(88 days)
The TeraRecon RTR-2000 Image Reconstruction System should be used when it is desirable to view scanned medical images immediately, rather than wait long periods of time for reconstruction. Indications would include monitoring of radiological examinations for patient movement. It is possible that patient movement during the middle of an examination, which may require re-scanning, would not be detected until after the completion of the exam when all of the images are reconstructed and thus visualized. High speed image reconstruction allows for real-time visualization of the images. Such real-time visualization of images gives the immediate visual feedback necessary to monitor the progress of examinations in efforts to maximize scanning accuracy and minimize radiation dose to the patient.
In short, real-time visualization of images is indicated in cases where the user prefers immediate visual feedback as opposed to having to wait long periods of time for image reconstruction.
The TERARECON, INC. RTR-2000 real-time image reconstruction system acquires medical image data from such medical imaging devices as CT and reconstructs the "raw" data into visible images. The RTR-2000 system reconstructs images with such high performance that the images This stand-alone high performance image are viewed in real-time. reconstruction system is offered as an upgrade to existing imaging devices, and is not intended to replace the devices' existing reconstruction system. Rather, it is intended to serve as an alternative means of viewing medical images, particularly where real-time visualization of images is beneficial.
Here's a breakdown of the acceptance criteria and study information for the RTR-2000 Medical Image Reconstruction and Processing Systems, based on the provided text:
Acceptance Criteria and Device Performance
| Acceptance Criteria Category | Acceptance Criteria (Implicit) | Reported Device Performance (Implicit) |
|---|---|---|
| Real-time Image Reconstruction | Ability to reconstruct images with high performance for real-time viewing. | The RTR-2000 system reconstructs images with such high performance that the images are viewed in real-time. |
| Image Quality | Produce visible images from "raw" medical image data. | The device acquires medical image data...and reconstructs the "raw" data into visible images. (No specific quality metrics are provided, but the outcome of "visible images" is stated). |
| Throughput Improvements | Provide solutions to medical image reconstruction throughput problems. | Offers enhanced operator flexibility and dramatically speeding up the image reconstruction process. |
| Safety and Effectiveness | No new intended uses that will affect the safety and effectiveness of the host system. | Other than dramatically speeding up the image reconstruction process, there are no perceived or imagined new intended uses which will affect the safety and effectiveness of the host system. |
| Clinical Utility | Allow for near real-time viewing, supporting existing system clinical uses. | Allows for near real-time viewing, in turn, supporting existing system clinical uses by offering enhanced operator flexibility. Indicated for monitoring of radiological examinations for patient movement to maximize scanning accuracy and minimize radiation dose. |
Study Information
It's important to note that the provided 510(k) summary is primarily a regulatory document for demonstrating substantial equivalence. It does not describe a detailed clinical study in the way a modern clinical trial report would. Instead, the "study" demonstrating acceptance is largely based on the comparison to a predicate device and the inherent functional description of the new device.
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Sample Size used for the test set and the data provenance:
- Test Set Sample Size: Not specified.
- Data Provenance: Not specified. The document states the device acquires medical image data from such medical imaging devices as CT, but doesn't detail the origin or characteristics of the test data used for validation. Given the era (1997) and the nature of the submission for an image reconstruction system (as opposed to a diagnostic AI), specific test sets for performance metrics like accuracy are typically not publicly detailed in these types of submissions, if they were used at all beyond internal engineering validation.
- Retrospective or Prospective: Not specified. Implied to be a functional demonstration rather than a formal clinical study with retrospective or prospective data.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not specified. There is no mention of expert-established ground truth in the context of what would typically be a diagnostic performance study. The "ground truth" here is implied to be the accurate reconstruction of images for real-time viewing.
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Adjudication method for the test set: Not applicable. No formal adjudication process is described as there isn't a human diagnostic decision being evaluated for accuracy against ground truth.
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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: Not applicable. This document does not describe an MRMC study. The device is for image reconstruction and processing, improving throughput, not for diagnostic assistance that would typically be evaluated in an MRMC study.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Yes, implicitly. The core function of the device is image reconstruction, which is an algorithmic process. The claim is that the system (algorithm + hardware) performs this reconstruction in real-time. The "performance" described is the speed and ability to generate visible images, which is a standalone algorithmic function.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not explicitly described in terms of a clinical "ground truth" for diagnostic accuracy. The ground truth for this device's performance would be the accurate and complete reconstruction of the input raw data into a visible image. This is a technical ground truth rather than a clinical one.
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The sample size for the training set: Not applicable/Not specified. This is a real-time image reconstruction system, not a machine learning model that would typically have a "training set" in the modern sense. Its development would involve engineering, signal processing, and optimization, rather than training on a dataset of labeled images.
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How the ground truth for the training set was established: Not applicable/Not specified, for the reasons mentioned above.
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.