliance with expected performance standards for a CT system, with the specific improvements highlighted above adding to this foundational equivalence.

2. Sample Size Used for the Test Set and Data Provenance

Sample Size for Test Set: The documentation does not explicitly state a specific number of cases or scans used for the quantitative phantom studies or the qualitative diagnostic image review. The qualitative evaluation mentions "Representative abdomen/pelvis, lung, and cardiac diagnostic images," implying a selection of cases rather than a large cohort. The quantitative evaluations used various phantoms.
Data Provenance: The studies were phantom-based for quantitative metrics and included "Representative ... diagnostic images" for qualitative assessment. There is no information provided regarding the country of origin of the diagnostic images, nor whether they were retrospective or prospective patient data. Given the context of a 510(k) submission primarily relying on technical performance and equivalence, large-scale patient outcome studies (prospective or retrospective) are often not required if technical equivalence and safety can be demonstrated through phantom studies and limited clinical image review.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

Number of Experts: For the qualitative assessment of diagnostic images, it is stated that images were "reviewed by an American Board Certified Radiologist." This indicates one expert was used for this part of the evaluation.
Qualifications of Experts: The expert was an "American Board Certified Radiologist." No specific years of experience are mentioned. For the quantitative phantom studies, experts establishing ground truth would typically be physicists or engineers, but this is not explicitly detailed.

4. Adjudication Method for the Test Set

For the qualitative assessment of diagnostic images, the documentation states that images were "reviewed by an American Board Certified Radiologist, and it was confirmed that the AiCE reconstructed images were of diagnostic quality." This implies a single-reader assessment for confirming diagnostic quality, rather than a multi-reader or consensus-based adjudication method (e.g., 2+1 or 3+1). For the phantom studies, adjudication methods for ground truth are generally not applicable as the "ground truth" is derived from the known properties of the phantoms or precise measurements.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, an MRMC comparative effectiveness study was not explicitly reported in this 510(k) summary. The evaluation of the AiCE algorithm appears to have focused on:

Quantitative measurements using phantoms (spatial resolution, LCD, noise, dose reduction).
Qualitative review by a single radiologist to confirm diagnostic quality of "representative" patient images.
A phantom-based "noise texture reader study" to compare noise appearance, but this is not described as a full MRMC study for diagnostic accuracy or reader performance.

Therefore, no effect size or improvement in human reader performance with AI assistance vs. without AI assistance is detailed in this submission. The AiCE algorithm acts as an inherent image reconstruction/processing component of the CT system, improving the fundamental image quality before interpretation, rather than an AI-assisted diagnostic tool for specific pathologies that would typically undergo MRMC studies.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone (algorithm only) performance assessment was conducted for the key imaging metrics. The phantom studies directly evaluate the performance of the AiCE algorithm in terms of:

Quantitative Spatial Resolution (7.4 lp/cm MTF)
Quantitative Body LCD Improvement (12.2% better)
Quantitative Noise Reduction (29.2% less noise)
Quantitative Dose Reduction (79.6-82.4% reduction vs. FBP)
Noise Texture/Appearance (more similar to FBP than FIRST)
General CT Image Quality metrics (CNR, CT Number Accuracy, Uniformity, SSP, MTF, SD, NPS, Pediatric water phantom).

These measurements assess the intrinsic performance of the AiCE algorithm's output (the reconstructed image) independent of human interpretation.

7. Type of Ground Truth Used

Quantitative Studies (Spatial Resolution, LCD, Noise, Dose): The ground truth was established through phantom studies. Phantoms have precisely known physical properties and are designed to provide a measurable "truth" for imaging performance metrics (e.g., specific object sizes for spatial resolution, known contrast differences for LCD).
Qualitative Assessment (Diagnostic Image Quality): The ground truth was expert consensus / opinion from a "single American Board Certified Radiologist" who confirmed the "diagnostic quality" of the AiCE reconstructed images.
Noise Texture Reader Study: The ground truth for noise texture comparison was based on the collective assessment/preference of readers in that dedicated phantom study, establishing what "more similar to standard filtered backprojection" means.

8. Sample Size for the Training Set

The 510(k) summary does not provide details on the sample size used for training the AiCE Deep Convolutional Neural Network. This information is typically proprietary and not always required in detail for a 510(k) submission, especially when the device is an image reconstruction algorithm rather than a diagnostic AI that provides a specific clinical output (e.g., detection of a disease). The focus of this submission is on the output performance of the trained algorithm in terms of image quality metrics.

