(14 days)
AutoSPECT® produces images, which depict the three-dimensional distribution of radiopharmaceutical tracers in a patient. This software is intended to provide enhancements to gamma camera emission image processing by automating previously manual image processing functions, providing manual and automated motion correction, providing enhanced reconstruction algorithms that include resolution recovery, scatter correction, noise compensation, and attenuation correction via application of a transmission dataset.
AutoSPECT is a software application that produces images, which depict the threedimensional distribution of radiopharmaceutical tracers in a patient via automatic or manual processing. One or more cardiac SPECT, gated SPECT, or MCD cardiac data sets may be processed automatically using AutoSPECT. Additionally, one or more non-cardiac SPECT, gated SPECT, or MCD data sets may be processed manually. AutoSPECT contains basic data-processing algorithms that have been cleared previously; in addition to enhanced data reconstruction algorithms that include scatter correction, resolution recovery, map-based attenuation correction, and OSEM (Astonish SPECT) reconstruction.
The AutoSPECT software option may be used on images from a gamma camera system that are DICOM 3.0 compatible. The following data sets may be used:
- Cardiac, brain, or other (bone, liver, etc.) SPECT datascts .
- . Gated SPECT datasets
- . Vantage SPECT datasets
- SPECT-CT datasets .
- Total Body SPECT datasets
- MCD and MCD-AC datasets .
AutoSPECT provides the user three options for automatically processing cardiac datasets: AutoAll, Auto Recon, and Auto Reorient. Each option is described in greater detail in the software description section.
AutoSPECT also allows the user to process non-cardiac SPECT and MCD datasets. In this case, the operator manually positions the reconstruction limit lines to reconstruct transverse data sets. If necessary, the data set can be reoriented manually by positioning the azimuth, elevation, and twist lines to the desired locations.
In addition, the capability of processing groups of SPECT data sets in a batch mode fashion is provided. Once the operator has selected the datasets and determined the processing method, AutoSPECT processes the first dataset, followed by all remaining datasets without further interaction from the user.
AutoSPECT application runs on Microsoft Windows XP Professional environment. The minimum hardware requirements is listed:
- . Intel x86/Pentium class processor > 1 GHz ;
- Graphics capability must meet or exceed 1280x1024 pixels with 32 bit pixel . depth;
- . 30 Gb of disk space (minimum);
- . 512 Mb of DRAM (minimum);
- . 10/100 BaseTX Ethernet interface;
- . Port capable of supporting a dongle;
- . CD drive- capable of reading and writing:
- 56Kbps modem (minimum) .
This document is a 510(k) summary for the AutoSPECT® device, a software application for processing gamma camera emission images. The submission aims to demonstrate substantial equivalence to a predicate device.
1. Table of Acceptance Criteria and Reported Device Performance:
The document does not provide a table of acceptance criteria with specific quantitative metrics or a study demonstrating the device meets those criteria. The 510(k) summary focuses on demonstrating substantial equivalence to a predicate device based on similar intended use and technological characteristics, rather than establishing new performance criteria through a specific study with defined acceptance thresholds.
The "performance" described is largely qualitative, focusing on enhanced reconstruction algorithms.
2. Sample Size Used for the Test Set and Data Provenance:
The document does not specify a test set size nor the provenance (e.g., country of origin, retrospective/prospective) of any data used for testing. The submission is a comparison to a predicate device and description of new features, not a report on a clinical performance study with a test set.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
Not applicable. The document does not describe a study involving a test set with expert-established ground truth.
4. Adjudication Method for the Test Set:
Not applicable. The document does not describe a study involving a test set with an adjudication method.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and effect size:
No, an MRMC comparative effectiveness study is not mentioned or described in this document. The submission focuses on device features and comparison to a predicate, not human reader performance with or without the AI.
6. If a Standalone (algorithm only without human-in-the-loop performance) was done:
No, a standalone algorithm performance study is not mentioned or described. The document describes a software application that processes images for human interpretation, not a fully automated diagnostic system.
7. The Type of Ground Truth Used:
Not applicable. The document does not describe a study involving any form of ground truth for performance evaluation.
8. The Sample Size for the Training Set:
Not applicable. The document does not provide details of a training set as it describes a software application and its features, not a machine learning model developed with a training dataset.
9. How the Ground Truth for the Training Set was Established:
Not applicable. As no training set is mentioned, the method for establishing its ground truth is also not applicable.
§ 892.1200 Emission computed tomography system.
(a)
Identification. An emission computed tomography system is a device intended to detect the location and distribution of gamma ray- and positron-emitting radionuclides in the body and produce cross-sectional images through computer reconstruction of the data. This generic type of device may include signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts, and accessories.(b)
Classification. Class II.