(233 days)
The uMR Omega system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces sagittal, transverse, coronal, and oblique cross sectional images, and spectroscopic images, and that display internal anatomical structure and/or function of the head, body and extremities.
These images and the physical parameters derived from the images when interpreted by a trained physician yield information that may assist the diagnosis. Contrast agents may be used depending on the region of interest of the scan.
The uMR Omega is a 3.0T superconducting magnetic resonance diagnostic device with a 75cm size patient bore. It consists of components such as magnet, RF power amplifier, RF coils, gradient power amplifier, gradient coils, patient table, spectrometer, computer, equipment cabinets, power distribution system, internal communication system, and vital signal module etc. The uMR Omega Magnetic Resonance Diagnostic Device is designed to conform to NEMA and DICOM standards.
This traditional 510(k) is to request modifications for the cleared uMR Omega(K240540). The modifications performed on the uMR Omega in this submission are due to the following changes that include:
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Addition of RF coils and corresponding accessories: Breast Coil - 12, Biopsy Configuration, Head Coil - 16, Positioning Couch-top, Coil Support, Tx/Rx Head Coil.
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Modification of the mmw component name: from mmw100 to mmw101.
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Modification of the dimensions of detachable table: from width 826mm, height 880mm, length 2578mm to width 810mm, height 880mm, length 2505mm.
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Addition and modification of pulse sequences:
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a) New sequences: gre_pass, gre_mtp, epi_dti_msh, gre_fsp_c(3D LGE).
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b) Added Associated options for certain sequences: fse(MicroView), fse_mx(MicroView), gre(Output phase image), gre_swi(QSM),
gre_fsp_c(DB/GB PSIR), gre_bssfp(TI Scout), gre_bssfp_ucs(Real Time Cine), epi_dwi(IVIM), epi_dti(DSI, DKI). -
c) Added Additional accessory equipment required for certain sequences: gre_bssfp (Virtual ECG Trigger).
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d) Added applicable body parts: epi_dwi_msh, gre_fine, fse_mx.
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Addition of imaging processing methods: Inline Cardiac function, Inline ECV, Inline MRS, Inline MOCO and MTP.
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Addition of workflow features: EasyFACT, TI Scout, EasyCrop, ImageGuard, MoCap and Breast Biopsy.
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Addition of image reconstruction methods: SparkCo.
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Modification of function: uVision (add Body Part Recognization), EasyScan(add applicable body parts).
The modification does not affect the intended use or alter the fundamental scientific technology of the device.
The provided text describes modifications to an existing MR diagnostic device (uMR Omega) and performs non-clinical testing to demonstrate substantial equivalence to predicate devices. It specifically details the acceptance criteria and study results for two components: SparkCo (an AI algorithm for spark artifact correction) and Inline ECV (an image processing method for extracellular volume fraction calculation).
Here's a breakdown of the requested information:
Acceptance Criteria and Device Performance for uMR Omega
1. Table of Acceptance Criteria and Reported Device Performance
For SparkCo (Spark artifact Correction):
| Test Part | Test Methods | Acceptance Criteria | Reported Device Performance |
|---|---|---|---|
| Spark detection accuracy | Based on the real-world testing dataset, calculating the detection accuracy by comparing the spark detection results with the ground-truth. | The average detection accuracy needs to be larger than 90%. | The average detection accuracy is 94%. |
| Spark correction performance | 1. Based on the simulated spark testing dataset, calculating the PSNR (Peak signal-to-noise ratio) of the spark-corrected images and original spark images. |
- Based on the real-world spark dataset, evaluating the image quality improvement between the spark-corrected images and spark images by one experienced evaluator. | 1. The average PSNR of spark-corrected images needs to be higher than the spark images.
- Spark artifacts need to be reduced or corrected after enabling SparkCo. | 1. The average PSNR of spark-corrected images is 1.6 higher than the spark images.
- The images with spark artifacts were successfully corrected after enabling the SparkCo. |
For Inline ECV (Extracellular Volume Fraction):
| Validation Type | Acceptance Criteria | Reported Device Performance (Summary from Subgroup Analysis) |
|---|---|---|
| Passing rate | To verify the effectiveness of the algorithm, the subjective evaluation method was used. The segmentation result of each case was obtained with the algorithm, and the segmentation mask was evaluated with the following criteria. The test pass criteria was: no failure cases, satisfaction rate S/(S+A+F) exceeding 95%. |
The criteria is as follows:
• Satisfied (S): the segmentation myocardial boundary adheres to the myocardial boundary and blood pool ROI is within the blood pool excluding the papillary muscles.
• Acceptable (A): These are small missing or redundant areas in the myocardial segmentation but not obviously and the blood pool ROI is within the blood pool excluding the papillary muscles.
• Fail (F): The myocardial mask does not adhere to the myocardial boundary or the blood pool ROI is not within the blood pool, or the blood pool ROI contains papillary muscles. | The segmentation algorithm performed as expected in different subgroups.
Total satisfaction Rate (S): 100% for all monitored demographic and acquisition subgroups, which means no failure cases (F) or acceptable cases (A) were reported. |
2. Sample Size Used for the Test Set and Data Provenance
For SparkCo:
- Test Set Sample Size:
- Simulated Spark Testing Dataset: 159 spark slices.
