Claritas iPET is an image processing software intended for use by radiologists and nuclear medicine physicians for noise reduction, sharpening, and resolution improvement of PET images (including PET/CT and PET/MRI) obtained with any kind of radionuclides, e.g. fluorodeoxyglucose (FDG). Enhanced images will be saved in DICOM files and exist in conjunction with original images.

Device Description

Claritas iPET v1.0 Image Enhancement System is a medical image enhancement software, i.e., a Software as a Medical Device (SaMD), that can be used to increase image quality by implementation of an image processing and image fusion algorithm.

Claritas iPET can be used to enhance Positron Emission Tomography (PET) images with optional simultaneous Magnetic Resonance Imaging (MRI) or Computerized Tomography (CT) scans of the same subject. Claritas iPET takes as input DICOM [Digital Imaging and Communications in Medicine] files of PET, MRI, and CT images, and produces an enhanced image of the same file. The objective is to enhance the DICOM files that are obscured and not clearly visible, to be more visible, sharper, and clearer through the Claritas iPET image enhancement process. Claritas iPET is intended to be used by radiologists and nuclear medicine physicians in hospitals, radiology centres and clinics, as well as by medical universities and research intuitions.

The image improvement includes noise reduction, sharpening of organ boundaries, and achieving super-resolution. With the help of Claritas iPET software, high quality PET scans can be produced. The Claritas iPET algorithm computes the fusion of functional (from PET) and anatomic (from MR or CT) information, and is based on Non-Local Means filtering. The goal of the software is to process and visualize the content of DICOM files storing 3D voxel arrays, i.e. a uniformly spaced sequence of slices of a PET scan. The processing algorithm may also input another 3D voxel array storing the density values obtained by a CT or MRI scan. The PET and CT/MR volumes should at least partially overlap to exploit the additional anatomic information. The CT or MR volume is expected to have a higher resolution than the PET volume in order for effective improvement. The sharpness. style and the detail of the visualization can be controlled by the user and can be compared to the visualization of the raw image data. During this process, no new feature is introduced that did not exist in the PET data, just the existing features are emphasized if they are also supported by the anatomy or suppressed if they are in the noise region and are not supported by the anatomy.

AI/ML Overview

This document does not contain the level of detail necessary to fully answer all aspects of your request, particularly regarding the specific acceptance criteria for regulatory clearance and the detailed methodology of human reader studies (MRMC). However, based on the provided text, here's a breakdown of the available information:

1. A table of acceptance criteria and the reported device performance

The document describes the device's performance in terms of improving image quality metrics (RMSE and SNR), but it does not explicitly state pre-defined acceptance criteria in a table format that would have been submitted to the FDA for regulatory clearance. Instead, it describes observed improvements.

Implied Acceptance Criteria (Performance Metric Improvements, based on the text):

Performance Metric	Acceptance Criteria (Implied)	Reported Device Performance
RMSE Reduction	At least 10% (for high dosage/longer scans)	Decreased by at least 10% (for high dosage/longer scans)
	50% (for low dosage/short scans)	Decreased by 50% (for low dosage/short scans)
SNR Increase	At least 20% (for high dosage/longer scans)	Increased by at least 20% (for high dosage/longer scans)
	4-5 times (for low dosage/short scans)	Increased by 4-5 times (for low dosage/short scans)

Note: The document states "All tests have passed," indicating that these performance levels met internal acceptance thresholds.

2. Sample size used for the test set and the data provenance

Test Set Sample Size: The document does not specify the exact number of cases/patients used for the test set. It mentions "real full body human PET scans" in the first option for ground truth, and the "Zubal mathematical phantom" in the second option. The number of scans for each of these is not quantified.
Data Provenance:
- Real Human Data: "real full body human PET scans" (retrospective, as they were "enhanced" after acquisition). The country of origin is not specified.
- Synthetic Data: "Zubal mathematical phantom" (synthetic/simulated data).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

The document does not mention the use of experts to establish ground truth for performance testing. Instead, it defines ground truth through:

Long scan / high dosage PET: "We executed a long scan and accepted the reconstructed results as ground truth."
Mathematical phantom: "we took the Zubal mathematical phantom, and considered it as the ground truth."

