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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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TruSPECT is intended for acceptance, transfer, display, storage, and processing of images for detection of radioisotope tracer uptakes in the patient's body. The device using various processing modes supported by the various clinical applications and various features designed to enhance image quality. The emission computerized tomography data can be coupled with registered and/or fused CT/MR scans and with physiological signals in order to depict, localize, and/or quantify the distribution of radionuclide tracers and anatomical structures in scanned body tissue for clinical diagnostic purposes. The acquired tomographic image may undergo emission-based attenuation correction.

Visualization tools include segmentation, colour coding, and polar maps. Analysis tools include Quantitative Perfusion SPECT (QPS), Quantitative Gated SPECT (QGS) and Quantitative Blood Pool Gated SPECT (QBS) measurements, Multi Gated Acquisition (MUGA) and Heart-to-Mediastinum activity ratio (H/M).

The system also includes reporting tools for formatting findings and user selected areas of interest. It is capable of processing and displaying the acquired information in traditional formats, as well as in three-dimensional renderings, and in various forms of animated sequences, showing kinetic attributes of the imaged organs.

TruSPECT is based on Windows operating system. Due to special customer requirements and the clinical focus the TruSPECT can be configured with different combinations of Windows OS based software options and clinical applications which are intended to assist the physician in diagnosis and/or treatment planning. This includes commercially available post-processing software packages.

TruSPECT is a processing workstation primarily intended for, but not limited to cardiac applications. The workstation can be integrated with the D-SPECT cardiac scanner system or used as a standalone post-processing station.

The TruSPECT Processing Station is a software-only medical device (SaMD) designed to operate on a dedicated, high-performance computer platform. It is distributed as pre-installed medical imaging software intended to support image visualization, quantitation, analysis, and comparison across multiple imaging modalities and acquisition time points. The software supports both functional imaging modalities, such as Single Photon Emission Computed Tomography (SPECT) and Nuclear Medicine (NM), as well as anatomical imaging modalities, such as Computed Tomography (CT).

The system enables integration, display, and analysis of multimodal image datasets to assist qualified healthcare professionals in image review and interpretation within the clinical workflow. The software is intended for use by trained medical professionals and assists in image assessment for various clinical applications, including but not limited to cardiology, electrophysiology, and organ function evaluation. The software does not perform automated diagnosis and does not replace the clinical judgment of the user.

The TruSPECT software operates on the Microsoft Windows® operating system and can be configured with various software modules and clinical applications according to user requirements and intended use. The configuration may include proprietary Spectrum Dynamics modules and commercially available third-party post-processing software packages operating within the TruSPECT framework.

The modified TruSPECT system integrates the TruClear AI application as part of its software suite. The TruClear AI module is a software-based image processing component designed to assist in the enhancement of SPECT image data acquired on the TruSPECT system. The module operates within the existing reconstruction and review workflow and does not alter the system's intended use, indications for use, or fundamental technology.

Verification and validation activities were performed to confirm that the addition of the TruClear AI module functions as intended and that overall system performance remains consistent with the previously cleared TruSPECT configuration. These activities included performance evaluations using simulated phantom datasets and representative clinical image data, conducted in accordance with FDA guidance. The results demonstrated that the modified TruSPECT system incorporating TruClear AI meets all predefined performance specifications and continues to operate within the parameters of its intended clinical use.

Here's a breakdown of the acceptance criteria and study details for the TruClear AI module of the TruSPECT Processing Station, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Study Details

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: 24 patients (8 female, 16 male), which yielded 74 images.
   • Data Provenance: Multi-center, retrospective dataset from three hospitals in the UK and Germany.

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

   • Number of Experts: Two (2)
   • Qualifications of Experts: Independent, board-certified nuclear medicine physicians.

3. Adjudication method for the test set:

   • The document states "two independent, board-certified nuclear medicine physicians visually compared denoised low-count images to the high-count reference using a 5-point Likert scale; inter-observer percent agreement after dichotomization (scores ≥3 vs <3) was 97–100% across key metrics." This suggests a consensus-based approach for establishing some aspect of the ground truth, particularly for the visual similarity assessment, though not explicitly a formal 2+1 or 3+1 adjudication for defining disease status. The reference standard itself was the high-count image, and the experts were comparing the derived AI-processed images to this reference.

4. 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:

   • An MRMC comparative effectiveness study was not explicitly described in terms of human readers improving with AI vs. without AI assistance. The study focused on validating the AI algorithm's output against a reference standard (high-count image) using visual and quantitative assessment. The two nuclear medicine physicians visually compared the denoised images to the reference, not their own diagnostic performance with and without AI.

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

   • Yes, a standalone performance assessment of the algorithm was conducted. The quantitative evaluation using the FDA-cleared Cedars-Sinai QPS/QGS to derive perfusion and functional parameters (TPD, volume, EDV, LVEF) directly compared the algorithm's output on low-count images (after denoising) to the high-count reference images. The Bland-Altman and correlation analyses are indicators of standalone performance.

6. The type of ground truth used:

   • The primary reference standard (ground truth) for the study was the clinical routine high-count SPECT image (~1.0 MCounts) acquired under standard D-SPECT protocols.
   • For quantitative parameters, FDA-cleared Cedars-Sinai QPS/QGS was used on the high-count reference images to derive the ground truth values for perfusion and functional parameters (TPD, volume, EDV, LVEF).
   • For visual assessment, the "high-count reference" images served as the ground truth for comparison.

7. The sample size for the training set:

   • The total dataset was 352 patients. The training/tuning set consisted of a portion of these patients; specifically, the "held-out test set" was 24 patients, meaning the remaining 328 patients (352 - 24) were used for training and tuning the algorithm.

8. How the ground truth for the training set was established:

   • The document implies the same ground truth methodology was used for the training set as for the test set. The algorithm was trained to transform low-count images to effectively match the characteristics of the clinical routine high-count SPECT image as the "gold standard." The Cedars-Sinai QPS/QGS would also have been used on these high-count images to generate the quantitative targets for training, allowing the AI to learn to derive similar quantitative parameters from denoised low-count images.

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