- Real-world Spark Testing Dataset: 59 cases from 15 patients.
- Data Provenance:
- Simulated Spark Testing Dataset: Generated by simulating spark artifacts from spark-free raw data (61 cases from 10 volunteers, various body parts and MRI sequences).
- Real-world Spark Testing Dataset: Acquired using uMR 1.5T and uMR 3T scanners, covering representative clinical protocols (T1, T2, PD with/without fat saturation) from 15 patients. The ethnicity for this dataset is 100% Asian, and the data originates from an unspecified location, but given the manufacturer's location (Shanghai, China), it is highly likely to be China. This appears to be retrospective as patients data is mentioned.
For Inline ECV:
- Test Set Sample Size: 90 images from 28 patients.
- Data Provenance: The distribution table shows data from patients with magnetic field strengths of 1.5T and 3T. Ethn_icity is broken down into "Asia" (17 patients) and "USA" (11 patients). This indicates a combined dataset potentially from multiple geographical locations, and appears to be retrospective.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
For SparkCo:
- Spark detection accuracy: The ground truth for spark detection accuracy was established by comparing to "ground-truth" spark locations, which were generated as part of the simulation process for the training data and likely also for evaluating the testing set during the simulation step. For the real-world dataset, the document mentions "comparing the spark detection results with the ground-truth" implying an existing ground truth, but doesn't specify how it was established or how many experts were involved.
- Spark correction performance: "One experienced evaluator" was used for subjective evaluation of image quality improvement on the real-world spark dataset. No specific qualifications are provided for this evaluator beyond "experienced".
For Inline ECV:
- The document states, "The segmentation result of each case was obtained with the algorithm, and the segmentation mask was evaluated with the following criteria." It does not explicitly mention human experts establishing a distinct "ground truth" for each segmentation mask for the purpose of the acceptance criteria. Instead, the evaluation seems to be a subjective assessment against predefined criteria. No number of experts or qualifications are provided.
4. Adjudication Method for the Test Set
For SparkCo:
- For spark detection accuracy, the comparison was against a presumed inherent "ground-truth" (likely derived from the simulation process).
- For spark correction performance, a single "experienced evaluator" made the subjective assessment, implying no adjudication method (e.g., 2+1, 3+1) was explicitly used among multiple experts.
For Inline ECV:
- The evaluation was a "subjective evaluation method" against specific criteria. No information about multiple evaluators or an adjudication method is provided. It implies a single evaluator or an internal consensus without formal adjudication.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done
- No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not explicitly mentioned for either SparkCo or Inline ECV. The studies were focused on the standalone performance of the algorithms.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done
- Yes, for both SparkCo and Inline ECV, the studies described are standalone algorithm performance evaluations.
- SparkCo focused on the algorithm's ability to detect and correct spark artifacts (objective metrics like PSNR and subjective assessment by one evaluator).
- Inline ECV focused on the algorithm's segmentation accuracy (subjective evaluation of segmentation masks against criteria).
7. The Type of Ground Truth Used
For SparkCo:
- Spark detection accuracy: Ground truth was generated by simulating spark artifacts from spark-free raw data, implying a simulated/synthetic ground truth for training and a comparison against this for testing. For real-world data, the "ground-truth" for detection is implied but not explicitly detailed how it was established.
- Spark correction performance: For PSNR, the "ground truth" for comparison is the original spark images. For subjective evaluation, it's against the "spark images" and the expectation of correction, suggesting human expert judgment (by one evaluator) rather than a pre-established clinical ground truth for each case.
For Inline ECV:
- The ground truth for Inline ECV appears to be a subjective expert assessment (though the number of experts is not specified) of the algorithm's automatically generated segmentation masks against predefined "Satisfied," "Acceptable," and "Fail" criteria. It is not an independent, pre-established ground truth like pathology or outcomes data.
8. The Sample Size for the Training Set
For SparkCo:
- Training dataset for the AI module: 61 cases from 10 volunteers. From this, a total of 24,866 spark slices along with corresponding "ground truth" (location of spark points) were generated for training.
For Inline ECV:
- The document states, "The training data used for the training of the cardiac ventricular segmentation algorithm is independent of the data used to test the algorithm." However, the sample size for the training set itself is not explicitly provided in the given text.
9. How the Ground Truth for the Training Set Was Established
For SparkCo:
- The ground truth for the SparkCo training set was established by simulating spark artifacts from spark-free raw data. This simulation process directly provided the "location of spark points" as the ground truth.
For Inline ECV:
- The document mentions that the training data is independent of the test data, but it does not describe how the ground truth for the training set of the Inline ECV algorithm was established.
§ 892.1000 Magnetic resonance diagnostic device.
(a)
Identification. A magnetic resonance diagnostic device is intended for general diagnostic use to present images which reflect the spatial distribution and/or magnetic resonance spectra which reflect frequency and distribution of nuclei exhibiting nuclear magnetic resonance. Other physical parameters derived from the images and/or spectra may also be produced. The device includes hydrogen-1 (proton) imaging, sodium-23 imaging, hydrogen-1 spectroscopy, phosphorus-31 spectroscopy, and chemical shift imaging (preserving simultaneous frequency and spatial information).(b)
Classification. Class II (special controls). A magnetic resonance imaging disposable kit intended for use with a magnetic resonance diagnostic device only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.