(258 days)
The uMR 680 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 680 is a 1.5T superconducting magnetic resonance diagnostic device with a 70cm 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 680 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 680(K240744). The modifications performed on the uMR 680 in this submission are due to the following changes that include:
(1) Addition of RF coils and corresponding accessories: Breast Coil -12, Biopsy Configuration, Head Coil-16, Positioning Couch-top, Coil Support.
(2) Deletion of VSM (Wireless UIH Gating Unit REF 453564324621, ECG module Ref 989803163121, SpO2 module Ref 989803163111).
(3) Modification of the dimensions of Detachable table: from width 826mm, height 880mm,2578mm to width 810mm, height 880mm, length 2505mm.
(4) Addition and modification of pulse sequences
a) New sequences: gre_snap, gre_quick_4dncemra, gre_pass, gre_mtp, gre_trass, epi_dwi_msh, epi_dti_msh, svs_hise.
b) Added associated options for certain sequences: fse(add Silicone-Only Imaging, MicroView, MTC, MultiBand), fse_arms(add Silicone-Only Imaging), fse_ssh(add Silicone-Only Imaging), fse_mx(add CEST, T1rho, MicroView, MTC), fse_arms_dwi(add MultiBand), asl_3d(add multi-PLD), gre(add T1rho, MTC, output phase image), gre_fsp(add FSP+), gre_bssfp(add CASS, TI Scout), gre_fsp_c(add 3D LGE, DB/GB PSIR), gre_bssfp_ucs(add real time cine), gre_fq(add 4D Flow), epi_dwi(add IVIM), epi_dti(add DKI, DSI).
c) Added additional accessory equipment required for certain sequences: gre_bssfp(add Virtual ECG Trigger).
d) Name change of certain sequences: gre_fine(old name: gre_bssfp_fi).
e) Added applicable body parts: gre_ute, gre_fine, fse_mx.
(5) Addition of imaging reconstruction methods: AI-assisted Compressed Sensing (ACS), Spark artifact Correction (SparkCo).
(6) Addition of imaging processing methods: Inline Cardiac Function, Inline ECV, Inline MRS, Inline MOCO, 4D Flow, SNAP, CEST, T1rho, FSP+, CASS, PASS, MTP.
(7) Addition of workflow features: TI Scout, EasyCrop, ImageGuard, Mocap, EasyFACT, Auto Bolus tracker, Breast Biopsy and uVision.
(8) Modification of workflow features: EasyScan(add applicable body parts)
The modification does not affect the intended use or alter the fundamental scientific technology of the device.
The provided FDA 510(k) clearance letter and summary for the uMR 680 Magnetic Resonance Imaging System outlines performance data for several new features and algorithms.
Here's an analysis of the acceptance criteria and the studies that prove the device meets them for the AI-assisted Compressed Sensing (ACS), SparkCo, Inline ED/ES Phases Recognition, and Inline MOCO algorithms.
1. Table of Acceptance Criteria and Reported Device Performance
| Feature/Algorithm | Evaluation Item | Acceptance Criteria | Reported Performance |
|---|---|---|---|
| AI-assisted Compressed Sensing (ACS) | AI Module Verification Test | The ratio of error: NRMSE(output)/ NRMSE(input) is always less than 1. | Pass |
| Image SNR | ACS has higher SNR than CS. | Pass (ACS shown to perform better than CS in SNR) | |
| Image Resolution | ACS has higher (standard deviation (SD) / mean value(S)) values than CS. | Pass (ACS shown to perform better than CS in resolution) | |
| Image Contrast | Bland-Altman analysis of image intensities acquired using fully sampled and ACS was shown with less than 1% bias and all sample points falls in the 95% confidence interval. | Pass (less than 1% bias, all sample points within 95% confidence interval) | |
| Image Uniformity | ACS achieved significantly same image uniformities as fully sampled image. | Pass | |
| Structure Measurement | Measurements differences on ACS and fully sampled images of same structures under 5% is acceptable. | Pass | |
| Clinical Evaluation | All ACS images were rated with equivalent or higher scores in terms of diagnosis quality. | "All ACS images were rated with equivalent or higher scores in terms of diagnosis quality" (implicitly, it passed) | |
| SparkCo | Spark Detection Accuracy | The average detection accuracy needs to be larger than 90%. | The average detection accuracy is 94%. |
| Spark Correction Performance (Simulated) | The average PSNR of spark-corrected images needs to be higher than the spark images. Spark artifacts need to be reduced or corrected. | The average PSNR of spark-corrected images is 1.6 dB higher than the spark images. The images with spark artifacts were successfully corrected after enabling SparkCo. | |
| Spark Correction Performance (Real-world) | Spark artifacts need to be reduced or corrected (evaluated by one experienced evaluator assessing image quality improvement). | The images with spark artifacts were successfully corrected after enabling SparkCo. | |
| Inline ED/ES Phases Recognition | Error between algorithm and gold standard | The average error does not exceed 1 frame. | The error between the frame indexes calculated by the algorithm for the ED and ES of all test data and the gold standard frame index is 0.13 frames, which does not exceed 1 frame. |
| Inline MOCO | Dice Coefficient (Left Ventricular Myocardium after Motion Correction) Cardiac Perfusion Images | The average Dice coefficient of the left ventricular myocardium after motion correction is greater than 0.87. | The average Dice coefficient of the left ventricular myocardium after motion correction is 0.92, which is greater than 0.87. Subgroup analysis also showed good generalization: - Age: 0.92-0.93 - Gender: 0.92 - Ethnicity: 0.91-0.92 - BMI: 0.91-0.95 - Magnetic field strength: 0.92-0.93 - Disease conditions: 0.91-0.93 |
| Dice Coefficient (Left Ventricular Myocardium after Motion Correction) Cardiac Dark Blood Images | The average Dice coefficient of the left ventricular myocardium after motion correction is greater than 0.87. | The average Dice coefficient of the left ventricular myocardium after motion correction is 0.96, which is greater than 0.87. Subgroup analysis also showed good generalization: - Age: 0.95-0.96 - Gender: 0.96 - Ethnicity: 0.95-0.96 - BMI: 0.96-0.98 - Magnetic field strength: 0.96 - Disease conditions: 0.96-0.97 |
2. Sample Size Used for the Test Set and Data Provenance
- AI-assisted Compressed Sensing (ACS):
- Sample Size: 1724 samples from 35 volunteers.
- Data Provenance: Diverse demographic distributions (gender, age groups, ethnicity, BMI) covering various clinical sites and separated time periods. Implied to be prospective or a carefully curated retrospective set, collected specifically for validation on the uMR 680 system, and independent of training data.
- SparkCo:
- Simulated Spark Testing Dataset: 159 spark slices (generated from spark-free raw data).
