Search Results

The Field Effects MRI system produces cross-sectional images:

• Anatomical Region: General body anatomy, including head, spine, torso, and extremities
• Nucleus excited: 1H nuclei (Proton)
• Diagnostic uses: Images correspond to the distribution of 1H nuclei exhibiting nuclear magnetic resonance, with image intensity dependent upon NMR parameters, including spin-lattice relaxation time (T1) spin-spin relaxation time (T2) density of nuclei (p) flow velocity chemical shift (δ)
• Clinical use: lmages may be interpreted by a trained physician to yield information that can be useful in the determination of a diagnosis
• RF Coils: Head - quadrature Cervical Spine - quadrature Lumbar Spine, Thoracic Spine, and Abdomen - quadrature Knee - linear
• Image Acquisition Spin Echo Gradient Echo (GE,RGE) Fast Spin Echo (FSE) Fast Dual Echo (FDE) 3D Fast Gradient Echo (3DGE, 3DRGE) Inversion Recovery with Fast Spin Echo (FSE STIR, FSE FLAIR)

Identical to the IMiG-MRI 510(k) K963953. The original IMiG-MRI system was renamed as TREX MRI in 510(k) submission K982157. As a result of changing business arrangements, the TREX MRI is now renamed as the Field Effects MRI System. In addition, following successful FDA determination of substantial equivalence of this 510(k) submission for the Image Processing software, Field Effects will cease commercialization efforts and support for the Field Effects MRI System. Service Support will be available through SMIS (refer to ttem 9, Address of Manufacturing Facilities for SMIS contact information). With the exception of the device modifications identified in this 510(k) submission, and the renaming of the product, the Field Effects MRI System is identical to the original IMiG-MRI system (and to the TREX.MRI).

The Field Effects MRI 0.15T Elliptical MRI Magnetic Resonance Diagnostic Device is being enhanced by image processing software to increase the clinical utility of the Field Effects MRI System in the stationary configuration.

The provided document, K983902, is a 510(k) Summary of Safety and Effectiveness for the Field Effects MRI device, specifically regarding an enhancement to its image processing software. It is a premarket notification from 1999.

Crucially, this document is a 510(k) submission applying to an MRI system itself, not an AI/ML-driven medical device in the contemporary sense. The "Image Processing Software" mentioned is part of the core functionality of the MRI to produce images, not a separate AI algorithm designed to interpret or enhance those images beyond the standard function of forming the image.

Therefore, the concepts of acceptance criteria, study design to prove device performance relative to a specific diagnostic task, sample sizes for test sets, ground truth establishment for AI, MRMC studies, or standalone algorithm performance as requested in the prompt, do not apply in the context of this 1999 MRI device submission.

The acceptance criteria for this device would have been related to the performance characteristics of an MRI system (e.g., field strength, image quality parameters, safety parameters) and its substantial equivalence to a predicate device, not the diagnostic performance of an AI algorithm. The study described is simply the 510(k) submission itself, asserting substantial equivalence to predicate MRI systems.

Below is an attempt to address the prompt using the limited and anachronistic information available in the 1999 document. Many fields will be "Not Applicable" or "Not Provided" because the regulatory and technological landscape for AI/ML devices did not exist in this form at that time.

Acceptance Criteria and Study for K983902 (Field Effects MRI Image Processing Software)

Based on the provided 510(k) summary, the "acceptance criteria" and "study" described are in the context of demonstrating substantial equivalence to predicate MRI devices, focusing on the safe and effective operation of the MRI system with its enhanced image processing software. It is not an AI/ML device with specific performance metrics against a defined diagnostic task.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not applicable/Not provided. This submission is for an MRI system's fundamental image processing, not an algorithm's diagnostic performance on a dataset. The "test" for substantial equivalence would involve comparing system specifications and intended use against predicate devices, likely through engineering analysis, not a clinical study with a specific patient test set in the modern AI sense.
• Data Provenance: Not applicable/Not provided in this document. The concept of "data provenance" for a test set of images for AI evaluation is not relevant here.

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

• Number of Experts: Not applicable. Ground truth establishment for an AI algorithm's diagnostic performance is not relevant to this submission. The "ground truth" for an MRI system's performance is its ability to generate images according to physical principles and specifications, and its safety parameters.
• Qualifications of Experts: Not applicable.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. This relates to AI performance evaluation, which is not the subject of this 510(k). Substantial equivalence is adjudicated by the FDA based on the provided documentation, comparisons to predicate devices, and regulatory standards.

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

• MRMC Study: No. This type of study is used to compare human reader performance with and without AI assistance, which is not relevant to this 1999 submission for an MRI system.

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

• Standalone Performance: No. The "Image Processing Software" is an integral part of the MRI system, not a standalone algorithm. The concept of "standalone performance" for an AI algorithm did not apply.

7. The Type of Ground Truth Used

• Type of Ground Truth: Not applicable in the context of AI evaluation. The "truth" for this submission revolves around the MRI system's physical and technical specifications, safety, and its ability to produce images that are "useful in the determination of a diagnosis" when interpreted by a trained physician, consistent with predicate devices.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable/Not provided. This is an MRI system, not an AI model that undergoes "training." The image processing software would have been developed through engineering and physics principles, not machine learning on a training set of data.

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

• Ground Truth for Training Set Establishment: Not applicable. As above, the concept of a training set and its ground truth does not apply to this type of device development.

Ask a specific question about this device