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

No adjudication method involving experts is mentioned for the test set. Ground truth was established via the long scan/high dosage PET or the mathematical phantom.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

The document does not indicate that an MRMC comparative effectiveness study was performed or submitted as part of this 510(k) summary. The performance testing described focuses solely on the algorithm's effect on image metrics (RMSE, SNR) compared to a defined ground truth, not on human reader performance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Yes, the performance testing described is precisely a standalone (algorithm only) performance evaluation. The improvements in RMSE and SNR are calculated based on the algorithm's output compared to the ground truth, without human intervention or interpretation as part of the study design.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Two types of ground truth were used:

"Golden Standard" Imaging: In the first option, a "long scan" and "reconstructed results" of real full-body human PET scans were accepted as ground truth. This is akin to using a high-fidelity scan as the reference.
Mathematical Phantom: In the second option, the "Zubal mathematical phantom" was used as the ground truth for simulated data.

8. The sample size for the training set

The document does not specify the sample size for the training set. It describes the algorithm as a "modification of the non-local means algorithm" and states it "finds the weights using the statistical analysis of the PET data and the data of additional modalities (MRI / CT)." This implies a more traditional image processing approach rather than a purely deep learning model requiring a distinct, large training dataset. The predicate device does use a CNN, implying a training set, but the subject device (Claritas iPET) is explicitly described as different in its core technology.

9. How the ground truth for the training set was established

Since the document does not specify a distinct "training set" in the context of a machine learning model, it also does not describe how ground truth was established for a training set. The algorithm's mechanism (non-local means guided by other modalities and statistical analysis of PET data) suggests a design that may not require a labeled training set in the same way a deep learning model would.

Summary

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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health and Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

Claritas HealthTech Pte. Ltd. % Devika Dutt COO 20A Tanjong Pagar Road Singapore, 088443 Singapore

Re: K213140

Trade/Device Name: Claritas iPET Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: LLZ Dated: September 24, 2021 Received: September 27, 2021

Dear Devika Dutt:

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

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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 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.

Thalia T. Mills, PhD 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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	DEPARTMENT OF HEALTH AND HUMAN SERVICES	Form Approved: OMB No. 0910-0120
	Food and Drug Administration	Expiration Date: 06/30/2023
	Indications for Use	See PRA Statement below.
510(k) Number (if known)	K213140
Device Name	Claritas iPET
Indications for Use (Describe)	Claritas iPET is an image processing software intended for use by radiologists and nuclear medicine physicians for noise reduction, sharpening, and resolution improvement of PET images (including PET/CT and PET/MRI) obtained with any kind of radionuclides, e.g. fluorodeoxyglucose (FDG). Enhanced images will be saved in DICOM files and exist in conjunction with original images.
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

The following information is provided in accordance with 21 CFR 807.92 for the Premarket 510(k) Summary:

Company:	Claritas HealthTech Pte. Ltd.20A Tanjong Pagar RoadSingapore, Singapore 088443
Contact:	Devika DuttCOOClaritas HealthTech Pte Ltd20A Tanjong Pagar RoadSingapore, Singapore 088443Telephone: (65) 9795-1921d.d@claritasco.com
Date Summary Prepared:	September 23, 2021

5.1 Submitter Information

5.2 Name of the Device

Trade Name:	Claritas iPET
Model Number:	V1.0
Common Name:	Image Enhancement System
Device:	System, Image Processing, Radiological
Regulation Name:	Medical Imaging Management and Processing System
Review Panel:	Radiology
Regulation Number:	21 CFR 892.2050
Device Class:	Class II
Product Code:	LLZ

5.3 Equivalence Claimed to Predicate Device

The Claritas iPET device is equivalent to the SubtlePET (K182336) device, manufactured by Subtle Medical, Inc.