- Real-world Spark Testing Dataset: 59 cases from 15 patients.
- Data Provenance: Real-world data acquired from uMR 1.5T and uMR 3T scanners, covering representative clinical protocols. The report specifies "Asian" for 100% of the real-world dataset's ethnicity, noting that performance is "irrelevant with human ethnicity" due to the nature of spark signal detection. This is retrospective data.
- Inline ED/ES Phases Recognition:
- Sample Size: 95 cases from 56 volunteers.
- Data Provenance: Includes various ages, genders, field strengths (1.5T, 3.0T), disease conditions (NOR, MINF, DCM, HCM, ARV), and ethnicities (Asian, White, Black). The data is independent of the training data. Implied to be retrospective from UIH MRI systems.
- Inline MOCO:
- Sample Size: 287 cases in total (105 cardiac perfusion images from 60 patients, 182 cardiac dark blood images from 33 patients).
- Data Provenance: Acquired from 1.5T and 3T magnetic resonance imaging equipment from UIH. Covers various ages, genders, ethnicities (Asian, White, Black, Hispanic), BMI, field strengths (1.5T, 3.0T), and disease conditions (Positive, Negative, Unknown). The data is independent of the training data. Implied to be retrospective from UIH MRI systems.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts
- AI-assisted Compressed Sensing (ACS):
- Number of Experts: More than one (plural "radiologists" used).
- Qualifications: American Board of Radiologists certificated physicians.
- SparkCo:
- Number of Experts: One expert for real-world SparkCo evaluation.
- Qualifications: "one experienced evaluator." (Specific qualifications like board certification or years of experience are not provided for this specific evaluator).
- Inline ED/ES Phases Recognition:
- Number of Experts: Not explicitly stated for ground truth establishment ("gold standard phase indices"). It implies a single, established method or perhaps a consensus by a team, but details are missing.
- Inline MOCO:
- Number of Experts: Three licensed physicians.
- Qualifications: U.S. credentials.
4. Adjudication Method for the Test Set
- AI-assisted Compressed Sensing (ACS): Not explicitly stated, but implies individual review by "radiologists" to rate diagnostic quality.
- SparkCo: For the real-world dataset, evaluation by "one experienced evaluator."
- Inline ED/ES Phases Recognition: Not explicitly stated; "gold standard phase indices" are referenced, implying a pre-defined or established method without detailing a multi-reader adjudication process.
- Inline MOCO: "Finally, all ground truth was evaluated by three licensed physicians with U.S. credentials." This suggests an adjudication or confirmation process, but the specific method (e.g., 2+1, consensus) is not detailed beyond "evaluated by."
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
- No MRMC comparative effectiveness study was explicitly described to evaluate human reader improvement with AI assistance. The described studies focus on the standalone performance of the algorithms or a qualitative assessment of images by radiologists for diagnostic quality.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
- Yes, standalone performance was done for all listed algorithms.
- ACS: Evaluated quantitatively (SNR, Resolution, Contrast, Uniformity, Structure Measurement) and then qualitatively by radiologists. The quantitative metrics are standalone.
- SparkCo: Quantitative metrics (Detection Accuracy, PSNR) and qualitative assessment by an experienced evaluator. The quantitative metrics are standalone.
- Inline ED/ES Phases Recognition: Evaluated quantitatively as the error between algorithmic output and gold standard. This is a standalone performance metric.
- Inline MOCO: Evaluated using the Dice coefficient, which is a standalone quantitative metric comparing algorithm output to ground truth.
7. The Type of Ground Truth Used
- AI-assisted Compressed Sensing (ACS):
- Quantitative: Fully-sampled k-space data transformed to image space.
- Clinical: Radiologist evaluation ("American Board of Radiologists certificated physicians").
- SparkCo:
- Spark Detection Module: Location of spark points (ground truth for simulated data).
- Spark Correction Module: Visual assessment by "one experienced evaluator."
- Inline ED/ES Phases Recognition: "Gold standard phase indices" (method for establishing this gold standard is not detailed, but implies expert-derived or a highly accurate reference).
- Inline MOCO: Left ventricular myocardium segmentation annotated by a "well-trained annotator" and "evaluated by three licensed physicians with U.S. credentials." This is an expert consensus/pathology-like ground truth.
8. The Sample Size for the Training Set
- AI-assisted Compressed Sensing (ACS): 1,262,912 samples (from a variety of anatomies, image contrasts, and acceleration factors).
- SparkCo: 24,866 spark slices (generated from 61 spark-free cases from 10 volunteers).
- Inline ED/ES Phases Recognition: Not explicitly provided, but stated to be "independent of the data used to test the algorithm."
- Inline MOCO: Not explicitly provided, but stated to be "independent of the data used to test the algorithm."
9. How the Ground Truth for the Training Set Was Established
- AI-assisted Compressed Sensing (ACS): Fully-sampled k-space data were collected and transformed to image space as the ground-truth. All data were manually quality controlled.
- SparkCo: "The training dataset for the AI module in SparkCo was generated by simulating spark artifacts from spark-free raw data... a total of 24,866 spark slices, along with the corresponding ground truth (i.e., the location of spark points), were generated for training." This indicates a hybrid approach using real spark-free data to simulate and generate the ground truth for spark locations.
- Inline ED/ES Phases Recognition: Not explicitly provided.
- Inline MOCO: Not explicitly provided.
FDA 510(k) Clearance Letter - uMR 680
Page 1
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
Doc ID # 04017.08.00
July 16, 2025
Shanghai United Imaging Healthcare Co., Ltd.
Gao Xin
RA Manager
No.2258 Chengbei Rd. Jiading District
Shanghai, 201807
China
Re: K243397
Trade/Device Name: uMR 680
Regulation Number: 21 CFR 892.1000
Regulation Name: Magnetic resonance diagnostic device
Regulatory Class: Class II
Product Code: LNH, MOS, QIH
Dated: June 20, 2025
Received: June 20, 2025
Dear Gao Xin:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
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K243397 - Gao Xin Page 2
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-
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K243397 - Gao Xin Page 3
assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Ningzhi Li -S [Digitally signed by Ningzhi Li -S]
for
Daniel M. Krainak, Ph.D.
Assistant Director
DHT8C: Division of Radiological
Imaging and Radiation Therapy Devices
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
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FORM FDA 3881 (8/23) Page 1 of 1
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
Indications for Use
Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.
510(k) Number (if known): K243397
Device Name: uMR 680
Indications for Use (Describe)
The uMR 680 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.
Type of Use (Select one or both, as applicable)
☒ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)
CONTINUE ON A SEPARATE PAGE IF NEEDED.
This section applies only to requirements of the Paperwork Reduction Act of 1995.
DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.
The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:
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"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number."
Page 5
Shanghai United Imaging Healthcare Co., Ltd.
