The Deep Learning Image Reconstruction for Gemstone Spectral Imaging option is a deep learning based CT reconstruction method intended to produce cross-sectional images by computer reconstruction of dual energy X-ray transmission data acquired with Gemstone Spectral Imaging, for all ages. Deep Learning Image Reconstruction for Gemstone Spectral Imaging can be used for whole body, vascular, and contrast enhanced head CT applications.

Device Description

Deep Learning Image Reconstruction for Gemstone Spectral Imaging (DLIR-GSI) is the next step in CT reconstruction advancement. Like its predicate device (DLIR), DLIR-GSI is an image reconstruction method that uses a dedicated Convolution Neural Network (CNN) that has been designed and trained specifically to reconstruct CT GSI Images to give an image appearance similar to traditional FBP images while maintaining or improving the performance of ASiR-V. The DLIR-GSI can generate monochromatic images (MC), material decomposition images (MD), and virtual unenhanced images (VUE). Multiple MD images such as lodine, Water, Calcium, Hydroxyapatite (HAP), Fat, Uric Acid can be prescribed by the user and generated by the subject device. DLIR-GSI demonstrates same or better Imaging performance as compared to ASiR-V in the following areas: low contrast detectability (LCD), image noise, contrast to noise ratio (CNR), high contrast spatial resolution, CT number accuracy, MD quantification accuracy and metal artifact reduction. Reconstruction times with DLIR-GSI support a normal throughput for routine CT.

The device is marketed as Deep Learning Image Reconstruction for Gemstone Spectral Imaging and the images produced are branded as "TrueFidelity™ CT Images".

Deep Learning Image Reconstruction for Gemstone Spectral Imaging is compatible with dual energy scan modes using the standard kernel and was trained specifically on the Revolution CT family of systems (K163213, K133705, K19177). 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 DLR-GSI: Low, Medium, or High. The strength selection will vary with individual users' preference for the specific clinical need.

As compared to the predicate device, the intended use of Deep Learning Image Reconstruction for Gemstone Spectral Imaging does not change (head and whole-body CT image reconstruction). Both algorithms are designed to produce cross-sectional images of the head and body by computer reconstruction of X-ray transmission data, for all ages.

AI/ML Overview

Acceptance Criteria and Study Details for Deep Learning Image Reconstruction for Gemstone Spectral Imaging (DLIR-GSI)

1. Table of Acceptance Criteria and Reported Device Performance:

Acceptance Criteria Category	Specific Metric	Acceptance Criteria	Reported Device Performance (DLIR-GSI vs. ASiR-V)
Image Quality (Bench Testing)	Low Contrast Detectability (LCD)	As good as or better than ASiR-V when substituted using raw data from the same scan.	Demonstrated "same or better Imaging performance as compared to ASiR-V" for LCD. (Implied acceptance by the statement and overall conclusion of substantial equivalence).
	Image Noise	As good as or better than ASiR-V when substituted using raw data from the same scan.	Demonstrated "same or better Imaging performance as compared to ASiR-V" for image noise.
	High Contrast Spatial Resolution	As good as or better than ASiR-V when substituted using raw data from the same scan.	Not explicitly stated as "same or better," but implied by "DLIR-GSI demonstrates same or better Imaging performance as compared to ASiR-V in the following areas: low contrast detectability (LCD), image noise, contrast to noise ratio (CNR), high contrast spatial resolution".
	Contrast to Noise Ratio (CNR)	As good as or better than ASiR-V when substituted using raw data from the same scan.	Demonstrated "same or better Imaging performance as compared to ASiR-V" for CNR.
	CT Number Accuracy	As good as or better than ASiR-V when substituted using raw data from the same scan.	Demonstrated "same or better Imaging performance as compared to ASiR-V" for CT number accuracy.
	CT Number Uniformity	Not explicitly stated as "better than" but was part of the comparison.	Compared against ASiR-V; specific performance not detailed, but overall conclusion of substantial equivalence suggests satisfactory performance.
	Material Decomposition Accuracy	As good as or better than ASiR-V when substituted using raw data from the same scan.	Demonstrated "same or better Imaging performance as compared to ASiR-V" for MD quantification accuracy.
	Iodine Detection	Not explicitly stated as "better than" but was part of the comparison.	Compared against ASiR-V; specific performance not detailed, but overall conclusion of substantial equivalence suggests satisfactory performance.
	Metal Artifact Reduction	Not explicitly stated as "better than" but was part of the comparison.	Compared against ASiR-V; specific performance not detailed, but overall conclusion of substantial equivalence suggests satisfactory performance.
	Pediatric Test	Adequate visualization of objects with anthropomorphic phantom.	Not explicitly detailed, but implied by inclusion in testing and overall conclusion of substantial equivalence.
Clinical Performance (Reader Study)	Diagnostic Quality Images	Produce diagnostic quality images.	Confirmed that DLIR-GSI "produce diagnostic quality images."
	Image Noise Texture	Preferred noise texture than the reference device ASiR-V.	Confirmed that DLIR-GSI had "preferred noise texture than the reference device ASiR-V."
	Visualization of Small, Low-Contrast Objects	Adequate visualization for diagnostic use in extremely clinically challenging cases.	A board-certified radiologist confirmed "all object(s) were adequately visualized for diagnostic use using DLIR-GSI" in 7 additional challenging cases.

