SPECT: The SPECT component is intended to detect or image the distribution of radionuclides in the body or organ (physiology), using the following techniques: whole body and tomographic imaging.

CT: The CT component is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data (anatomy) from either the same axial plane taken at different angles or spiral planes take at different angles.

SPECT +CT: The SPECT and CT components used together acquire SPECT/CT images. The SPECT images can be corrected for attenuation with the CT images, and can be combined (image registration) to merge the patient's physiological (SPECT) and anatomical (CT) images.

Device Description

The VERITON CT 300/400 Digital SPECT/CT System is a hybrid imaging system combining SPECT and multi-slice CT imaging for anatomical and functional assessment. The subject device introduces a software-only modification to the cleared system by adding VERITAS.AI Noise Reduction, an optional deep-learning post-processing feature integrated into the VERITON-CT Operator's Console. The AI module uses a convolutional neural network (CNN) to reduce noise in reconstructed SPECT images without altering acquisition parameters, hardware performance, radiation-emitting components, or quantitative reconstruction values.

Three workflow-specific models are included: Bone-IQ.AI, Thera-IQ.AI, and MIBG-IQ.AI.

All hardware, electrical safety characteristics, EMC characteristics, and imaging subsystems are unchanged from the predicate device.

AI/ML Overview

This FDA 510(k) clearance letter describes the VERITON CT Digital SPECT/CT System with an added VERITAS.AI Noise Reduction feature. Here's a breakdown of the acceptance criteria and the study proving the device meets them:

Acceptance Criteria and Reported Device Performance

Device: VERITON CT Digital SPECT/CT System with VERITAS.AI Noise Reduction (Software-only modification)
Purpose of AI Module: Reduce noise in reconstructed SPECT images.
Models included: Bone-IQ.AI, Thera-IQ.AI, and MIBG-IQ.AI.

Acceptance Criterion	Metric	Pre-specified Pass Criteria	Reported Device Performance
Signal Preservation	Lesion maximum voxel intensity (Bq/ml) - Linear regression R²	> 0.8	Bone (Tc-99m): R² = 0.99Soft Tissue (Lu-177 and mIBG): R² = 0.99
	Lesion maximum voxel intensity (Bq/ml) - Linear regression Slope	0.9–1.1	Bone (Tc-99m): Slope = 0.94Soft Tissue (Lu-177 and mIBG): Slope = 0.99
	Mean difference in lesion maximum voxel intensity (Bq/ml)	Not explicitly stated beyond slope	Bone: -6%Soft Tissue: -0.7%
Noise Suppression	Background SD/Mean ratio reduction	Not explicitly stated, implied improvement	Bone: 28% reductionSoft Tissue: 20% reduction
Blinded Visual Assessment	Independent rating of noise, artifacts, overall IQ, diagnostic confidence (5-point Likert scale)	Not explicitly stated, implied consistent "similar" or "better"	NR images consistently rated "similar" or "better" than non-processed (means 3.0–4.9/5 across tracers)
	Inter-reader agreement for visual assessment	Not explicitly stated, implied high agreement	92–100% across all domains (kappa analysis)