Tel: +86 (21) 67076888 Fax：+86 (21) 67076889
www.united-imaging.com
Page 1 of 23
510(k) SUMMARY
1. Sponsor Identification
Shanghai United Imaging Healthcare Co., Ltd.
No.2258 Chengbei Rd. Jiading District, 201807, Shanghai, China
Contact Person: Xin GAO
Position: Regulatory Affairs Specialist
Tel: +86-21-67076888-5386
Fax: +86-21-67076889
Email: xin.gao@united-imaging.com
2. Identification of Proposed Device
Trade Name: uMR 680
Common Name: Magnetic Resonance Imaging System
Model: uMR 680
Regulatory Information
Regulation Number: 21 CFR 892.1000
Regulation Name: Magnetic Resonance Diagnostic Device
Regulatory Class: II
Product Code: LNH, MOS
Secondary Product Code: QIH
Review Panel: Radiology
3. Identification of Primary/Reference Device(s)
Predicate Device
510(k) Number: K240744
Device Name: uMR 680
Regulation Name: Magnetic Resonance Diagnostic Device
Regulatory Class: II
Product Code: LNH, MOS
Review Panel: Radiology
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Shanghai United Imaging Healthcare Co., Ltd.
Tel: +86 (21) 67076888 Fax：+86 (21) 67076889
www.united-imaging.com
Page 2 of 23
Reference Device#1
510(k) Number: K220332
Device Name: uWS-MR
Regulation Name: Medical Image Management and Processing System
Regulatory Class: II
Product Code: LLZ, QIH
Review Panel: Radiology
Reference Device#2
510(k) Number: K234154
Device Name: uPMR 790
Regulation Name: Emission computed tomography system
Regulatory Class: II
Product Code: OUO, MOS
Review Panel: Radiology
Reference Device#3
510(k) Number: K230152
Device Name: uMR Omega
Regulation Name: Magnetic Resonance Diagnostic Device
Regulatory Class: II
Product Code: LNH, MOS
Review Panel: Radiology
4. Device Description
The uMR 680 is a 1.5T superconducting magnetic resonance diagnostic device with a 70cm 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 680 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 680(K240744). The modifications performed on the uMR 680 in this submission are due to the following changes that include:
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Shanghai United Imaging Healthcare Co., Ltd.
Tel: +86 (21) 67076888 Fax：+86 (21) 67076889
www.united-imaging.com
Page 3 of 23
(1) Addition of RF coils and corresponding accessories: Breast Coil -12, Biopsy Configuration, Head Coil-16, Positioning Couch-top, Coil Support.
(2) Deletion of VSM (Wireless UIH Gating Unit REF 453564324621, ECG module Ref 989803163121, SpO2 module Ref 989803163111).
(3) Modification of the dimensions of Detachable table: from width 826mm, height 880mm,2578mm to width 810mm, height 880mm, length 2505mm.
(4) Addition and modification of pulse sequences
a) New sequences: gre_snap, gre_quick_4dncemra, gre_pass, gre_mtp, gre_trass, epi_dwi_msh, epi_dti_msh, svs_hise.
b) Added associated options for certain sequences: fse(add Silicone-Only Imaging, MicroView, MTC, MultiBand), fse_arms(add Silicone-Only Imaging), fse_ssh(add Silicone-Only Imaging), fse_mx(add CEST, T1rho, MicroView, MTC), fse_arms_dwi(add MultiBand), asl_3d(add multi-PLD), gre(add T1rho, MTC, output phase image), gre_fsp(add FSP+), gre_bssfp(add CASS, TI Scout), gre_fsp_c(add 3D LGE, DB/GB PSIR), gre_bssfp_ucs(add real time cine), gre_fq(add 4D Flow), epi_dwi(add IVIM), epi_dti(add DKI, DSI).
c) Added additional accessory equipment required for certain sequences: gre_bssfp(add Virtual ECG Trigger).
d) Name change of certain sequences: gre_fine(old name: gre_bssfp_fi).
e) Added applicable body parts: gre_ute, gre_fine, fse_mx.
(5) Addition of imaging reconstruction methods: AI-assisted Compressed Sensing (ACS), Spark artifact Correction (SparkCo).
(6) Addition of imaging processing methods: Inline Cardiac Function, Inline ECV, Inline MRS, Inline MOCO, 4D Flow, SNAP, CEST, T1rho, FSP+, CASS, PASS, MTP.
(7) Addition of workflow features: TI Scout, EasyCrop, ImageGuard, Mocap, EasyFACT, Auto Bolus tracker, Breast Biopsy and uVision.
(8) Modification of workflow features: EasyScan(add applicable body parts)
The modification does not affect the intended use or alter the fundamental scientific technology of the device.
5. Indications for Use
The uMR 680 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.
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Shanghai United Imaging Healthcare Co., Ltd.
Tel: +86 (21) 67076888 Fax：+86 (21) 67076889
www.united-imaging.com
Page 4 of 23
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.
6. Comparison of Technological Characteristics with the Predicate Device
uMR 680 employs the same basic operating principles and fundamental technologies, and has the same indications for use as the predicate device. A comparison between the technological characteristics of proposed and predicate/reference devices is provided as below.
Table 1 Comparison to Predicate device
| ITEM | Proposed Device uMR 680 | Predicate Device uMR 680(K240744) | Remark |
|---|---|---|---|
| General | |||
| Magnet system | |||
| Field Strength | 1.5 Tesla | 1.5 Tesla | Same |
| Type of Magnet | Superconducting | Superconducting | Same |
| Patient-accessible bore dimensions | 70cm | 70cm | Same |
| Type of Shielding | Actively shielded, OIS technology | Actively shielded, OIS technology | Same |
| Magnet Homogeneity | 1.40ppm @ 50cm DSV0.90ppm @ 45cm DSV0.45ppm @ 40cm DSV0.190ppm @ 30cm DSV0.120ppm @ 20cm DSV0.040ppm @ 10cm DSV | 1.40ppm @ 50cm DSV0.90ppm @ 45cm DSV0.45ppm @ 40cm DSV0.190ppm @ 30cm DSV0.120ppm @ 20cm DSV0.040ppm @ 10cm DSV | Same |
| Gradient system | |||
| Max gradient amplitude | 45mT/m | 45mT/m | Same |
| Max slew rate | 200T/m/s | 200T/m/s | Same |
| Shielding | active | active | Same |
| Cooling | water | water | Same |
| RF system | |||
| Resonant frequencies | 63.87 MHz | 63.87 MHz | Same |
| Number of transmit channels | 1 | 1 | Same |
| Number of receive channels | Up to 96 | Up to 96 | Same |
| Amplifier peak power per channel | 18 kW | 18 kW | Same |
| RF Coils | |||
| Head & Neck Coil -24 | Yes | Yes | Same |
| Spine Coil - 32 | Yes | Yes | Same |
| Body Array Coil - 12 | Yes | Yes | Same |
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Shanghai United Imaging Healthcare Co., Ltd.
