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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.

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.

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.

Study Details Proving Acceptance Criteria

1. 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.

2. 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."

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

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