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Trade Name:	SubtlePET
Manufacturer:	Subtle Medical, Inc., 880 Santa Cruz Ave, Suite 200Menlo Park, CA 94025
Regulation Number:	21 CFR 892.2050
Regulation Name:	Picture archiving and communications system
Device Class:	Class II
Product Code:	LLZ
510(k) Number:	K182336

5.4 Predicate Device Information

5.5 Indications for Use

510(k) Clearance Date:

November 30, 2018

5.6 Device Description

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PET volume in order for effective improvement. The sharpness. style and the detail of the visualization can be controlled by the user and can be compared to the visualization of the raw image data. During this process, no new feature is introduced that did not exist in the PET data, just the existing features are emphasized if they are also supported by the anatomy or suppressed if they are in the noise region and are not supported by the anatomy.

5.7 Substantial Equivalence Comparison of Technological Characteristics

The predicate device, SublePET and the subject device, Claritas iPET have similar technological characteristics. Both devices implement an image enhancement algorithm as the core of their image enhancement software. The predicate device implements an algorithm based on a trained convolutional network (CNN) and the subject device implements an algorithm based on 3D non-local means using a guide from a different 3D scan. The algorithms implemented in both devices enhance the image and reduce noise. Both devices predict the voxel value as a weighted sum of the values in the neighbourhood. The difference between the two devices is that the predicate device, SubtlePET finds the weights with a trained CNN, while the subject device, Claritas iPET finds the weights using the statistical analysis of the PET data and the data of additional modalities (MRI / CT). Verification and Validation testing and Performance testing for the subject device have been done on static and dynamic PET, PET/MR and PET/CT scans, and the test results confirm that the subject device is as safe and effective as the predicate device, hence the differences in the technological characteristics do not raise new risks related to the safety and effectiveness.

The table below shows the similarities and differences between the technological characteristics of the two devices.

Characteristics	Predicate Device	Subject Device
	SubtlePET [K182336]	Claritas iPET [K213140]
Device Class	Class II	Class II
Product Code	LLZ	LLZ
Intended Use	Image enhancement system whichis an image processing softwarefor image enhancement of PETimages including PET/CT andPET/MRI	Same
Indications forUse	SubtlePET is an image processingsoftware intended for use byradiologists and nuclearmedicine physicians for transfer,storage, and noise reduction offluorodeoxyglucose (FDG) andamyloid PET images (includingPET/CT and PET/MRI).	Claritas iPET is an imageprocessing software intended foruse by radiologists and nuclearmedicine physicians for noisereduction, sharpening, andresolution improvement of PETimages (including PET/CT andPET/MRI) obtained with any kindof radionuclides, e.g.fluorodeoxyglucose (FDG).Enhanced images will be saved in
Characteristics	Predicate Device	Subject Device
	SubtlePET [K182336]	Claritas iPET [K213140]DICOM files and exist inconjunction with original images.
PhysicalCharacteristics	Software package that operateson a virtual machine (VM)	Software package that operates on avirtual machine (VM) or deployedon a local computer.
Computer	Virtual machine host-compatiblesystem	Virtual machine host-compatiblesystem or local computer.
ImageProcessingEnhancementLocation	Onsite on the facility VMand/or offsite on the cloud VM,depending on the site'sconfiguration	Same in case of the PACS integratedversion. The stand-alone versionruns on the client computer.
DICOMstandardCompliance	The software processes DICOMcompliant image data	Same
OperatingSystem	CentOS 7 Linux	Windows/Linux
Modalities	Multi-modality; specificallyprocesses PET, PET/CT andPET/MR images	Same
User Interface	None - enhanced images areviewed on existing PACSworkstations	None - when integrated into existingPACS workstations, viewed onexisting PACS workstation.A user interface for stand-aloneversion visualizing 2D slices and 3Drendering for demo and researchpurposes.
Protocols	Standard scanner protocols	Same
CoreTechnology	Image Enhancement Algorithm	Same
ImageEnhancementAlgorithmDescription	The software employs aconvolutional neural network-based method in a pixel'sneighborhood to generate thevalue for each pixel. Using aresidual learning approach, thesoftware predicts the noisecomponents and structuralcomponents. The softwareseparates these components,which enhances the structurewhile simultaneously reducing thenoise.	The image enhancement algorithmis a modification of the non-localmeans algorithm where the filteringweights can be obtained fromhigher resolution and lower noisevoxel arrays obtained with othermodalities, i.e. CT or MR. Theresolution of the target is at leastthe maximum of the combinedmodalities, but may be higher.
Imageacquisition	The acquisition remains the same,i.e. the image processing can begenerated from multiplemodalities and with predefined orspecific acquisition protocol	The acquisition remains thesame.
Characteristics	Predicate Device	Subject Device
	SubtlePET [K182336]	Claritas iPET [K213140]
	settings.
Workflow	The product acts as a DICOM	Same in case of the PACS integrated
	node that receives DICOM 3.0	version.
	digital medical image data from
	the modality or another DICOM	The stand-alone version can input
	source, processes the data and	the slices of the PET, MR or CT
	then forwards the enhanced study	scans as DICOM files, interactively
	to the selected destination. This	visualizes the input and the output
	destination can be any DICOM	data, and saves the enhanced
	node, typically either the PACS	volume in DICOM files.
	system or a specific workstation.