The study concluding that the device meets the acceptance criteria is based on:

Bench Testing: Performed on the identical raw datasets obtained on GE's Revolution CT family of systems, applying both DLIR-GSI and ASiR-V reconstructions for comparison.
Clinical Reader Study: A retrospective study involving radiologists evaluating images reconstructed with both ASiR-V and DLIR-GSI.

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

Test Set Sample Size:
- Main Reader Study: 40 retrospectively collected cases.
- Additional Clinical Evaluation: 7 additional retrospectively collected clinically challenging cases.
Data Provenance: Retrospectively collected clinical cases. The country of origin is not specified in the provided text.

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

Main Reader Study: 5 board-certified radiologists.
- Qualifications: Expertise in the specialty areas that align with the anatomical region of each case. (e.g., three readers for body/extremity, three for contrast-enhanced head/neck, one qualified for both). Specific years of experience are not mentioned.
Additional Clinical Evaluation: 1 board-certified radiologist.
- Qualifications: Expertise in the specialty area that aligns with all cases containing small, low-contrast objects. Specific years of experience are not mentioned.

4. Adjudication Method for the Test Set:

Main Reader Study: Each image was read by 3 different radiologists. The radiologists provided an assessment of image quality using a 5-point Likert scale.
- Adjudication Method: Implicitly, a consensus or agreement among the 3 readers would have been used for the assessment of diagnostic quality and noise texture preference. The document states, "The result of this reader study confirmed that the DLIR-GSI (the subject device) produce diagnostic quality images and have preferred noise texture than the reference device ASiR-V," suggesting that the collective findings of the readers led to this confirmation. Explicit details of a 2+1 or 3+1 adjudication are not provided.
Additional Clinical Evaluation: A single board-certified radiologist evaluated the 7 challenging cases. No adjudication method was applicable as there was only one reader.

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

Yes, an MRMC study was performed (the "Clinical Testing" described).
Effect Size (Improvement with AI vs. without AI assistance): The document states that readers confirmed DLIR-GSI produced "diagnostic quality images" and had "preferred noise texture" compared to ASiR-V (considered without AI assistance in this context, or a lesser AI-powered reconstruction). The additional evaluation confirmed "adequately visualized for diagnostic use" in challenging cases. However, specific quantitative effect sizes (e.g., a percentage improvement in diagnostic accuracy, a specific change in AUC, or a numerical metric of improvement in reader performance) are not provided in the given text.

6. Standalone (Algorithm Only) Performance Study:

Yes, a standalone performance study was done through engineering bench testing. This testing compared DLIR-GSI (algorithm only) against ASiR-V (reference/control algorithm) using identical raw datasets. Metrics like LCD, image noise, CNR, spatial resolution, CT number accuracy, material decomposition accuracy, iodine detection, and metal artifact reduction were evaluated directly from the reconstructed images without human interpretation.

7. Type of Ground Truth Used:

Bench Testing: The ground truth for metrics like LCD, image noise, spatial resolution, CT number accuracy, etc., would have been based on physical phantom measurements and known parameters of the phantoms used in the engineering tests.
Clinical Reader Study: The ground truth for image quality and diagnostic usability was established by expert consensus/interpretation from the board-certified radiologists. The text doesn't mention pathology or outcomes data as the primary ground truth for the reader study, but rather the radiologists' assessment of diagnostic quality and visualization.

8. Sample Size for the Training Set:

The document states that the neural network was "trained specifically to reconstruct CT GSI Images" using "single energy acquired images on the CT Scanner" (for the predicate) and "dual energy acquired images on the CT Scanner" (for the proposed device). It also mentions "information obtained from extensive phantom and clinical data" was used for noise characteristics.
However, the specific sample size (number of images or cases) for the training set is NOT provided in the text.