Study Details Proving Acceptance Criteria

Sample Size Used for the Test Set and Data Provenance:
- Total Patients in Dataset: 106 (multi-center, international)
- Evaluation Set (Test Set): 30 patients
  - 13 Bone (Tc-99m)
  - 17 Soft Tissue (10 Lu-177, 7 mIBG)
- Number of Samples (SPECT bed-position scans) in Evaluation Set: 30 images (one per patient).
- Data Provenance: Multi-center, international. The submission does not specify if the data was retrospective or prospective.
Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of those Experts:
- For Quantitative Analysis (Lesion ROIs): Not explicitly stated how many experts defined ROIs, but it mentions Lesion ROIs were defined in MIM (K233620) for quantitative analysis.
- For Visual Assessment: Two independent, board-certified nuclear medicine physicians. The experience level is not specified beyond being "board-certified."
Adjudication Method for the Test Set:
- For visual assessment, the two physicians rated images independently. Inter-reader agreement was calculated (92–100%), but no explicit adjudication method (like 2+1 or 3+1) is described for resolving disagreements. The high agreement suggests disputes were minimal or not a primary focus of the reporting.
If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of how much human readers improve with AI vs. without AI assistance:
- A formal MRMC comparative effectiveness study, comparing human readers with AI assistance vs. without AI assistance, was not explicitly described.
- The study focused on the performance of the AI noise reduction software by evaluating image quality for human readers and quantitative metrics. While human readers assessed the images, the study did not measure their diagnostic performance improvement with or without AI assistance.
If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, performance metrics like Signal Preservation (R² and Slope for lesion maximum voxel intensity) and Noise Suppression (Background SD/Mean ratio reduction) were evaluated as standalone algorithm performance before human visual assessment.
- The AI module "operates only on reconstructed SPECT images" and "produces secondary enhanced images while preserving original images," indicating a standalone processing step.
The Type of Ground Truth Used:
- For quantitative analysis (Signal Preservation, Noise Suppression): Non-processed clinical images (paired with NR-processed for direct comparison) served as the reference standard. Lesion Regions of Interest (ROIs) were defined for quantitative analysis.
- For visual assessment: The "reference standard" for comparison was the paired non-processed clinical images that the two nuclear medicine physicians compared against the NR-processed images. This is a comparative qualitative ground truth established by expert consensus.
The Sample Size for the Training Set:
- Training/Tuning Set: 76 patients
  - 39 Bone
  - 37 Lu-177
- Total SPECT bed-position (BP) scans: 369 (from 106 patients total dataset)
- Note: The submission mentions "Each patient contributed multiple BP scans (Bone: typically, 3 BPs; Lu-177: typically, 6 BPs)." If all 76 training patients contributed multiple BPs, the actual number of training images is significantly higher than 76.
How the Ground Truth for the Training Set was Established:
- The document states that the evaluation set was "not used in training/tuning," implying that the training set also utilized real patient data.
- The method for establishing ground truth for the training set is not explicitly detailed in the provided text. However, for a noise reduction algorithm, the ground truth typically involves the "original" (non-noise-reduced) image for training the model to predict a "cleaner" version. The quantitative metrics (signal preservation, noise suppression) tested on the evaluation set suggest that similar quantitative measures or comparisons with the original images were likely used during training and tuning.

Summary

FDA 510(k) Clearance Letter - VERITON CT Digital SPECT/CT System

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U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

Doc ID # 04017.07.00

January 13, 2026

Spectrum Dynamics Medical, Ltd.
Igor Naroditsky
Vice President, Quality and Regulatory Affairs
22 Bareket St. North Industrial Park
Caesarea, 3079837
Israel

Re: K254001
Trade/Device Name: VERITON CT 300 Series Digital SPECT/CT System (VERITON CT 316/364)
VERITON CT 400 Series Digital SPECT/CT System (VERITON CT 416/464)
Regulation Number: 21 CFR 892.1200
Regulation Name: Emission computed tomography system
Regulatory Class: Class II
Product Code: KPS, JAK, LLZ, QIH
Dated: December 15, 2025
Received: December 15, 2025

Dear Igor Naroditsky:

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 (the 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 available 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.

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K254001 - Igor Naroditsky
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

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 (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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-devices/medical-device-safety/medical-device-reporting-mdr-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/medical-devices/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

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K254001 - Igor Naroditsky
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the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/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,

Daniel M. Krainak, Ph.D.
Assistant Director
DHT8C: Division of Radiological Imaging
and Radiation Therapy Devices
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

Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions.
K254001

Please provide the device trade name(s).
VERITON CT 300 Series Digital SPECT/CT System (VERITON CT 316/364)
VERITON CT 400 Series Digital SPECT/CT System (VERITON CT 416/464)

Please provide your Indications for Use below.

SPECT: The SPECT component is intended to detect or image the distribution of radionuclides in the body or organ (physiology), using the following techniques: whole body and tomographic imaging.

CT: The CT component is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data (anatomy) from either the same axial plane taken at different angles or spiral planes take at different angles.

SPECT +CT: The SPECT and CT components used together acquire SPECT/CT images. The SPECT images can be corrected for attenuation with the CT images, and can be combined (image registration) to merge the patient's physiological (SPECT) and anatomical (CT) images.