5.8 Technological Characteristics Comparison Table

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Summary of Technological Characteristics Comparison Table

As per the table above the two devices are technologically similar and have similar indications of use. Verification, validation, and performance testing demonstrates the differences in the algorithm implemented by the subject and predicate, do not raise new questions of safety and effectiveness.

5.9 Performance Testing

Claritas iPET has been developed under the Quality System Regulations of ISO 13485. The design has been verified and validated according to the software development plan which follows IEC 62304:2006 and ISO 14971:2019 requirements.

Safety and performance have been evaluated and verified in accordance with the software specification to ensure the performance meets the specified requirements and the requirement of the FDA guidance document, titled, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices". Testing included design traceability confirming all requirement tracing is complete from design inputs and verification/validation and that all risk controls are implemented. Design validation testing simulated intended use to confirm that the end-to-end functionality of the Claritas iPET meets the design requirements.

Claritas iPET is an image processing software which reduces the noise without blurring organ boundaries and compromising the true signal. The accuracy of the processing and the reduction of the noise can be quantified by the Root Mean Square Error (RMSE) and the Signal to Noise Ratio (SNR) calculated before and after processing the data with iPET. Both measures need the ground truth of the analysed data.

In order to obtain the ground truth, we consider two different options:

In the first option, we enhanced real full body human PET scans. We executed a long scan ● and accepted the reconstructed results as ground truth. The PET scanning time is decomposed to uniform frames and the reconstruction process has been executed for subsets of the original frames demonstrating that the reconstruction quality can be maintained even for reduced scanning time and/or dosage if the Claritas iPET software is executed. The comparison is repeated for the case when the additional CT/MRI information is also utilized by the Claritas iPET software. We have concluded that the RMSE has been decrease by at least 10% and the SNR has been increased by at least 20%. However, for low dosage or

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short time scans, the improvement can be significantly higher, the RMSE is decreased by 50% and the SNR can be increased by 4-5 times. All tests have passed.

In the second option, we took the Zubal mathematical phantom, and considered it as the ground truth. The measured data are then generated by adding multiplicative, i.e. Poisson noise to the ground truth data. The noisy images are processed with the iPET software and the results are compared to the ground truth. The conclusions are similar to those of the measured scans. For high dosage and longer scans, we can expect 10-20% improvement in RMSE and SNR, which grows rapidly for low dosage or short scans. This test has passed.

5.10 Safety and Effectiveness

Based on the Claritas iPET software performance test results and incorporated risk minimisation methods in design, Claritas HealthTech Pte. Ltd. concludes that this device is substantially equivalent to the predicate device.

5.11 Substantial Equivalence Conclusion

Claritas iPET is an image enhancement software which has similar intended use and indications for use as the predicate device. The difference is that the predicate device sets the filtering weights using a pre-trained net, while Claritas iPET applies multi-channel non-local means filtering. The two devices have similar technological characteristics: both predicate device and subject device use image enhancement algorithms as their core technology. Performance test results and incorporated risk minimization methods demonstrate that Claritas iPET is as safe and effective as the predicate device. This 510(k) submission includes information on the Claritas iPET technological characteristics, as well as performance data and verification and validation activities demonstrating that Claritas iPET is substantially equivalent to the predicate device, and does not raise different questions of safety and effectiveness.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).