Who is the manufacturer of uMR Astra?

Shanghai United Imaging Healthcare Co., Ltd. filed the 510(k) submission for uMR Astra (K260113).

What is the FDA product code for uMR Astra?

LNH. It is classified under 21 CFR 892.1000 as a Class 2 device in the Radiology panel.

What is the regulatory pathway for uMR Astra?

510(k) clearance (submission type: Traditional).

Does uMR Astra use AI or machine learning?

Yes. uMR Astra (K260113) is flagged as an AI/ML-enabled medical device in the FDA's AI/ML device list.

Who can help prepare an FDA 510(k) submission like uMR Astra?

Innolitics — a medical-device software consultancy — provides regulatory and engineering support for FDA 510(k) submissions. See https://innolitics.com/services/510ks/ or contact https://innolitics.com/contact.

Can an AI/ML device like uMR Astra get a guaranteed FDA 510(k) clearance?

Yes. Innolitics offers a guaranteed FDA 510(k) clearance engagement for AI/ML Software as a Medical Device — combining engineering and regulatory execution under one agreement with a guaranteed clearance outcome. Eligibility: the device must be an AI/ML Software as a Medical Device (SaMD), and its performance must meet the criteria Innolitics determines during an initial regulatory strategy engagement to be necessary for FDA clearance. See https://innolitics.com/services/end-to-end-samd/ or contact https://innolitics.com/contact.

uMR Astra

K260113 · Shanghai United Imaging Healthcare Co., Ltd. · LNH · May 22, 2026 · Radiology

Device Facts

Record ID	K260113
Device Name	uMR Astra
Applicant	Shanghai United Imaging Healthcare Co., Ltd.
Product Code	LNH · Radiology
Decision Date	May 22, 2026
Decision	SESE
Submission Type	Traditional
Regulation	21 CFR 892.1000
Device Class	Class 2
Attributes	AI/ML, Pediatric

AI Performance

Output	Algorithm	Acceptance	Observed	Dev DS	Dev Readers	Test DS	Test Readers
AI-assisted Compressed Sensing (ACS) image reconstruction	AI-assisted Compressed Sensing (CS) with regularization term	NRMSE(ACS)/NRMSE(CS) < 1; SSIM(ACS)/SSIM(CS) < 1; PSNR difference < 5%	Pass	1,262,912 samples from UIH MRI systems	—	749 samples from 25 volunteers	>1 (US board-certified radiologists)
DeepRecon image de-noising and super-resolution	Deep-learning based image processing algorithm	Uniformity difference < 5%; Intensity difference < 5%; Structure measurement difference < 5%	Pass	165,837 samples from 264 volunteers	—	25 volunteers	>1 (US board-certified radiologists)
EasyScan slice positioning	Deep learning algorithm	Success rate P1/(P1+P2+F) > 80%	99.6%	—	—	444 cases from 116 subjects	1 (US credentialed MRI technologist)
t-ACS dynamic MR imaging	Deep learning priors with low-rank characteristics	—	Pass	108 volunteers	—	741 cases from 34 volunteers	—
AiCo motion correction	K-space based AI motion correction	—	Pass	140,000 images from 114 volunteers	—	112 samples from 24 volunteers	—
SparkCo spark artifact correction	AI-based spark detection and parallel imaging reconstruction	Detection accuracy > 90%; PSNR of corrected images > spark images	Detection accuracy 94%; PSNR 1.6 higher	24,866 spark slices from 10 volunteers	—	59 cases from 15 patients	—
ImageGuard motion artifact monitoring	—	Success rate P/(P+F) > 90%	100%	—	—	191 cases from 80 subjects	1 (US credentialed MRI technologist)
EasyCrop automatic MRA cropping	—	Pass rate P1/(P1+P2+F) > 90%	100%	—	—	65 subjects	1 (US credentialed MRI technologist)
EasyBolus enhanced scanning automation	Neural networks	Success rate P1+P2/(P1+P2+F) > 100%	100%	—	—	20 subjects	>1 (US certified professionals)
EasyFACT liver ROI placement	—	Satisfied and Acceptable ratio (S+A)/(S+A+F) > 95%	100%	—	—	25 volunteers	—
TI Scout inversion time detection	—	Average frame difference <= 1; Maximum frame difference <= 2	Average frame difference 0.37; Maximum frame difference 2	—	—	27 patients	—
Inline MOCO motion correction	—	Average Dice coefficient > 0.87	Cardiac Perfusion: 0.92; Cardiac Dark Blood: 0.96	—	—	Cardiac Perfusion: 105 cases from 60 patients; Cardiac Dark Blood: 182 cases from 33 patients	>1 (US licensed physicians)
Inline Cardiac Function ED/ES phase recognition	—	Average error <= 1 frame	0.13 frames	—	—	95 cases from 56 volunteers	—
Inline Cardiac Function segmentation	—	Dice > 0.95	Pass	—	—	66 cases	—
Inline ECV segmentation	—	Satisfaction rate S/(S+A+F) > 95%	100%	—	—	90 images from 28 patients	—
EasyRegister patient anthropometry estimation	Regression on human natural images	—	Height: 92.4% within 5%; Weight: 68.64% within 10%	—	—	118 cases from 63 patients	—

Indications for Use

The uMR Astra 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.