Please select the types of uses (select one or both, as applicable).
☑ Prescription Use (21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) SUMMARY OF SAFETY AND EFFECTIVENESS

(As Required by 21 CFR 807.92)

510(k) Number: K254001
Date of submission: December 12, 2025

Submitter:
Spectrum Dynamics Medical Ltd.
22 Bareket St. North Industrial Park
Caesarea, Israel 3079837.

Submitter Contact:
Mr. Igor Naroditsky, VP QA/RA
Tel: + (972) 73-737-4548
Email: igorn@spectrum-dynamics.com

Device Trade Name:
VERITON CT 300 Series Digital SPECT/CT System (VERITON CT 316/364)
VERITON CT 400 Series Digital SPECT/CT System (VERITON CT 416/464)

Classification Panel: Radiology
Regulation: 21 CFR 892.1200 - Emission Computed Tomography
Product Codes: KPS, JAK, LLZ, QIH (Class II)
Common Name/Classification: Single Photon Emission Computed Tomography (SPECT)/Computed Tomography (CT) system

Predicate device:
Predicate Device: VERITON CT 300/400 Series Digital SPECT/CT System
Sponsor: Spectrum Dynamics Medical Ltd.
510(k) Number: K230600
The predicate has not been withdrawn or found to be unsafe or ineffective.

Device Description:

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convolutional neural network (CNN) to reduce noise in reconstructed SPECT images without altering acquisition parameters, hardware performance, radiation-emitting components, or quantitative reconstruction values.

Three workflow-specific models are included: Bone-IQ.AI, Thera-IQ.AI, and MIBG-IQ.AI.

All hardware, electrical safety characteristics, EMC characteristics, and imaging subsystems are unchanged from the predicate device.

Indications for Use:

Spectrum Dynamics Medical's VERITON system is intended for use by trained healthcare professionals to aid in the detection, localization, diagnosis, staging and restaging of lesions, diseases, and organ function. For evaluating diseases and disorders such as cardiovascular disease, neurological disorders, and trauma. System outcomes can be used to plan, guide, and monitor therapy. SPECT: The SPECT component is intended to detect or image the distribution of radionuclides in the body or organ (physiology), using the following techniques: whole body and tomographic imaging. CT: The CT component is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data (anatomy) from either the same axial plane taken at different angles or spiral planes take at different angles. SPECT +CT: The SPECT and CT components used together acquire SPECT/CT images. The SPECT images can be corrected for attenuation with the CT images, and can be combined (image registration) to merge the patient's physiological (SPECT) and anatomical (CT) images.

Summary of Technological Characteristics

The technological characteristics of the subject and predicate devices are identical except for the addition of the optional VERITAS.AI Noise Reduction feature.

The AI module:

Operates only on reconstructed SPECT images
Produces secondary enhanced images while preserving original images
Does not alter quantitative values, raw data, or dosimetry
Requires no changes to system hardware, acquisition workflow, or operator procedures

The modification does not introduce new materials, new energy sources, or new diagnostic functions

Note: This device should not be used to deviate from the approved dosing and administration instructions; see SPECT drug prescribing information for additional information.

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Summary of Performance Data

Non-Clinical Testing

Software Version 2.5.0 was developed under an IEC 62304 Class B SDLC and evaluated per FDA software and cybersecurity guidance, IEC 81001-5-1, AAMI TIR57, AAMI TIR97, ISO 14971, and IEC 62366-1.

Verification and validation activities included:

Software unit, integration, and system testing
AI model performance testing
Phantom-based image-quality testing (SNR, uniformity, resolution, detectability)
Cybersecurity risk assessment and SBOM vulnerability analysis
Usability and workflow testing
Diagnostic-quality assessment by a board-certified radiologist

All results met predefined acceptance criteria.

VERITAS.AI Validation

Summary Test Statistics, Acceptance Criteria, and Results

Signal Preservation: Lesion maximum voxel intensity (Bq/ml) was compared between NR-processed and non-processed images using linear regression.

