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The Indomitable Magnetic Resonance Imaging System is indicated for use in producing images of multiple planes in the head and body. These images correspond to the distribution of hydrogen nuclei exhibiting nuclear magnetic resonance (NMR) and depend for their contrast upon NMR parameters [hydrogen nuclei concentration and flow velocity, T1 (spinlattice relaxation time) and T2 (spin-spin relaxation time)]. As a result of the acquisition and processing of the NMR data, these images display the internal structure of the head and body, and when interpreted by a trained physician, can vield diagnostically useful information.

FONAR Corporation has modified the existing control limits for the Indomitable scanner gradient subsystem to allow the maximum gradient current to increase by 50 percent, with a corresponding increase in the maximum slew rate for the gradient waveforms. All other subsystems remain essentially unchanged from their previously approved (K002490) state.

This modified gradient subsystem follows the same basic design principles as our previously approved gradient systems. It functions on the same basic operating and physical principles as the predicate device, the Indomitable gradient system (K002490). It is constructed with the same manufacturing materials, and is fabricated in the same fashion to the previously approved gradients.

The primary differences of this modification to the gradient subsystem are the software limits for maximum gradient current value and the increased slew rate that accompanies the changed current value. It is, in summary, a significant equivalent of FONAR's currently approved predicate device.

The provided text describes a special 510(k) submission for a device modification, specifically an increase in the operational limits for the gradient subsystem of an MRI scanner. The goal of the study was to demonstrate that this modification does not adversely affect patient safety and that the device remains substantially equivalent to its predicate device. This is primarily a safety validation rather than a diagnostic performance study in the typical sense of AI-powered diagnostic devices.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device modification are primarily focused on maintaining patient safety and demonstrating substantial equivalence to the predicate device, particularly regarding the absence of Peripheral Nerve Stimulation (PNS) at the new operational limits. The "reported device performance" here refers to the outcomes of the safety testing.

Study Details

This submission focuses on a safety validation for a hardware/software modification, not a diagnostic AI algorithm. Therefore, many standard AI/diagnostic study metrics (like specific test set sizes for diagnostic accuracy, expert qualifications for ground truth in diagnostic tasks, MRMC studies, or training set details) are not directly applicable or reported in the same way.

1. Sample size used for the test set and the data provenance:

   • Sample Size: This is a clinical trial involving "volunteers," implying a relatively small number of healthy individuals rather than a large patient cohort for diagnostic validation. The exact number of volunteers is not specified in the provided text, only that "All volunteers completed the test."
   • Data Provenance: Prospective. The study was a "voluntary clinical trial" specifically conducted for this submission. The origin of the volunteers (e.g., country) is not specified but is implicitly within the context of FDA regulations in the US.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Ground Truth Establishment: For this safety study, the "ground truth" is the absence of PNS. This was established directly by the test subjects themselves reporting any "sensations associated with PNS." There isn't a need for multiple external experts to establish a "ground truth" in the diagnostic sense, as the criterion is subjective patient experience combined with adherence to international safety standards.
   • Expert Qualifications: The study was implicitly supervised by medical professionals (as required for a clinical trial), but no specific number or qualifications of experts involved in establishing ground truth (i.e., PNS assessment beyond patient self-reporting) are detailed.

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

   • Adjudication Method: Not applicable in the context of this safety study. The primary "adjudication" for PNS was the direct report from the "volunteers" themselves according to the IEC 60601-2-33 standard. There is no mention of independent reviewers or a consensus process for assessing PNS beyond this.

4. 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:

   • MRMC Study: No. This is not an AI-assisted diagnostic device; it's a modification to an MRI scanner's gradient system. Therefore, an MRMC study and effects on human reader performance are not applicable.

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

   • Standalone Performance: Not applicable. This device is an MRI scanner modification, not a standalone algorithm. The "algorithm" here refers to the system's operating software, which sets hardware limits. The performance evaluated is the physical system's safety, not an algorithm's diagnostic output.

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

   • Ground Truth Type: Patient self-reporting of physiological sensation (absence of PNS) per the guidelines of international safety standards (IEC 60601-2-33). This is complemented by non-clinical technical measurements to confirm adherence to new physical operating parameters.

7. The sample size for the training set:

   • Training Set Sample Size: Not applicable. This is not a machine learning or AI algorithm in the context of a "training set" for diagnostic performance.

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

   • Training Set Ground Truth: Not applicable, as there is no training set mentioned for this type of device modification.

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The FONAR 360° Magnetic Resonance Imaging System is indicated for use in producing images of multiple planes in the head and body. These images correspond to the distribution of hydrogen nuclei exhibiting nuclear magnetic resonance (NMR) and depend for their contrast upon NMR parameters [hydrogen nuclei concentration and flow velocity, T1 (spinlattice relaxation time) and T2 (spin-spin relaxation time)]. As a result of the acquisition and processing of the NMR data, these images display the internal structure of the head and body, and when interpreted by a trained physician, can yield diagnostically useful information.

