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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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The D-SPECT® Cardiac Scanner is an emission computed tomography system intended for detection of radioisotope tracer uptake in the patient's body and produce cross-sectional images through computer reconstruction of the data. The system uses a variety of scanning modes supported by various acquisition types and imaging features designed to enhance image quality. The scanning modes include planar mode (Static, Multi-gated and Dynamic) and tomographic mode (Static, Multi-gated and Dynamic). The acquisition types include single and multi-isotope/multi peak frame/list mode single-photon imaging. The imaging-enhancement features include gating by physiological signals, real-time body movement control, and low count rate (low dose) acquisition without loss of image quality.

The D-SPECT® Cardiac Scanner may consist and display equipment contain data and image processing software to produce images in a variety of trans-axial and reformatted planes. To perform analysis and uptake quantitation and to apply the appropriate filters. The system utilizes combined images for attenuation corrected imaging as well as functional and anatomical mapping imaging (localization, registration and fusion).

The D-SPECT® Cardiac Scanner is intended for use by the appropriately trained healthcare professionals to aid in detecting, localizing and diagnosing of (but not limited) cardiac or individual organs diseases. The system output can be used for planning, guiding, and monitoring therapy.

Spectrum Dynamics D-SPECT® Cardiac Scanner System is a single photon emission computing tomography system intended for detection of radioisotope tracer uptake in the body and produce cross-sectional images through computer reconstruction of the data.

The device uses a variety of scanning modes supported by various acquisition types and imaging features designed to enhance image quality. System's scanning modes include planar mode (Static, Multi-Gated and Dynamic) and tomographic mode (Static, Multi-Gated and Dynamic). The acquisition types include single and multi-isotope/multi peak frame/list mode single-photon imaging. The imaging-enhancement features include gating by physiological signals, real-time body movement control. The device may proceed a low count rate (low dose) acquisition without loss of image quality. The device may utilize variate modalities to create attenuation corrected images along with functional and anatomical mapping imaging (localization, registration and fusion).

The device is a high performance and compact Single Photon Emission Computed Tomography system intended for imaging of the breast and additional small organs in order to aid in the evaluation of lesions.

The system detectors support radionuclides within the energy range of 40 -170 Kev.

D-SPECT® Cardiac Scanner System comprising detector head, gantry, patient supports, uninterruptible power supply (UPS), image display and processing equipment, gating and real-time body movement control tools, interconnecting cables and related appurtenances.

The device is available in two models, D-SPECT with nine detectors configuration and the D-SPECT L with six detectors configuration.

D-SPECT® Cardiac Scanner System consist integrated signal analysis and display equipment or may use an FDA cleared D-SPECT® Processing and Reviewing Workstation (K160120) for image processing.

The provided text describes the D-SPECT® Cardiac Scanner System, an emission computed tomography system. However, the document does not contain explicit acceptance criteria and a detailed study report proving the device meets those criteria with specific performance metrics.

The text mentions "All testing results are met the predetermined acceptance values" but does not elaborate on what these values are or the specific results. It also states "The device efficacy and safety as well as the performance specifications remain the same," referring to a previous 510(k) cleared device (K110507). Without the original K110507 submission or a more detailed current submission, specific acceptance criteria and performance data are unavailable in this document.

Therefore, many of the requested details cannot be extracted directly from the provided text.

Here is what can be inferred or stated based on the text provided:

1. A table of acceptance criteria and the reported device performance:

This information is not explicitly stated in the provided document. The document mentions: "All testing results are met the predetermined acceptance values." and "The device efficacy and safety as well as the performance specifications remain the same." This implies that the device's performance matches or exceeds the criteria established for the predicate device (K110507) and potentially international standards, but the specific metrics are not provided.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

This information is not available in the provided text. The document refers to "performance validation testing" but does not specify details about patient data, sample sizes, or provenance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

This information is not available in the provided text. The document mentions the device is intended for use by "appropriately trained healthcare professionals," but it doesn't describe any studies involving experts establishing ground truth.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

This information is not available in the provided text.

5. 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:

This information is not available in the provided text. The device described is a SPECT imaging system, not an AI-assisted diagnostic tool, so an MRMC study comparing AI assistance would not be directly relevant to its fundamental performance.

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

This device is an imaging system, not an algorithm in the typical sense of a standalone AI diagnostic tool. Its "performance" refers to the quality of the images it produces and its ability to detect radioisotope uptake. The document indicates that "performance validation testing conducted according to NEMA NU-1:2012," which are standards for performance measurements of SPECT systems. This would refer to the technical performance of the device itself.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

This information is not explicitly stated in the provided text. For SPECT imaging systems, ground truth for performance testing typically involves phantoms with known activity distributions to assess resolution, sensitivity, uniformity, etc., and potentially clinical correlation for certain aspects of image quality, but the document does not specify the method used.

8. The sample size for the training set:

This information is not applicable/available. The D-SPECT Cardiac Scanner System is a hardware imaging device that uses reconstruction algorithms, but the text does not describe an AI/machine learning component that would require a "training set" in the context of device performance claims.

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

This information is not applicable/available for the reasons stated above.

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