Tel: +86 (21) 67076888 Fax：+86 (21) 67076889
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Page 5 of 23
| ITEM | Proposed Device uMR 680 | Predicate Device uMR 680(K240744) | Remark |
|---|---|---|---|
| Body Array Coil - 24 | Yes | Yes | Same |
| Breast Coil - 10 | Yes | Yes | Same |
| Knee Coil - 12 | Yes | Yes | Same |
| Lower Extremity Coil - 24 | Yes | Yes | Same |
| Shoulder Coil - 12 | Yes | Yes | Same |
| Small Loop Coil | Yes | Yes | Same |
| Wrist Coil - 12 | Yes | Yes | Same |
| Cardiac Coil - 24 | Yes | Yes | Same |
| Foot & Ankle Coil - 24 | Yes | Yes | Same |
| Temporomandibular Joint Coil - 4 | Yes | Yes | Same |
| Carotid Coil - 8 | Yes | Yes | Same |
| Flex Coil Large - 8 | Yes | Yes | Same |
| Flex Coil Small - 8 | Yes | Yes | Same |
| Infant Coil-24 | Yes | Yes | Same |
| SuperFlex Large - 12 | Yes | Yes | Same |
| SuperFlex Small - 12 | Yes | Yes | Same |
| SuperFlex Body - 24 | Yes | Yes | Same |
| Breast Coil-24 | Yes | Yes | Same |
| Breast Coil 12 | Yes | No | Note 1 |
| Head Coil 16 | Yes | No | Note 2 |
| Patient table | |||
| Dimensions | Patient Table:width 640 mm, height 880 mm, length 2620 mm | Patient Table:width 640 mm, height 880 mm, length 2620 mm | Same |
| Detachable Table:width 826 mm, height 880 mm, length 2578 mm | Detachable Table:width 810 mm, height 880 mm, length 2505 mm | Note 3 | |
| Maximum supported patient weight | Patient Table:250 kg | Patient Table:250 kg | Same |
| Detachable Table:310 kg | Detachable Table:310 kg | Same | |
| Accessories | |||
| Vital Signal Gating | / | Wireless UIH Gating Unit REF 453564324621ECG module Ref 989803163121SpO2 module Ref 989803163111(alternative) | Note 4 |
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| ITEM | Proposed Device uMR 680 | Predicate Device uMR 680(K240744) | Remark |
|---|---|---|---|
| uVWMERPuMVRX(alternative) | uVWMERPuMVRX(alternative) | Same | |
| mmw100 (optional) | mmw100 (optional) | Same | |
| Image Processing | |||
| Inline ECV | Yes | No | Note 5 |
| Inline MOCO | Yes | No | Note 6 |
| MTP | Yes | No | Note 7 |
| Workflow | |||
| TI Scout | Yes | No | Note 8 |
| Breast Biopsy | Yes | No | Note 9 |
| Auto Bolus tracker | Yes | No | Note 10 |
| EasyScan | Yes | Yes | Note 11 |
| Image Reconstruction | |||
| SparkCo | Yes | No | Note 12 |
Table 2 Comparison to Reference device#1
| ITEM | Proposed Device uMR 680 | Reference Device#1 uWS-MR (K220332) | Remark |
|---|---|---|---|
| Image Processing | |||
| Inline Cardiac function | Yes | Yes | Note 13 |
| Inline MRS | Yes | Yes | Note 14 |
Table 3 Comparison to Reference device#2
| ITEM | Proposed Device uMR 680 | Reference Device#2 uPMR 790 (K234154) | Remark |
|---|---|---|---|
| Workflow | |||
| EasyCrop | Yes | Yes | Note 15 |
| ImageGuard | Yes | Yes | Same |
| Mocap (also named MoCap-Monitoring) | Yes | Yes | Same |
| EasyFACT (also named Inline FACT) | Yes | Yes | Same |
| Image Processing | |||
| 4D Flow | Yes | Yes | Same |
| SNAP | Yes | Yes | Same |
| CEST | Yes | Yes | Same |
| T1rho | Yes | Yes | Same |
| FSP+ | Yes | Yes | Same |
| CASS | Yes | Yes | Same |
| PASS | Yes | Yes | Same |
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Table 4 Comparison to Reference device#3
| ITEM | Proposed Device uMR 680 | Reference Device#3 uMR Omega (K230152) | Remark |
|---|---|---|---|
| ACS | Yes | Yes | Same |
| uVision | Yes | Yes | Note 16 |
Note 1 The intended use of Breast Coil - 12 is essentially identical to previously cleared Breast Coil - 10. There are two differences between Breast Coil – 12 and Breast Coil – 10. One is that Breast Coil - 12 can be used with Biopsy Configuration to provide breast biopsy function. The other one is the number of channels of the receiver coil. The difference did not raise new safety and effectiveness concerns.
Note 2 The intended use of Head Coil - 16 is equivalent to Head Coil – 12 previously cleared via K200024. The only difference between them is the number of channels of the receiver coil. The difference did not raise new safety and effectiveness concerns.
Note 3 The dimensions of detachable table were changed from width 826mm, height 880mm, length 2578mm to width 810mm, height 880mm, length 2505mm. The difference did not raise new safety and effectiveness concerns.
Note 4 One alterative wireless VSM was removed from the proposed device. The difference did not raise new safety and effectiveness concerns.
Note 5 Inline ECV aims to calculate the pixel-wise ECV (extracellular volume fraction) images from the native and post T1 mapping. The difference did not raise new safety and effectiveness concerns.
Note 6 Inline MOCO is a function that perform motion correction on MR images, which can reduce the motion caused by physiological factors such as breathing and heart beat in the images. The difference did not raise new safety and effectiveness concerns.
Note 7 MTP is substantially equivalent to GRE and acquires two flip angles and multi-echo images with one scan, and then uses specific image processing to attain multiparametric images. The difference did not raise new safety and effectiveness concerns.
Note 8 TI Scout automatically selects the TI frame which has the darkest ventricular myocardium of the TI Scout image, allowing users to achieve the best inversion time (TI) and simplifying the workflow which needed the TI. The difference did not raise new safety and effectiveness concerns.
Note 9 Breast Biopsy is a workflow that can provide biopsy instruction for technicians based on the hardware and lesion parameters, the instruction include the biopsy grid coordinate, needle block cell and needle depth. The difference did not raise new safety and effectiveness concerns.
Note 10 Auto Bolus tracker is a feature that automatically recognizes the time when the contrast agent reaches the target position and triggers the scan. The difference did not raise new safety and effectiveness concerns.
