Deep Learning Image Reconstruction

K220961 · Ge Healthcare Japan Corporation · JAK · Jul 29, 2022 · Radiology

Device Facts

• Record ID: K220961
• Device Name: Deep Learning Image Reconstruction
• Applicant: Ge Healthcare Japan Corporation
• Product Code: JAK · Radiology
• Decision Date: Jul 29, 2022
• Decision: SESE
• Submission Type: Special
• Regulation: 21 CFR 892.1750
• Device Class: Class 2
• Attributes: AI/ML, Pediatric

Intended Use

The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.

Device Story

Deep Learning Image Reconstruction (DLIR) software; utilizes dedicated Deep Neural Network (DNN) to reconstruct CT images from raw X-ray transmission data; produces 'TrueFidelity' CT images; integrated into existing CT scanner reconstruction chain; deployed on GE Edison Platform; operated by clinicians on CT console; provides three user-selectable reconstruction strengths (Low, Medium, High); maintains image noise, spatial resolution, and artifact suppression performance comparable to ASiR-V; aids diagnostic interpretation by providing images with FBP-like appearance; supports routine clinical throughput.

Clinical Evidence

Bench testing only. Performance verified through design control testing, including image performance testing (noise, low contrast detectability, spatial resolution, artifact suppression) and simulated use testing. No clinical data presented.

Technological Characteristics

Software-based image reconstruction; utilizes Deep Neural Network (DNN); deployed on GE Edison Platform; DICOM compatible output; supports three user-selectable reconstruction strengths; operates on raw CT data; developed under ISO 13485 and 21 CFR 820.

Indications for Use

Indicated for all ages requiring head, whole body, cardiac, and vascular CT imaging. Used for computer reconstruction of X-ray transmission data from axial, helical, and cardiac acquisitions.

Regulatory Classification

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.

Predicate Devices

• Deep Learning Image Reconstruction (K183202)

Reference Devices

• ASIR-V (K133640)
• Discovery CT750 HD (K120833)
• Revolution CT systems (K133705, K163213, K19177)
• Revolution EVO (K131576)
• Revolution Maxima (K192686)
• Revolution Ascend (K201369)

Submission Summary (Full Text)

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July 29, 2022

GE Healthcare Japan Corporation % Wang Xing Regulatory Affairs Manager GE Hangwei Medical Systems Co., Ltd. West Area of Building No.3. No.1 Yongchang North Road Beijing Economic & Technological Development Area, Beijing 100176 CHINA

Re: K220961

Trade/Device Name: Deep Learning Image Reconstruction Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK Dated: July 13, 2022 Received: July 14, 2022

Dear Wang Xing:

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

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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 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 (OS) 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 ([e-mail]) or phone [phone] or 301-796-7100).

Sincerely,

Laurel Burk, Ph.D. Assistant Director DHT8B: Division of Imaging Devices and Electronic Products OHT8: Office of 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)

#### K220961

Device Name Deep Learning Image Reconstruction

#### Indications for Use (Describe)

The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, (Volumetric), and Cardiac acquisitions, for all ages. Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.

| Type of Use (Select one or both, as applicable) | |
|----------------------------------------------------------------------------------|---------------------------------------------------------------------------------|
| <div> <span> Prescription Use (Part 21 CFR 801 Subpart D) </span> </div> | <div> <span> Over-The-Counter Use (21 CFR 801 Subpart C) </span> </div> |

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Image /page/3/Picture/1 description: The image shows the logo for General Electric (GE). The logo is a blue circle with the letters "GE" in a stylized font in the center. There are decorative swirls around the letters. The logo is simple and recognizable, and it is associated with a well-known and established company.

# 510(k) SUMMARY

K220961

This 510(k) summary of Safety and Effectiveness information is submitted in accordance with the requirement of 21 CFR Part 807.87(h):

| Date: | March 31, 2022 |
|----------------------------------|---------------------------------------------------------------------------------------------------------------------------------|
| Submitter: | GE Healthcare Japan Corporation<br>7-127, Asahigaoka, 4-chome<br>Hino-shi, Tokyo, 191-8503, Japan |
| Primary Contact: | Wang Xing<br>Regulatory Affairs Manager<br>Phone [phone]<br>Email: [e-mail] |
| Secondary Contacts: | Helen Peng<br>Senior Regulatory Affairs Director<br>GE Healthcare<br>Tel: [phone]<br>Email: [e-mail] |
| | John Jaeckle<br>Chief Regulatory Affairs Strategist<br>GE Healthcare<br>Tel: [phone]<br>Email: [e-mail] |
| Device Trade Name: | Deep Learning Image Reconstruction |
| Device Classification | Class II |
| Regulation Number/ Product Code: | 21 CFR 892.1750 Computed tomography x-ray system /JAK |
| Predicate Device Information | |
| Device Name: | Deep Learning Image Reconstruction |
| Manufacturer: | GE Medical Systems, LLC |
| 510(k) Number: | K183202 cleared on April 12, 2019 |
| Regulation Number/ Product Code: | 21 CFR 892.1750 Computed tomography x-ray system /JAK |
| Reference Devices Information | |
| Device Name: | ASIR-V |
| Manufacturer: | GE Medical Systems, LLC |
| 510(k) Number: | K133640 cleared on March 25, 2014 |
| Regulation Number/ Product Code: | 21 CFR 892.1750 Computed tomography x-ray system /JAK |
| Device Name: | Discovery CT750 HD |
| Manufacturer: | GE Medical Systems, LLC |
| 510(k) Number: | K120833 cleared on June 13, 2012 |
| Regulation Number/ Product Code: | 21 CFR 892.1750 Computed tomography x-ray system /JAK |

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Image /page/4/Picture/1 description: The image shows the General Electric (GE) logo. The logo consists of the letters "GE" in a stylized script, enclosed within a blue circle. There are decorative swirls around the letters, adding a touch of elegance to the design. The logo is simple, recognizable, and represents the General Electric brand.

