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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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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 VERITONCT 300/400 Series consist of back - to - back Single Photon Emission Computed Tomography (SPECT) and X-Ray Computed Tomography (CT) scanners. The SPECT subsystem images and measures the distribution of radiopharmaceuticals in humans for the purpose of determining various metabolic (molecular) and physiologic functions within the human body and integrates CT's anatomical detail for precise reference of the location of the metabolic activity. CT subsystem produces cross-sectional images of the body by computer reconstruction of X-Ray transmission data from either the same axial plane taken at different angles or spiral planes taken at different angles. The system can be used as an integrated SPECT and CT modality while also enabling independent functionality of SPECT and CT as standalone diagnostic imaging devices.

All models employ a same software version 2.3.0

The proposed series consists of four variations:

Modifications in VERITON Family include:

Enhanced CZT module's introduction to support an extended energy range

The provided text describes the VERITON CT 300/400 Series Digital SPECT/CT System, a modification of previously cleared devices (VERITON CT 64 and VERITON CT 16). The submission focuses on demonstrating substantial equivalence to the predicate device VERITON CT 16 (K190457).

Here's an analysis of the acceptance criteria and study information, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document mentions that "All testing has met the acceptance criteria for the proposed device" for various performance metrics, but it does not explicitly list the specific acceptance criteria values for each test. It only states that the tests were conducted against the predicate device's performance.

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

• Sample Size (Clinical Evaluation): The document states "Sample clinical images." It does not specify the number of images or cases used in this clinical evaluation.
• Data Provenance (Clinical Evaluation): The document does not specify the country of origin of the clinical data nor whether it was retrospective or prospective.

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

• Number of Experts: "a board-certified radiologist" (singular).
• Qualifications of Experts: "board-certified radiologist." The document does not provide information on the years of experience of this radiologist.

4. Adjudication Method:

• The document describes a single board-certified radiologist evaluating "sample clinical images" to confirm diagnostic quality. This indicates no formal adjudication method (like 2+1 or 3+1) was used, as it was a single reader assessment.

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

• No, an MRMC comparative effectiveness study was not explicitly mentioned or described. The clinical evaluation involved a single radiologist confirming diagnostic quality, not comparing human readers with and without AI assistance to measure an effect size.

6. Standalone Performance Study:

• Yes, a standalone performance was done for several technical aspects. The document describes "non-clinical performance evaluations" using "a variety of test methods and phantoms" to characterize the performance of the device's functionality. This included areas like energy resolution, count rate linearity, uniformity, system resolution, and lesion detectability. These tests assess the algorithm's direct output on controlled inputs (phantoms) or simulated conditions, rather than human-in-the-loop performance.

7. Type of Ground Truth Used:

• For Non-Clinical Performance Tests (Standalone): The ground truth would typically be established by the characteristics of the phantoms used and the expected physical and mathematical properties of the SPECT/CT system being tested. These are objective measures based on known inputs.
• For Clinical Evaluation: The ground truth for this simple evaluation was the expert opinion/assessment of diagnostic quality by a board-certified radiologist. It doesn't mention more definitive ground truths like pathology or long-term clinical outcomes.

8. Sample Size for the Training Set:

• The document does not provide any information regarding the sample size of a training set. Given that the device is presented as a modification of existing systems, and the focus is on hardware/software modifications and demonstrating equivalence, it's possible that a new, extensive training set was not required if the underlying algorithms are largely unchanged. However, the document does not clarify this.

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

• Since there is no information on a training set, there is also no information on how its ground truth was established.

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