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

The text implies that the neural network was trained to produce an "image appearance similar to traditional FBP images while maintaining or improving the performance of ASiR-V." This suggests that the "ground truth" for training was implicitly the characteristics of high-quality CT images, likely leveraging existing FBP and ASiR-V reconstructed images from "extensive phantom and clinical data."
For noise modeling, ground truth was based on "characterization of the photon statistics as it propagates through the preprocessing and calibration imaging chain" and using a trained neural network that "models the scanned object using information obtained from extensive phantom and clinical data."
Specific details on how the ground truth was rigorously established for the training data (e.g., expert annotations, pathology correlation, quantitative metrics derived from known phantoms) are NOT explicitly described. The training appears to be focused on matching or improving upon established reconstruction methods using a large dataset.

Summary

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December 10, 2020

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GE Medical Systems, LLC % Ms. Katelyn Rowley Regulatory Affairs Leader 3000 N. Grandview Blvd. WAUKESHA WI 53188

Re: K201745

Trade/Device Name: Deep Learning Image Reconstruction for Gemstone Spectral Imaging Regulation Number: 21 CFR 892.1750 Regulation Name: Computed tomography x-ray system Regulatory Class: Class II Product Code: JAK Dated: November 13, 2020 Received: November 16, 2020

Dear Ms. Rowley:

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/cfpmp/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 devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see

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combination-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 medical devices and radiation-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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Section 4: Indications for Use

Deep Learning Image Reconstruction for Gemstone Spectral Imaging (GSI_DLIR)

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Indications for Use

510(k) Number (if known)

K201745

Device Name

Deep Learning Image Reconstruction for Gemstone Spectral Imaging

Indications for Use (Describe)

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 OF SAFETY AND EFFECTIVNESS

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

Date:	June 25, 2020
Submitter:	GE Medical Systems, LLC
	3000 North Grandview Blvd
	Waukesha, WI 53188
Primary Contact:	Katelyn Rowley
	Regulatory Affairs Leader
	Phone 262-309-5888
	Email: Katelyn.rowely@ge.com
Secondary Contacts:	Helen Peng
	Senior Regulatory Affairs Director
	GE Healthcare
	Tel: 262-424-8222
	Email: hong.peng@med.ge.com
Device Trade Name:	Deep Learning Image Reconstruction for Gemstone Spectral Imaging
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:	K134640 cleared on March 25, 2014

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Regulation Number/ 21 CFR 892.1750 Computed tomography x-ray system / JAK Product Code:

Reference Devices Information

Device Name:	Revolution CT
Manufacturer:	GE Medical Systems, LLC
510(k) Number:	K163213 cleared on December 16, 2016
Regulation Number/Product Code:	21 CFR 892.1750 Computed tomography x-ray system / JAK

Device Description

The device is marketed as Deep Learning Image Reconstruction for Gemstone Spectral Imaging and the images produced are branded as "TrueFidelity™ CT Images".

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

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Intended Use

The Deep Learning Image Reconstruction for Gemstone Spectral Imaging option is intended for whole body, vascular, and contrast enhanced head CT applications.

Indications for Use

Comparisons

The Deep Learning Image Reconstruction for Gemstone Spectral Imaging (DLIR-GSI) employs the same fundamental technology as that of the predicate device. In fact, the DLIR-GSI deep learning neural network was modified from that of the predicate. The DLIR-GSI option is substantially equivalent to the predicate device, DLIR reconstruction option. Because both the DLIR-GSI and the predicate device DLIR reconstruction for single energy are implemented on reference device, the Revolution CT family of Systems with GSI (K163213) they utilize the same hardware and software platform technology on which substantial equivalency is demonstrated. The table below summarizes the substantive feature/technological similarities and differences between the predicate device and the proposed device:

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GE Healthcare 510(k) Premarket Notification Submission – DLIR-GSI

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Specification/ Attribute	Deep Learning ImageReconstruction(Predicate Device, K183202)	Deep Learning ImageReconstruction for GemstoneSpectral Imaging(Proposed Device)
Compatible Image Types	Compatible with single energy scanmodes and standard kernel.	Compatible with dual energyscan modes and standard kernel.
IQ performance vs dose	Low contrast detectability (LCD),image noise, contrast to noise ratio(CNR), and high contrast spatialresolution are as good or betterthan ASiR-V when substituted usingraw data from the same scan.	Same
Technology	Utilizes a dedicated neural networkwhich is trained using single energyacquired images on the CT Scannerand designed specifically togenerate high quality CT images.	Utilizes a dedicated neuralnetwork which is trained usingdual energy acquired images onthe CT Scanner and designedspecifically to generate highquality CT images.
System statistics - Noisemodeling of the datacollection imaging chain(photon noise andelectronic noise)	Characterization of the photonstatistics as it propagates throughthe preprocessing and calibrationimaging chain.	Same
System statistics - Noisecharacteristics of thereconstructed images	Utilizes a trained neural networkwhich models the scanned objectusing information obtained fromextensive phantom and clinicaldata.	Same
Clinical Workflow	Select recon type and strength(Low, Medium, High).	Same