#### Device Description

Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT Images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: dose, image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression.

The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction software support a normal throughput for routine CT.

The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity™ CT Images".

The system allows user selection of three strengths of Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preference for the specific clinical need.

DLIR has been cleared on GE's multiple CT systems of varying image chains, including Revolution CT systems (K133705, K163213, K19177), Revolution EVO (K131576) /Revolution Maxima (K192686) and Revolution Ascend (K201369). Now the DLR algorithm is being ported to Discovery CT750 HD family CT systems (K120833) which include Discovery CT750 HD, Revolution Frontier and Revolution Discovery CT, thus triggering this premarket notification.

Compared to the predicate device, the intended use and indications for use of Deep Learning Image Reconstruction are identical.

# Intended Use

The Deep Learning Image Reconstruction software is intended for head, whole body, cardiac, and vascular CT scans.

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Image /page/5/Picture/1 description: The image shows the logo for General Electric (GE). The logo is a blue circle with the letters "GE" in a stylized font in the center. There are four water droplet shapes surrounding the circle. The logo is simple and recognizable, and it is often used to represent the company's brand.

# Indications for Use

The Deep Learning Image Reconstruction software is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages.

Deep Learning Image Reconstruction software can be used for head, whole body, cardiac, and vascular CT applications.

# Comparisons

The GE Deep Learning Image Reconstruction (DLIR) software for Discovery CT750 HD family CT systems, is substantially equivalent to the unmodified predicate device DLR reconstruction option (K183202) for Revolution CT family. The fundamental technology, i.e., the DLIR algorithm, remains unchanged from the predicate. The table below summarizes the substantive feature/technological differences between the predicate device and the proposed device:

| Specification/ Attribute | Deep Learning Image<br>Reconstruction | Deep Learning Image<br>Reconstruction |
|-----------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| | (Predicate Device, K183202) | (Proposed Device) |
| Technology | Utilizes a dedicated Deep Neural<br>Network (DNN) designed specifically<br>to generate high quality CT images | Same |
| Clinical Workflow | Select recon type and strength (Low,<br>Medium, High) | Same |
| Clinical Use | Routine Clinical Use | Same |
| Reference<br>protocols/dose | Using the same Reference protocols<br>provided on the Revolution CT<br>family systems for ASiR-V | Using the same Reference protocols<br>provided on the Discovery CT750<br>HD family CT systems for ASiR-V |
| IQ performance vs dose | Image noise, low contrast<br>detectability, spatial resolution, and<br>low signal artifact suppression as<br>good or better than ASiR-V on<br>Revolution CT family systems | Image noise, low contrast<br>detectability, spatial resolution, and<br>low signal artifact suppression as<br>good or better than ASiR-V on<br>Discovery CT750 HD family CT<br>systems |
| Deployment<br>Environment | On CT Console | On GE's Edison Platform. |

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Image /page/6/Picture/1 description: The image shows the General Electric (GE) logo. The logo consists of the letters 'GE' written in a stylized script, enclosed within a circular frame. The logo is blue and the background is white.

Deep Learning Image Reconstruction on the Discovery CT750 HD family CT systems does not introduce any new risks/hazards, warnings, or limitations.

The changes described above do not change the fundamental control mechanism, operating principle, and do not change the intended use from the predicate device.

# Determination of Substantial Equivalence

# Summary of Performance Testing

Deep Learning Image Reconstruction has successfully completed the design control testing per our quality system. No additional hazards were identified, and no unexpected test results were observed. Deep Learning Image Reconstruction was designed under the Quality System Regulations of 21CFR 820 and ISO 13485. GE believes that the extensive software testing are sufficient for FDA's substantial equivalence determination.

The following quality assurance measures have been applied to the development of the software:

- Requirement Definition
- Risk Analysis and Control
- . Technical Design Reviews
- Formal Design Reviews
- Software Development Lifecycle
- o Code Review
- O Software Unit Implementation
- O Software Integrations and Integration Testing
- System Testing
- Safety Testing (Verification) O
- Image Performance Testing (Verification) O
- O Simulating Use Testing (Validation)
- Software Release

The testing and results did not raise different questions of safety and effectiveness than associated with predicate device. We consider the proposed device is substantially equivalent to the predicate device, DLIR for Revolution CT products.

The substantial equivalence is also based on the software documentation for a "Moderate" level of concern device.

Additionally, the same set of tests and test methods employed on the predicate DLIR for Revolution CT were reproduced to support substantial equivalence and the product performance claims of the subject device.

# Substantial Equivalence

Deep Learning Image Reconstruction (DLIR) software for Discovery CT750 HD family CT systems was developed under GE Healthcare's quality system. The changes associated with Deep Learning Image Reconstruction (DLIR) do not change the Intended Use from the predicate, and represent equivalent technology characteristics, with no impact on control mechanism, operating principle, and energy type.

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Image /page/7/Picture/1 description: The image shows the General Electric (GE) logo. The logo consists of the letters 'GE' intertwined in a stylized script, enclosed within a circle. Three water droplet-like shapes surround the circle, adding a decorative element to the design. The logo is presented in a blue color scheme.

Design verification and validation, including IQ bench testing, demonstrate that the Deep Learning Image Reconstruction (DLIR) software met all of its design requirement and performance criteria. Design control and risk management processes did not identify any new hazards, unexpected results, or adverse effects stemming from the changes to the predicate.

GE Healthcare believes that Deep Learning Image Reconstruction Software is substantially equivalent to the legally marketed predicate device and hence is safe and effective for its intended use.