9. How the Ground Truth for the Training Set Was Established

The 510(k) summary does not provide details on how the ground truth was established for the training set of the AiCE Deep Convolutional Neural Network. For deep learning image reconstruction algorithms, the training process often involves:

Pairs of "noisy" and "clean" image data: The network learns to transform low-dose/noisy inputs into high-quality outputs. The "clean" ground truth might be derived from high-dose scans, or synthetic data generated with known characteristics, or even "ideal" images produced by traditional, more time-consuming reconstruction methods.
Simulations: Using physics-based models to simulate noise and artifacts, then generating ideal "ground truth" images.
Existing clinical data: Using a large dataset of patient images, processed or curated to serve as ground truth for noise reduction and image enhancement.

Without explicit information, the specific method used to establish ground truth for AiCE's training set remains undetailed in this FDA submission.

Summary

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Canon Medical Systems Corporation % Orlando Tadeo, Jr. Sr. Manager, Regulatory Affairs Canon Medical Systems USA, Inc. 2441 Michelle Drive TUSTIN CA 92780

Re: K183046

Trade/Device Name: Aquilion ONE (TSX-305A/6) V8.9 with AiCE Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK Dated: April 25, 2019 Received: April 30, 2019

Dear Mr. Tadeo:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for

June 12, 2019

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devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely.

For

Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known)

K183046

Device Name

Aquilion ONE (TSX-305A/6) V8.9 with AiCE

Indications for Use (Describe)

FIRST is an iterative reconstruction algorithm intended to reduce exposure dose and improve high contrast spatial resolution for abdomen, pelvis, chest, cardiac, extremities and head applications.

AiCE is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung and cardiac applications.

Type of Use (Select one or both, as applicable)
☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) SUMMARY

1. SUBMITTER'S NAME: Canon Medical Systems Corporation 1385 Shimoishigami Otawara-Shi, Tochigi-ken, Japan 324-8550
1. OFFICIAL CORRESPONDENT: Naofumi Watanabe Senior Manager, Regulatory Affairs and Vigilance
1. ESTABLISHMENT REGISTRATION: 9614698

4. CONTACT PERSON:

Orlando Tadeo, Jr. Sr. Manager, Regulatory Affairs Canon Medical Systems USA, Inc 2441 Michelle Drive Tustin, CA 92780 (714) 669-7459

1. Date Prepared: April 25, 2019
1. TRADE NAME(S): Aquilion ONE (TSX-305A/6) V8.9 with AiCE

7. COMMON NAME:

System, X-ray, Computed Tomography

1. DEVICE CLASSIFICATION (Regulatory Class, CFR Reference, Name): Class II (per 21 CFR 892.1750, Computed Tomography X-ray System)

9. PRODUCT CODE / DESCRIPTION: JAK / Computed Tomography X-Ray System

10. PERFORMANCE STANDARD:

This device conforms to applicable Performance Standards for Ionizing Radiation Emitting Products [21 CFR, Subchapter J, Part 1020]

PHONE: 800-421-1968 2441 Michelle Drive, Tustin, CA 92780

Made For life

K183046

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11. PREDICATE DEVICE:

Product	Marketedby	RegulationNumber	RegulationName	Product Code	510(k)Number	ClearanceDate
Aquilion ONE(TSX-305A/3) V8.3 withFIRST 2.1(Primary PredicateDevice)	CanonMedicalSystems,USA	21 CFR892.1750	ComputedTomographyX-ray System	JAK:System, X-ray,Tomography,Computed	K170177	06/30/2017

12. REASON FOR SUBMISSION:

Modification of existing medical device

13. DEVICE DESCRIPTION:

14. INDICATIONS FOR USE:

This device is indicated to acquire and display cross-sectional volumes of the whole body, to include the head, with the capability to image whole organs in a single rotation. Whole organs include, but are not limited to brain, heart, pancreas, etc.

FIRST is an iterative reconstruction algorithm intended to reduce exposure dose and improve high contrast spatial resolution for abdomen, pelvis, chest, cardiac, extremities and head applications.

AiCE is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung and cardiac applications.

15. SUBSTANTIAL EQUIVALENCE:

The Aquilion ONE (TSX-305A/6) V8.9 with AiCE, is substantially equivalent to the Aquilion ONE (TSX-305A/3) V8.3 with FIRST 2.1, which received premarket clearance under K170177and is marketed by Canon Medical Systems USA. The intended use of the Aquilion ONE is the same as that of the predicate device. The changes made to the subject device include the addition of AiCE (Advanced intelligent Clear-IQ Engine), a reconstruction algorithm that utilizes Deep Convolutional Neural Network methods to reduce image noise and improve image quality. A comparison of the technological characteristics between the subject and the predicate device is included below.