Results:

Bone (Tc-99m): R² = 0.99, Slope = 0.94
Soft Tissue (Lu-177 and mIBG): R² = 0.99, Slope = 0.99
Pass criteria: R² > 0.8, Slope 0.9–1.1 (pre-specified)
Mean difference: −6% (Bone), −0.7% (Soft Tissue)

Noise Suppression:
Background SD/Mean ratio reduced by 28% (Bone) and 20% (Soft Tissue) after NR processing.

Blinded Visual Assessment:

Two board-certified nuclear medicine physicians independently rated images (noise, artifacts, overall IQ, diagnostic confidence) on a 5-point Likert scale.
NR images were consistently rated as "similar" or "better" than non-processed (means 3.0–4.9/5 across tracers).
Inter-reader agreement: 92–100% across all domains.

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Number of Individual Patients

Total patients in dataset: 106 (multi-center, international)
Evaluation set: 30 patients (not used in training/tuning), 13 Bone (Tc-99m), 17 Soft Tissue (10 Lu-177, 7 mIBG)

Number of Samples and Relationship

Total SPECT bed-position (BP) scans: 369 (from 106 patients)
Each patient contributed multiple BP scans (Bone: typically, 3 BPs; Lu-177: typically, 6 BPs).
Evaluation set: 30 images from 30 patients (one per patient).

Demographic Distribution

Gender: 44 females / 62 males
Age: 66.5 ± 21.6 years (overall); 45.6 ± 26.8 years (evaluation set)
BMI: 24.9 ± 4.2 (overall); 24.3 ± 3.8 (evaluation set)
Ethnicity: Ethnicity does not affect SPECT image physics or algorithm performance. Image quality depends on tracer biodistribution, acquisition protocol, and patient size (BMI), not genetic factors. Including age, gender, and BMI ensures clinical variability is covered.

Clinical Subgroups and Confounders

Clinical subgroups: Bone (Tc-99m), Soft Tissue (Lu-177, mIBG)

Confounders addressed:

Detected counts/statistics (primary driver of noise)
Motion artifacts and patient positioning QC-screened at acquisition
Daily QC for detector performance

Equipment and Protocols

System: VERITON-CT whole-body SPECT/CT, software v2.5.0

Acquisition:

Bone: 3 BPs (head-to-thighs)
Lu-177: 6 BPs (head-to-toes)

Reconstruction: OSEM, 2.46 mm³ voxels, standard parameters per tracer
QC: Daily NEMA NU-1:2023 compliance

Reference Standard ("Truthing") and Inter-Observer Agreement

Reference standard: Non-processed clinical images (paired with NR-processed for direct comparison)
Lesion ROIs: Defined in MIM (K233620) for quantitative analysis

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Visual assessment: Two independent, board-certified nuclear medicine physicians
Inter-reader agreement: 92–100% (kappa analysis)

Independence of Test Data from Training Data

Patient-level split:

76 patients (39 Bone, 37 Lu-177) for training/tuning
30 patients (13 Bone, 17 Soft Tissue) held out for evaluation only

No overlap between training/tuning and evaluation sets

EMC / Electrical Safety

EMC and electrical safety retesting were not required, as the modification is software-only and does not affect emissions, immunity, or safety-related hardware functions. Predicate compliance to IEC 60601-1 and IEC 60601-1-2 remains applicable.

No clinical testing was required to support substantial equivalence.

Substantial Equivalence Discussion

The subject device has the same intended use, principles of operation, hardware configuration, and fundamental scientific technology as the predicate device. The addition of the optional VERITAS.AI Noise Reduction feature does not alter performance characteristics related to safety or effectiveness. Verification and validation demonstrate that the modified device performs as intended and does not raise new questions of safety or effectiveness.

Conclusion

Based on the submitted information, including device description, technological comparison, non-clinical performance testing, and risk management, the VERITON® CT 300/400 Digital SPECT/CT System with Software Version 2.5.0 is as safe, as effective, and substantially equivalent to the predicate device (K230600).

Regulation Number and Section

§ 892.1200 Emission computed tomography system.

(a)
Identification. An emission computed tomography system is a device intended to detect the location and distribution of gamma ray- and positron-emitting radionuclides in the body and produce cross-sectional images through computer reconstruction of the data. This generic type of device may include signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts, and accessories.(b)
Classification. Class II.