Note 11 EasyScan of the proposed device supports more body parts than that of the predicate device. In this submission, breast and pelvis and hip and ankle and thorax are included. The difference did not raise new safety and effectiveness concerns.
Note 12 SparkCo is an algorithm that can detect and correct spark artifacts (a specific occurring MRI artifact that is caused by electromagnetic interference (EMI)) in MRI images. The difference did not raise new safety and effectiveness concerns.
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Note 13 During this submission, inline ED/ES Phases Recognition was included in Inline Cardiac function, which allows automatically rearrange the cardiac images into an aligned cardiac cycle. The difference did not raise new safety and effectiveness concerns.
Note 14 Inline MRS of the proposed device are the same as the predicate device. The difference is that the algorithms of Inline MRS are shifted from the post-processing workstation to inline console. The difference did not raise new safety and effectiveness concerns.
Note 15 EasyCrop of the proposed device supports more body parts than that of the predicate device. In this submission, head and carotid and renal are included. The difference did not raise new safety and effectiveness concerns.
Note 16 uVision of the reference device just supports Hand Gesture Recognition. During this submission, Body Part Recognition function was included in uVision, which allows assist patient positioning by performing image recognition on human natural images through a 3D camera during the positioning stage. The difference did not raise new safety and effectiveness concerns.
7. Performance Data
The following performance data were provided in support of the substantial equivalence determination.
Non-Clinical Testing
Non-clinical testing including surface heating and image performance tests were conducted for the uMR 680 to verify that the proposed device met all design specifications as it is Substantially Equivalent (SE) to the predicate device.
UNITED IMAGING HEALTHCARE claims conformance to the following standards and guidance:
Electrical Safety and Electromagnetic Compatibility (EMC)
- ANSI/AAMIES60601-1: 2005/ (R) 2012+A1:2012+C1:2009/(R)2012+A2:2010/(R)2012) [IncludingAmendment2(2021)] Medical electrical equipment - Part 1: General requirements for basic safety and essential performance
- IEC 60601-1-2:2014+A1:2020, Medical electrical equipment - Part 1-2: General requirements for basic safety and essential performance - Collateral standard: Electromagnetic disturbances - Requirements and tests
- IEC 60601-2-33 Ed. 4.0:2022 Medical Electrical Equipment - Part 2-33: Particular Requirements for The Basic Safety and Essential Performance of Magnetic Resonance Equipment for Medical Diagnostic
- IEC 60825-1: 2014, Edition 3.0, Safety of laser products - Part 1: Equipment classification and requirements.
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- IEC 60601-1-6:2010+A1:2013+A2:2020, Edition 3.2, Medical electrical equipment - Part 1-6: General requirements for basic safety and essential performance - Collateral standard: Usability.
- IEC 62304:2006+AMD1:2015 CSV Consolidated version, Medical device software - Software life cycle processes
- IEC 62464-1 Edition 2.0: 2018-12, Magnetic resonance equipment for medical imaging Part 1: Determination of essential image quality parameters.
- NEMA MS 1-2008(R2020), Determination of Signal-to-Noise Ratio (SNR) in Diagnostic Magnetic Resonance Images
- NEMA MS 2-2008(R2020), Determination of Two-Dimensional Geometric Distortion in Diagnostic Magnetic Resonance Images
- NEMA MS 3-2008(R2020), Determination of Image Uniformity in Diagnostic Magnetic Resonance Images
- NEMA MS 4-2023, Acoustic Noise Measurement Procedure for Diagnosing Magnetic Resonance Imaging Devices
- NEMA MS 5-2018, Determination of Slice Thickness in Diagnostic Magnetic Resonance Imaging
- NEMA MS 6-2008(R2014, R2020), Determination of Signal-to-Noise Ratio and Image Uniformity for Single-Channel Non-Volume Coils in Diagnostic MR Imaging
- NEMA MS 8-2016, Characterization of the Specific Absorption Rate (SAR) for Magnetic Resonance Imaging Systems
- NEMA MS 9-2008(R2020), Standards Publication Characterization of Phased Array Coils for Diagnostic Magnetic Resonance Images
- NEMA MS 14-2019, Characterization of Radiofrequency (RF) Coil Heating in Magnetic Resonance Imaging Systems
- IEC /TR 60601-4-2: 2024, Medical electrical equipment - Part 4-2: Guidance and interpretation - Electromagnetic immunity: performance of medical electrical equipment and medical electrical systems
Software
- NEMA PS 3.1-3.20(2022d): Digital Imaging and Communications in Medicine (DICOM)
- Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices
- Content of Premarket Submissions for Management of Cybersecurity in Medical Devices
Biocompatibility
- ISO 10993-5: 2009, Edition 3.0, Biological evaluation of medical devices - Part 5: Tests for in vitro cytotoxicity.
- ISO 10993-10: 2021, Edition 4.0, Biological evaluation of medical devices - Part 10: Tests for skin sensitization.
- ISO 10993-23: 2021, Edition 1.0, Biological evaluation of medical devices - Part
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10: Tests for irritation.
- Use of International Standard ISO 10993-1, "Biological evaluation of medical devices - Part 1: Evaluation and testing within a risk management process"
Other Standards and Guidance
- ISO 14971: 2019, Edition 3.0, Medical Devices – Application of risk management to medical devices
- Code of Federal Regulations, Title 21, Part 820 - Quality System Regulation
- Code of Federal Regulations, Title 21, Subchapter J - Radiological Health
Performance Verification
Non-clinical testing was conducted to verify the features described in this premarket submission.
- Performance evaluation report for AI-assisted Compressed Sensing(ACS), TI Scout, EasyScan, EasyCrop, ImageGuard, Mocap, Breast Biopsy, EasyFACT, Auto Bolus tracker, Inline Cardiac, Inline ECV, Inline MOCO, Silicon-Only Imaging, Brain MRS using SVS-HISE, 4D Flow, CEST, T1rho, MTP, SparkCo and uVision.
- Sample clinical images for all clinical sequences, coils and imaging processing were reviewed by U.S. board-certified radiologist comparing the proposed device and predicate device. It was shown that the proposed device can generate diagnostic quality images in accordance with the MR guidance on premarket notification submissions.
Summary of the Machine Learning Algorithm
AI-assisted Compressed Sensing(ACS)
ACS（AI-assisted Compressed Sensing）is an acceleration reconstruction technique. By adding one more regularization term from AI module, ACS is a slight extension of CS (Compressed Sensing).
The training dataset of AI module in ACS was collected from a variety of anatomies, image contrasts, and acceleration factors. Each subject was scanned by UIH MRI systems for multiple body parts and clinical protocols, resulted in a total of 1,262,912 samples. Fully-sampled k-space data were collected and transformed to image space as the ground-truth. The input data were generated by sub-sampling the fully-sampled k-space data with different parallel imaging acceleration factors and partial Fourier factors. All data were manually quality controlled before included for training.