The FONAR 360° magnet follows the same basic design, operating and physical principles, and construction methods and materials of the predicate magnets. The field strength of this magnet is 6000 gauss (0.6T). This magnet configuration is essentially a combination of basic structural elements of Fonar's previously approved magnets. The two pole assemblies of the vertical-field iron-core electromagnet are separated and supported by steel supports that form the "walls", "ceiling" and "floor" of the room-sized magnet. This provides an unimpeded 360° access to the magnet's imaging gap. The electromagnet coil elements are windings of multiple turns of epoxy-insulated copper installed around the poles of the magnet frame, connected to a regulated power source, and chilled via the closed loop chiller system.

During operation, the poles of the magnet establish a vertical magnet field with a limited fringe field. The magnetic field is created by passing a regulated DC current through the coil windings surrounding the magnet poles. The field strength is proportional to the amount of current in the coils. In this magnet the current supplied will result in a magnetic field of 6000 gauss, ± 5% (0.6T ± 5%), operating at frequencies between 24.27 and 26.92 MHz. This magnet functions with all imaging sequences available in the current software releases. All other non-magnet related equipment and procedures used are as previously reviewed by the FDA in PMA 830076, its supplements, and the 510(k) submissions K910839 and K951681.

The transmitter coil for the Fonar 360° is modified from the coil used in the Quad 12000 magnet to accommodate the openness of the gap and the removal of the vertical posts of the Quad magnet construction. The resulting transmitter coil is configured as a quadrature coil instead of the linear coil used in the Quad. While the construction methods are different, the resulting field after application of the RF power produces the same level of excitation as the previously approved coils, and results in images that are equivalent to those produced by the predicate devices.

The provided text describes the FONAR 360° Magnetic Resonance Imaging Scanner and its substantial equivalence to previously approved FONAR MRI magnets. The document focuses on non-clinical and clinical testing to demonstrate safety and effectiveness primarily through comparison to NEMA standards and predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the FONAR 360° MRI scanner are primarily based on demonstrating substantial equivalence to predicate devices (Fonar B3000 and Quad 12000) and adherence to NEMA standards. The reported device performance is presented in comparison to these benchmarks.

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

• Sample Size for Test Set: The document mentions "a period of human imaging" for clinical testing, however, it does not specify the number of human participants or images included in this test set.
• Data Provenance: The document states "A period of human imaging was performed." It does not specify the country of origin, but given the submission to the FDA (U.S. Federal law), it's highly probable the data was collected in the United States. The study appears to be prospective for the clinical imaging mentioned, as it was specifically performed for the purpose of demonstrating safety and effectiveness of the new device.

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

The document states that images "when interpreted by a trained physician, can yield diagnostically useful information." For the clinical testing, it focuses on image quality and safety. However, it does not specify the number of experts used to interpret the images in the clinical test set, nor does it detail their specific qualifications (e.g., "radiologist with 10 years of experience").

4. Adjudication Method for the Test Set

The document does not specify any particular adjudication method (e.g., 2+1, 3+1) for the clinical interpretation of images. The focus is on the overall quality and diagnostic utility, rather than a specific consensus process for individual findings.

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

The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study. This submission is for a new MRI scanner and focuses on demonstrating its safety and equivalence to existing MRI technology, not on the performance of an AI algorithm or its impact on human reader performance. Therefore, there is no information on the effect size of human readers improving with AI vs. without AI assistance.

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

This submission is for an MRI scanner, a hardware device, not an AI algorithm. Therefore, a "standalone algorithm only" performance study is not applicable and was not done. The primary focus is on the physical and performance characteristics of the magnetic resonance imaging system itself.

7. The Type of Ground Truth Used

For the non-clinical tests, the ground truth was based on NEMA standards and direct physical measurements of the device's performance parameters (e.g., field strength, acoustic noise, SAR, dB/dt, SNR, geometric distortion, image uniformity, slice thickness, spatial resolution).

For the clinical testing, the "ground truth" for demonstrating effectiveness seems to be based on the qualitative assessment of "high-quality images with good signal intensity and image uniformity" by trained physicians, implying a clinical judgment rather than a specific external pathology or outcomes data for each case. The comparison is also made implicitly to images produced by the predicate devices.

8. The Sample Size for the Training Set

The document does not mention a training set in the context of an AI algorithm, as this submission is for a physical medical device (MRI scanner) and its hardware/physical performance characteristics.

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

As no training set is discussed or implied for an AI algorithm, this question is not applicable.

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