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Item	Aquilion ONE (TSX-305A/6) V8.9 with AiCE	Aquilion ONE (TSX-305A/3) V8.3 with FIRST 2.1 (K170177)
Anatomical Region	AIDR 3D(Whole Body)FIRST(Abdomen, pelvis, lung, cardiac, extremities and head)AiCE(Abdomen, pelvis, lung, and cardiac)	AIDR 3D(Whole Body)FIRST(Abdomen, pelvis, lung, cardiac, extremities and head)
Noise ReductionProcessing	AIDR 3DAIDR 3D EnhancedQuantum Denoising Smoothing (QDS)AiCE	AIDR 3DAIDR 3D EnhancedQuantum Denoising Smoothing (QDS)
Processing capability	Console CKCN-018B/1Reconstruction processing unit(AiCE/FIRST)	Console CKCN-018A/3I-REC BOX (FIRST only)
Display console kit	CGS-94A (Optional)*	CGS-75A (Optional)
Image Quality Claim	-Improved Quantitative SpatialResolution over AIDR 3D-Improved Quantitative DoseReduction over AIDR 3D-Improved Low-contrast Detectabilityover AIDR 3D	No change

*Minor hardware changes to increase data processing capability

16. SAFETY:

The device is designed and manufactured under the Quality System Regulations as outlined in 21 CFR § 820 and ISO 13485 Standards. This device is in conformance with the applicable parts of the following standards IEC60601-1, IEC60601-1-2, IEC60601-1-2, IEC60601-1-3, IEC60601-1-4, IEC60601-1-6, IEC60601-2-28, IEC60601-2-32, IEC60601-2-44, IEC60825-1, IEC62304, IEC62366, NEMA XR-25, NEMA XR-26 and NEMA PS 3.1-3.18. Additionally, this device complies with all applicable requirements of the radiation safety performance standards, as outlined in 21 CFR §1010 and §1020.

17. TESTING

Risk analysis and verification/validation testing conducted through bench testing demonstrate that the established specifications for the device have been met.

Image Quality Evaluation:

CT image quality metrics were performed, utilizing phantoms, to assess Contrast-to-Noise Ratios (CNR), CT Number Accuracy, Uniformity, Slice Sensitivity Profile (SSP), Modulation Transfer Function (MTF)-Wire, Modulation Transfer Function (MTF)-Edge, Standard Deviation of Noise (SD), Noise Power Spectra (NPS), Low Contrast Detectability (LCD) and Pediatric water phantom. AiCE is substantially equivalent to the predicate device as demonstrated by the results of the above testing.

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Quantitative Spatial Resolution Evaluation:

A comparison study was conducted, utilizing phantoms, in order to support a quantitative spatial resolution improvement claim of 7.4 lp/cm at 10% of the MTF for AiCE, relative to 5.7 lp/cm at 10% of the MTF when using AIDR 3D/STANDARD.

Quantitative Body LCD & Noise Improvement and Dose Reduction:

A model observer evaluation was conducted and the subject device demonstrated a dose reduction of 79.6-82.4% compared to filtered back projection, a 12.2% improvement in low contrast detectability and 29.2% noise reduction at the same dose for body compared to AIDR 3D.

Low-contrast Detectability Evaluation:

A phantom study was conducted in order to support claims of low-contrast detectability of 1.5 mm at 0.3% with 22.6mGy.

Noise Texture Reader Study:

A phantom study was conducted and it was determined that the noise appearance/texture produced by the AiCE reconstruction is more similar to standard filtered backprojection than texture produced by the FIRST reconstruction.

Representative abdomen/pelvis, lung, and cardiac diagnostic images, reviewed by an American Board Certified Radiologist, were obtained using the subject device and it was confirmed that the AiCE reconstructed images were of diagnostic quality.

Software Documentation for a Moderate Level of Concern, per the FDA guidance document, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices Document" issued on May 11, 2005, is also included as part of this submission.

Cybersecurity documentation, per the FDA cybersecurity premarket guidance document "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices " issued on October 2, 2014, is also included as part of this submission.

Additionally, testing of the subject device was conducted in accordance with the applicable standards published by the International Electrotechnical Commission (IEC) for Medical Devices and CT Systems.

18. CONCLUSION

The Aquilion ONE (TSX-305A/6) V8.9 with AiCE performs in a manner similar to and is intended for the same use as the predicate device, as indicated in product labeling. Based upon this information, conformance to standards, successful completion of software validation, application of risk management and design controls and the performance data presented in this submission it is concluded that the subject device has demonstrated substantial equivalence to the predicate device and is safe and effective for its intended use.

Regulation Number and Section

§ 892.1750 Computed tomography x-ray system.

(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.