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ACS has already received FDA cleared for the uMR Omega system, with the approval number K243122. In addition, we have conducted additional validation on the uMR 680 system with 1724 samples from 35 volunteers, with diverse demographic distributions covering various genders, age groups, ethnicity, and BMI groups (Table 5).
Table 5 Distribution of volunteer dataset
| Subjects' Characteristics (N=35) | N(%) |
|---|---|
| Gender, N(%) | |
| Male | 23(65.7%) |
| Female | 12(34.3%) |
| Age, N(%): Min=18, Max=68, Avg.=34.2, Std.=10.83 | |
| 18-29 | 4(11.4%) |
| 30-44 | 12(34.3%) |
| 45-64 | 10(28.6%) |
| >=65 | 9(25.7%) |
| Ethnicity, N(%) | |
| White | 9(25.7%) |
| Asian | 21(60%) |
| Black | 5(14.3%) |
| Body Mass Index (BMI), N(%): Min=16.0, Max=53.5, Avg.=23.3, Std.=9.57 | |
| Underweight (<18.5) | 6(17.1%) |
| Healthy weight (18.5-24.9) | 14(40%) |
| Overweight (25.0-29.9) | 10(28.6%) |
| Obesity (>=30.0) | 5(14.3%) |
The independence of these testing datasets were ensured by collecting testing data from various clinical sites and during separated time periods and on subjects different from the training data. Thus, the testing data have no overlap with the training data and are completely independent. No clinical subgroups and confounders have been defined for the datasets. The acceptance criteria for performance testing and the corresponding testing results can be found in Table 6.
Table 6 The performance evaluation report criteria of ACS
| Evaluation Item | Evaluation Method | Criteria | Results |
|---|---|---|---|
| AI Module Verification Test | The performance of AI was evaluated by comparing the input error and output error with respect of NRMSE. If the NRMSE of the AI output is less than the NRMSE of input, the | The ratio of error: NRMSE(output)/ NRMSE(input) is always less than 1. | Pass |
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| | performance of AI is considered to be validated. | | |
| Image SNR | Calculating SNR for both ACS and CS images under the same acceleration factors and protocol parameters | ACS has higher SNR than CS. | Pass |
| Image Resolution | Calculating the resolution value in elliptic ROI using the (standard deviation (SD) / mean value(S)) for both ACS and CS images under the same acceleration factors and protocol parameters | ACS has higher (standard deviation (SD) / mean value(S)) values than CS. | Pass |
| Image Contrast | Comparing the ROI signal intensities between images acquired with fully sampled and ACS images under the same acceleration factors with different TI values | Bland-Altman analysis of image intensities acquired using fully sampled and ACS was shown with less than 1% bias and all sample points falls in the 95% confidence interval. | Pass |
| Image Uniformity | Images of the phantom were acquired with fully sampled and ACS protocol respectively. Method used here for data analysis is according to NEMA MS 6-2008(R2004) | ACS achieved significantly same image uniformities as fully sampled image | Pass |
| Structure Measurement | Dimensions of selected small structures were identified and measured on ACS images as well as on fully sampled images. | Measurements differences on ACS and fully sampled images of same structures under 5% is acceptable. | Pass |
The ACS was shown to perform better than CS by measuring SNR and resolution using images from various ethnicities, age groups, BMIs, and pathological variations. Meanwhile, results from the tests also demonstrated that ACS maintained image qualities, such as contrast and uniformity, as compared against fully sampled data as golden standards, thus ACS introduces significantly few risks of image quality degradation and artifacts.
In addition, ACS images were evaluated by American Board of Radiologists certificated physicians, covering a range of protocols and body parts. Clinical
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protocols various contrast such as T1, T2, T1Flair, T2Flair, PD, STIR, etc. The evaluation reports from radiologists verified that ACS meets the requirements of clinical diagnosis. All ACS images were rated with equivalent or higher scores in terms of diagnosis quality.
SparkCo
SparkCo (Spark artifact Correction) is an algorithm that can detect and correct spark artifacts in MRI images, which will help to restore spark-free image for clinical review when encountering spark artifacts on MRI images.
The spark detection module of SparkCo is based on the AI algorithm, however, it won't change the image directly, and it only provides the K-space location of spark points. Then, the spark correction module based on traditional parallel imaging reconstruction algorithm will utilize the spark detection results to remove spark points and restore the full-sampled K-space. Through this two-step process, SparkCo can correct spark artifacts and restore the spark-free image.
The training dataset for the AI module in SparkCo was generated by simulating spark artifacts from spark-free raw data. The spark-free raw data comprises 61 cases collected from 10 volunteers across various body parts and MRI sequences. From this data, a total of 24,866 spark slices, along with the corresponding ground truth (i.e., the location of spark points), were generated for training. Additionally, 159 spark slices were generated as the simulated spark testing dataset.
The real-world spark raw data consists of 59 cases collected from 15 patients, serving as the independent testing dataset, which does not overlap with the training dataset. The demographic distribution of this testing dataset is presented in Table 4. And this testing dataset were acquired by using uMR 1.5T and uMR 3T scanners, which cover representative protocols in clinical practice such as T1, T2, and PD with and without fat saturation. Details of acquisition from various body parts are outlined in Table 7.
Table 7 The demographic distribution of real-world spark testing dataset
| Subjects' Characteristics (N=15) | N(%) |
|---|---|
| Gender, N(%) | |
| Male | 9(60%) |
| Female | 6(40%) |
| Age, N(%): Min=18, Max=59, Avg.=30.8, Std.=9.88 | |
| 18-29 | 2(20%) |
| 30-44 | 8(53.3%) |
| 45-64 | 4(26.7%) |
| >=65 | 0(0.0%) |
| Ethnicity, N(%) | |
| White | N.A. |
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| Asian | 15(100%) |
| Body Mass Index (BMI), N(%): Min=17.0, Max=53.5, Avg.=24.0, Std.=7.05 | |
| Underweight (<18.5) | 2(13.3%) |
| Healthy weight (18.5-24.9) | 10(66.7%) |
| Overweight (25.0-29.9) | 3(20%) |
| Obesity (>=30.0) | 0(0.0%) |
Remark: The performance of SparkCo is irrelevant with human ethnicity. The spark detection module of SparkCo is designed to classify and locate the spark signals with abnormally high amplitude in the K-space data. These spark signals exhibit similar characteristics across different human ethnicity, so no testing was conducted on other human ethnicity.