Deep Learning Image Reconstruction for Gemstone Spectral imaging does not introduce any additional risks/hazards, warnings, or limitations.

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Determination of Substantial Equivalence

Summary of Non-Clinical Testing

Deep Learning Image Reconstruction for Gemstone Spectral Imaging 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 for Gemstone Spectral Imaging was designed under the Quality System Regulations of 21CFR 820 and ISO 13485. GE believes that the extensive bench testing and the physician evaluation are sufficient for FDA's substantial equivalence determination.

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

Requirement Definition ●
. Risk Analysis
Technical Design Reviews
. Formal Design Reviews
. Code Review
Software Unit Implementation
Software Integrations and Integration Testing
System Testing
- o Safety Testing (Verification)
- o Image Performance Testing (Verification)
- 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.

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

Additional Non-Clinical Testing

Engineering bench testing was performed to support substantial equivalence and the product performance claims. The evaluation and analysis used the identical raw datasets obtained on GE's Revolution CT family of systems and then applies the Deep Learning Image Reconstruction for Gemstone Spectral Imaging or ASiR-V reconstruction. The resultant images were then compared for:

Low contrast detectability (LCD)
. Image noise (pixel standard deviation)
. High contrast spatial resolution (MTF)
Contrast to noise ratio (CNR)
CT Number accuracy
I CT Number uniformity
. Material Decomposition accuracy
트 lodine detection

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. Metal artifact reduction
. Pediatric test with anthropomorphic phantom

Clinical Testing

The reader study used a total of 40 retrospectively collected cases which represent typical and challenging clinical cases. The raw data from each of these cases was reconstructed with both ASIR-V and Deep Learning Image Reconstruction for Gemstone Spectral Imaging and presented to each reader independently. The results of the study support substantial equivalence and performance claims.

These images were read by 5 board-certified radiologists with expertise in the specialty areas that align with the anatomical region of each case. Each image was read by 3 different radiologists who provided an assessment of image quality related to diagnostic use according to a 5-point Likert scale. Three readers read the cases primarily covering body and extremity anatomy, three readers read the cases primarily covering contrast enhanced head/neck anatomy. One reader was qualified to read both body and head/neck anatomy.

Additionally, the readers were asked to compare directly the ASiR-V and Deep Learning Image Reconstruction for Gemstone Spectral Imaging images according to the key metric of image noise texture.

The result of this reader study confirmed that the DLIR-GSI (the subject device) produce diagnostic quality images and have preferred noise texture than the reference device ASiR-V.

To further challenge the DLR-GSI reconstruction algorithm, 7 additional retrospectively collected clinically challenging cases containing small, low contrast objects were evaluated. These images were read by a board-certified radiologist with expertise in the specialty area that aligns with all cases. The reader confirmed that all object(s) were adequately visualized for diagnostic use using DLIR-GSI. This additional clinical evaluation further confirmed that DLIR-GS! produces diagnostic quality images, even for extremely clinically challenging cases.

Substantial Equivalence

The changes associated with Deep Learning Image Reconstruction for Gemstone Spectral Imaging do not change the Intended Use from the predicate, and represent equivalent technological characteristics including the dedicated neural network, with no impact on control mechanism or operating principle.

Deep Learning Image Reconstruction for Gemstone Spectral Imaging was developed under GE Healthcare's quality system. Design verification, along with bench testing and the clinical reader study provided in this submission demonstrates that Deep Learning Image Reconstruction for Gemstone Spectral Imaging is substantially equivalent and hence as safe and as effective as the legally marketed predicate device. GE's quality system's design, verification, and risk management processes did not identify any additional hazards, unexpected results, or adverse effects stemming from the changes to the predicate.

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Based on development under GE Healthcare's quality system, the successful verification and validation testing, including the additional engineering bench testing, and the clinical reader study, GE Healthcare believes that Deep Learning Image Reconstruction for Gemstone Spectral Imaging is substantially equivalent to the predicate device and hence 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.