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TruSPECT is intended for acceptance, 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 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 application can be integrated with the D-SPECT cardiac scanner system or used as a standalone post-processing station.

The TruSPECT is a Nuclear Medicine Software system designed for nuclear medicine images' post processing and further review procedures for detection of the radioisotope tracer uptake in the patient's body. Thus, using a variety of post processing features oriented to specific clinical applications.

SUMO Workflow enables visual evaluation and assessment of the sympathetic innervation system of the heart by quantification of uptake ratios between regions of interest, identifying discreet uptake areas of AdreViewtm (lobenguane 123 Injection) or similar agents within the heart. The results generated by the SUMO workflow can be displayed on the D-SPECT processing station and additionally, can be exported to EP systems. It can also be used by the physician to aid in ablation treatment planning by electrophysiologists.

D-SPECT Dynamic CFR is a workflow for visualization, and quantification of specific areas of attention. 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 providing quantitative blood flow measurements of SPECT images. The application provides visualization and measurement tools for both qualitative and quantitative visualization and input data evaluation. It provides automated and manual tools for orientation and segmentation of the myocardium. The software calculates myocardial blood flow measurements and provides tools, such as a database comparison workflow, to the clinician to evaluate these outcomes.

TruSPECT CT based Attenuation Correction (CTAC) is an application that removes soft tissue artifacts from SPECT images. The goal is to minimize the impact of attenuation to provide more consistent and reliable reading images. The CT Attenuation Correction (CTAC) uses a second form of imaging (CT) to develop a density map of each patient and correct the SPECT image accordingly.

TruCorr enhances the user's ability to visualize the acquired information (by way of a single clear image) - thus optimizing what would otherwise be a disjointed visual comparison. It is an Emission Based attenuation correction application using the deep learning model which was trained to directly estimate attenuation corrected SPECT images from non-attenuation corrected ones without the use of any anatomical images.

This document describes the TruSPECT Radiological Image Processing Station (K212230), which is a modification to the D-SPECT® Processing and Reviewing Workstation (K160120). The key modification is the TruCorr application, an image attenuation correction method that integrates pre-trained neural networks in the iteration reconstruction process.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in a tabular format. However, it mentions that the "performance testing for the AI-based algorithm for iteration reconstruction process to control image attenuation have been evaluated and demonstrates algorithm's performance and uses test datasets of representative clinical exams." The evaluation method involved a 5-point Likert scale by experts.

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

• Sample Size: The document refers to "test datasets of representative clinical exams" but does not specify the exact number of cases or images.
• Data Provenance: Not explicitly stated. It mentions "representative clinical exams," suggesting real-world patient data, but the country of origin or whether it was retrospective or prospective is not provided.

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

• Number of Experts: Not explicitly stated, but it mentions "experienced NM Physicists and Physicians." The plural form indicates more than one expert.
• Qualifications of Experts: "experienced NM Physicists and Physicians." No specific years of experience are provided.

4. Adjudication Method for the Test Set

The document states that "experienced NM Physicists and Physicians... were used as ground truth. The NM Physicists and Physicians also performed the algorithm evaluation. They reviewed the results and scored them using a 5-point Likert scale." This implies an expert-driven evaluation, but a specific adjudication method like "2+1" or "3+1" is not mentioned. It could be that experts individually scored the images, or they reached a consensus for the ground truth and then individually evaluated the algorithm's output.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

• There is no mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study being done to quantify the improvement of human readers with AI assistance versus without AI assistance. The evaluation focused on the algorithm's standalone performance as assessed by experts.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, a standalone performance evaluation was done. The "performance testing for the AI-based algorithm... have been evaluated" and "NM Physicists and Physicians also performed the algorithm evaluation. They reviewed the results and scored them..." This indicates the algorithm's output was evaluated directly.

7. The Type of Ground Truth Used

• Expert Consensus/Manual Assessment: The ground truth for the test set was established by "experienced NM Physicists and Physicians" who "manually accessed" the clinical exams.

8. The Sample Size for the Training Set

• The document states that the TruCorr deep learning model "was trained to directly estimate attenuation corrected SPECT images from non-attenuation corrected ones without the use of any anatomical images." However, the sample size for the training set is not provided. It mentions the use of "nonadaptive machine learning algorithms trained with clinical and/or artificial data," suggesting a combination of real and synthetic data.

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

• The document implies that the "pre-trained neural networks" for TruCorr were trained to estimate "attenuation corrected SPECT images from non-attenuation corrected ones." While it doesn't explicitly state how the "ground truth" for the training set was established, for a deep learning model to generate "attenuation corrected SPECT images," the training data would typically consist of pairs of non-attenuated and corresponding accurately attenuated SPECT images. This typically involves either:
  • Images that have undergone a known, reliable attenuation correction method (e.g., CTAC) used as the target for the AI.
  • Simulated data where the true attenuation is known.
  • Expert-defined "ideal" attenuation correction.

The text does not detail this process but refers to "clinical and/or artificial data" being used for training.

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