Table 8 The various body parts and number of cases included in the real-world spark testing dataset
| Body Parts | Number of cases |
|---|---|
| Head | 21 |
| C-spine | 5 |
| Shoulder | 1 |
| Wrist | 1 |
| Thorax | 2 |
| Abdomen | 8 |
| L-spine | 2 |
| Pelvis | 19 |
| Total | 59 |
By the test, the SparkCo have been demonstrated with high spark detection accuracy and spark correction effectiveness, as the following Table 9 shows.
Table 9 The test methods and test results of SparkCo
| Test parts | Test Methods | Accept criteria | Test Results |
|---|---|---|---|
| Test on the 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 need be larger than 90% | The average detection accuracy is 94%. |
| Test on the spark correction performance | Based on the simulated spark testing dataset, calculating the PSNR (Peak signal-to-noise ratio) of the spark-corrected images and original spark images | The average PSNR of spark-corrected images need to be higher than the spark images.Spark artifacts need to be reduced or corrected | The average PSNR of spark-corrected images is 1.6 higher than the spark images.The images with spark artifacts were |
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| | Based on the real-world spark dataset, evaluating the image quality improvement between the spark-corrected images and spark images by one experienced evaluator. | after enable the SparkCo. | successfully corrected after enable the SparkCo. |
Inline ED/ES Phases Recognition
The performance testing for inline ED/ES phases recognition algorithm was performed on 95 cases.
The validation type and acceptance criteria is shown in Table 10 below:
Table 10 Validation type and acceptance criteria
| Validation Type | Acceptance Criteria |
|---|---|
| The error between the phase indices calculated by the algorithm for the ED and ES of test data and the gold standard phase indices. | The average error does not exceed 1 frame. |
The distribution and protocols for volunteer dataset used for validation is listed in Table 11. A total of 95 cases from 56 volunteers were used.
Table 11 Distribution of volunteer dataset
| Gender | Number of people | Number of cases |
|---|---|---|
| Male | 36 | 72 |
| Female | 10 | 13 |
| Unknown | 10 | 10 |
| Age | ||
| [20,30) | 15 | 20 |
| [30,40) | 9 | 23 |
| [40,50) | 14 | 22 |
| [50,60) | 5 | 17 |
| >=60 | 3 | 3 |
| Unknown | 10 | 10 |
| Field strength | ||
| 1.5T | 10 | 19 |
| 3.0T | 36 | 66 |
| Unknown | 10 | 10 |
| Disease conditions | ||
| NOR | 46 | 85 |
| MINF | 2 | 2 |
| DCM | 2 | 2 |
| HCM | 5 | 5 |
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| ARV | 1 | 1 |
| Ethnicity | | |
| Asian | 22 | 60 |
| White | 16 | 16 |
| Black | 8 | 9 |
| Unknown | 10 | 10 |
The error between the frame indexes calculated by the algorithm for the ED and ES of all test data and the gold standard frame index is 0.13 frames, which does not exceed 1 frame. Meanwhile, the subgroup analysis shows that the proposed algorithm has good generalization in different subgroups.
Table 12 Subgroup analysis of results
| Gender | Case Number | Average of frame index differences |
|---|---|---|
| Male | 72 | 0.12 |
| Female | 13 | 0.14 |
| Unknown | 10 | 0.14 |
| Age | ||
| [20,30) | 20 | 0.13 |
| [30,40) | 23 | 0.10 |
| [40,50) | 22 | 0.16 |
| [50,60) | 17 | 0.10 |
| >=60 | 3 | 0.16 |
| Unknown | 10 | 0.14 |
| Field strength | ||
| 1.5T | 19 | 0.13 |
| 3.0T | 66 | 0.12 |
| Unknown | 10 | 0.14 |
| Disease conditions | ||
| NOR | 85 | 0.12 |
| MINF | 2 | 0.15 |
| DCM | 2 | 0.13 |
| HCM | 5 | 0.15 |
| ARV | 1 | 0.14 |
| Ethnicity | ||
| Asian | 60 | 0.11 |
| White | 16 | 0.19 |
| Black | 9 | 0.13 |
| Unknown | 10 | 0.14 |
The training data used for the training of the inline ED/ES phases recognition algorithm is independent of the data used to test the algorithm.
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Inline ECV
A total of 90 images from 28 patients were used as the test data. For each patient, it had the native t1map data and post t1map data.
The validation type and acceptance criteria is shown in the Table 12 below:
Table 23 Validation type and acceptance criteria
| Validation Type | Acceptance Criteria |
|---|---|
| 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. |
A total of 90 images from 28 patients were used as the test data. For each patient, it had the native t1map data and post t1map data. The distribution is as the following table 14.
Table 14 Distribution of Patient dataset
| Gender | Number |
|---|---|
| Male | 20 |
| Female | 8 |
| Age | |
| <18 | 1 |
| 18-28 | 4 |
| 29-40 | 1 |
| > 41 | 22 |
| Protocol | |
| post_t1map_sax | 28 |
| native_t1map_sax | 28 |
| BMI (kg/m(2)) | |
| <18.5 | 0 |
| [18.5, 25) | 7 |
| >=25 | 15 |
| Unknown | 6 |
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| Magnetic field strength (T) | |
| 1.5 | 13 |
| 3 | 15 |
| Ethnicity | |
| Asian | 17 |
| White | 11 |
| Healthy | |
| Negative | 19 |
| Postive | 4 |
| Unknown | 5 |
According to the subgroup analysis in Table 15, it can be seen that the segmentation algorithm performs as expected in different subgroups.
Table 15 Segmentation algorithm subgroup analysis
| Gender | Satisfied (S) | Acceptable (A) | Total Failure Rate | Total satisfaction Rate |
|---|---|---|---|---|
| Male | 100% | 0% | 0% | 100% |
| Female | 100% | 0% | 0% | 100% |
| Age | ||||
| 18-28 | 100% | 0% | 0% | 100% |
| 29-40 | 100% | 0% | 0% | 100% |
| > 41 | 100% | 0% | 0% | 100% |
| Protocol | ||||
| post_t1map_sax | 100% | 0% | 0% | 100% |
| native_t1map_sax | 100% | 0% | 0% | 100% |
| BMI (kg/m(2)) | ||||
| <18.5 | 100% | 0% | 0% | 100% |
| [18.5, 25) | 100% | 0% | 0% | 100% |
| >=25 | 100% | 0% | 0% | 100% |
| Unknown | 100% | 0% | 0% | 100% |
| Magnetic field strength (T) | ||||
| 1.5 | 100% | 0% | 0% | 100% |
| 3 | 100% | 0% | 0% | 100% |
| Ethnicity | ||||
| Asian | 100% | 0% | 0% | 100% |
| White | 100% | 0% | 0% | 100% |
| Healthy | ||||
| Negative | 100% | 0% | 0% | 100% |
| Postive | 100% | 0% | 0% | 100% |
| Unknown | 100% | 0% | 0% | 100% |
The training data used for the training of the cardiac ventricular segmentation algorithm is independent of the data used to test the algorithm.
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Inline MOCO
The performance testing for inline MOCO algorithm was performed on 287 cases during the product development, where 105 cases were cardiac perfusion images (data shown in Table 3) and 182 cases were cardiac dark blood images.
The validation type and acceptance criteria is shown in the Table 16 below:
Table 16 Validation type and acceptance criteria
| Validation Type | Acceptance Criteria |
|---|---|
| Dice | The average Dice coefficient of the left ventricular myocardium after motion correction is greater than 0.87. |
Testing Data Information
1) Cardiac Perfusion Images Subgroup Information
Sample Size:
Table 17 Sample size information of testing data
| Dataset | Patients Number | Cases Number |
|---|---|---|
| Testing Data | 60 | 105 |
(Note: One patient may have multiple cases, because of the differences in slice location.)
Equipment and Protocols:
The data were acquired from 1.5T and 3T magnetic resonance imaging equipment from UIH. The data were GRE sequence data, including T1 contrast, with the scanning range covering the cardiac.
Clinical Subgroups:
The subgroup information of cardiac perfusion images in testing data is summarized below.
Table 18 Cardiac perfusion images subgroup information
| Subgroup | Details of each subgroup | Number of cases |
|---|---|---|
| Age | ||
| <22 | 3 | |
| [22, 40) | 17 | |
| [40, 60) | 38 | |
| [60, 90) | 47 | |
| Gender | Female | 26 |
| Male | 79 | |
| Ethnicity | Asian | 74 |
| White | 31 | |
| BMI (kg/m(2)) | < 18.5 | 1 |
| [18.5, 25) | 23 |
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| | >=25 | 37 |
| | Unknown | 44 |
| Magnetic field strength (T) | 1.5 | 40 |
| | 3.0 | 65 |
| Disease conditions | Positive | 49 |
| | Negative | 16 |
| | Unknown | 40 |
2) Cardiac Dark Blood Images Subgroup Information
Sample Size:
Table 19 Sample size information of testing data
| Dataset | Patients Number | Cases Number |
|---|---|---|
| Testing Data | 33 | 182 |
(Note: One patient may have multiple cases, because of the differences in slice location.)
Equipment and Protocols:
The data were acquired from 1.5T and 3T magnetic resonance imaging equipment from UIH. The data were FSE sequence data, including T2 contrast, with the scanning range covering the cardiac.
Clinical Subgroups:
The subgroup information of cardiac dark blood images in testing data is summarized below.
Table 20 Cardiac dark blood images subgroup information
| Subgroup | Details of each subgroup | Samples Number |
|---|---|---|
| Age | ||
| <22 | 34 | |
| [22, 40) | 110 | |
| [40, 60) | 34 | |
| [60, 90) | 4 | |
| Gender | Female | 58 |
| Male | 124 | |
| Ethnicity | ||
| Asian | 89 | |
| White | 64 | |
| Black | 26 | |
| Hispanic | 3 | |
| BMI (kg/m(2)) | ||
| < 18.5 | 21 | |
| [18.5, 25) | 79 | |
| >=25 | 76 | |
| Unknown | 6 | |
| Magnetic field strength (T) | 1.5 | 50 |
| 3.0 | 132 | |
| Disease conditions | Positive | 3 |
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| | Negative | 35 |
| | Unknown | 144 |
Performance Testing Summary
1) Cardiac Perfusion Images Performance Testing Summary
The average Dice coefficient of the left ventricular myocardium after motion correction is 0.92, which is greater than 0.87. Meanwhile, the subgroup analysis shows that (Table 6) the proposed device algorithm has good generalization in different subgroups.
Table 21 Cardiac perfusion images subgroup performance test
| Age | Average Dice after motion correction |
|---|---|
| <22 | 0.92 |
| [22, 40) | 0.93 |
| [40, 60) | 0.92 |
| [60, 90) | 0.92 |
| Gender | Average Dice after motion correction |
| Female | 0.92 |
| Male | 0.92 |
| Ethnicity | Average Dice after motion correction |
| Asian | 0.92 |
| White | 0.91 |
| BMI (kg/m(2)) | Average Dice after motion correction |
| < 18.5 | 0.95 |
| [18.5, 25) | 0.93 |
| >=25 | 0.91 |
| Unknown | 0.93 |
| Magnetic field strength (T) | Average Dice after motion correction |
| 1.5 | 0.92 |
| 3.0 | 0.93 |
| Disease conditions | Average Dice after motion correction |
| Positive | 0.93 |
| Negative | 0.92 |
| Unknown | 0.91 |
2) Cardiac Dark Blood Images Performance Testing Summary
The average Dice coefficient of the left ventricular myocardium after motion correction is 0.96, which is greater than 0.87. Meanwhile, the subgroup analysis shows that (Table 7) the proposed device algorithm has good generalization in different subgroups.
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Table 22 Cardiac dark blood images subgroup performance test
| Age | Average Dice after motion correction |
|---|---|
| <22 | 0.96 |
| [22, 40) | 0.96 |
| [40, 60) | 0.96 |
| [60, 90) | 0.95 |
| Gender | Average Dice after motion correction |
| Female | 0.96 |
| Male | 0.96 |
| Ethnicity | Average Dice after motion correction |
| Asian | 0.96 |
| White | 0.95 |
| Black | 0.95 |
| Hispanic | 0.96 |
| BMI (kg/m(2)) | Average Dice after motion correction |
| < 18.5 | 0.96 |
| [18.5, 25) | 0.96 |
| >=25 | 0.96 |
| Unknown | 0.98 |
| Magnetic field strength (T) | Average Dice after motion correction |
| 1.5 | 0.96 |
| 3.0 | 0.96 |
| Disease conditions | Average Dice after motion correction |
| Positive | 0.97 |
| Negative | 0.96 |
| Unknown | 0.96 |
Standard Annotation Process
For ground truth annotations, all ground truth was annotated by a well-trained annotator. The annotator used an interactive tool to observe the image, and then labeled the left ventricular myocardium in the image. Finally, all ground truth was evaluated by three licensed physicians with U.S. credentials.
Testing & Training Data Independence
The training data used for the training of the inline MOCO algorithm is independent of the data used to test the algorithm.
Summary
The features described in this premarket submission are supported with the results of the testing mentioned above, the uMR 680 was found to have a safety and effectiveness profile that is similar to the predicate device.
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8. Conclusion
Based on the comparison and analysis above, the proposed device has similar indications for use, performance, safety equivalence, and effectiveness as the predicate device. The differences above between the proposed device and predicate device do not affect the intended use, technology characteristics, safety, and effectiveness. And no issues are raised regarding to safety and effectiveness. The proposed device is determined to be Substantially Equivalent (SE) to the predicate device.
§ 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.