Device Story

uMR Astra is a 3T superconducting MRI system with a 70cm bore and 2-channel RF transmit system. It acquires MR signals via various RF coils and processes them using a spectrometer and computer system. The device features multiple AI-based and traditional software modules for image reconstruction (ACS, DeepRecon, t-ACS), workflow automation (EasyScan, EasyBolus, EasyFACT, EasyCrop, EasyRegister), motion correction (AiCo, Inline MOCO), artifact reduction (SparkCo), and quality monitoring (ImageGuard). It is operated by trained clinicians in a clinical setting. The output consists of diagnostic-quality anatomical and functional images. These outputs assist physicians in clinical decision-making and diagnosis. The device benefits patients by providing high-quality imaging with accelerated acquisition times and automated workflow features that reduce scan time and potential motion artifacts.

Clinical Evidence

No clinical trials were conducted. Evidence consists of bench testing, performance verification of software features, and clinical image review by three U.S. board-certified radiologists. Performance metrics for AI algorithms (e.g., ACS, DeepRecon, EasyScan, t-ACS, AiCo, SparkCo, ImageGuard, EasyCrop, EasyBolus, EasyFACT, TI Scout, Inline MOCO, Inline Cardiac Function, Inline ECV, EasyRegister) were validated using independent datasets (N=20 to 749 samples per feature) covering diverse demographics. Results demonstrated equivalent or superior performance compared to traditional methods or gold standards (e.g., Dice coefficients >0.92 for cardiac segmentation, >90% accuracy for spark detection).

Technological Characteristics

3T superconducting magnet, 70cm bore, 2-channel RF transmit, 144 receive channels. Materials comply with ISO 10993. Standards: NEMA MS series, IEC 60601-1, IEC 60601-2-33, IEC 62464-1. Connectivity: DICOM compliant. Software: Includes AI/ML-based reconstruction and workflow algorithms (ACS, DeepRecon, etc.). Sterilization: N/A (non-invasive).

Indications for Use

Indicated for use as an MRDD to produce cross-sectional and spectroscopic images of the head, body, and extremities for diagnostic purposes in patients of all ages, when interpreted by a trained physician. Contrast agents may be used.

Regulatory Classification

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).

Special Controls

*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.

Predicate Devices

uMR Ultra (K243547)

Reference Devices

uMR Omega (K243122)
uWS-MR (K220332)

Submission Summary (Full Text)

{0} FDA U.S. FOOD & DRUG ADMINISTRATION May 22, 2026 Shanghai United Imaging Healthcare Co., Ltd. Gao Xin RA Manager #2258 Chengbei Rd. Jiading District Shanghai, 201807 China Re: K260113 Trade/Device Name: uMR Astra Regulation Number: 21 CFR 892.1000 Regulation Name: Magnetic Resonance Diagnostic Device Regulatory Class: Class II Product Code: LNH Dated: April 26, 2026 Received: April 27, 2026 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. U.S. Food & Drug Administration 10903 New Hampshire Avenue Silver Spring, MD 20993 www.fda.gov {1} K260113 - 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 Management System Regulation (QMSR) (21 CFR Part 820), which includes, but is not limited to, ISO 13485 clause 7.3 (Design controls), ISO 13484 clause 8.3 (Nonconforming product), and ISO 13485 clause 8.5 (Corrective and preventative action). Please note that regardless of whether a change requires premarket review, the QMSR requires device manufacturers to review and approve changes to device design and production (ISO 13485 clause 7.3 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 Management System Regulation (QMSR) (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- {2} K260113 - 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, ![img-0.jpeg](img-0.jpeg) Daniel M. Krainak, PhD 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 {3} | Indications for Use | | | | --- | --- | --- | | Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions. | K260113 | ? | | Please provide the device trade name(s). | | ? | | uMR Astra | | | | Please provide your Indications for Use below. | | ? | | The uMR Astra 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. | | | | Please select the types of uses (select one or both, as applicable). | ☑ Prescription Use (21 CFR 801 Subpart D) ☐ Over-The-Counter Use (21 CFR 801 Subpart C) | ? | {4} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com K260113 UNITED IMAGING 510 (k) SUMMARY 1. Date of Preparation August 22, 2025 2. Sponsor Identification Shanghai United Imaging Healthcare Co., Ltd. No.2258 Chengbei Rd. Jiading District, 201807, Shanghai, China Contact Person: Xin GAO Position: Regulatory Affair Manager Tel: +86-021-67076888-5386 Fax: +86-021-67076889 Email: xin.gao@united-imaging.com 3. Identification of Proposed Device Trade Name: uMR Astra Common Name: Magnetic Resonance Imaging System Model: uMR Astra Regulatory Information Regulation Number: 892.1000 Regulation Name: Magnetic resonance diagnostic device Regulatory Class: II Product Code: LNH Review Panel: Radiology 4. Identification of Primary/Reference Device(s) Predicate Device 510(k) Number: K243547 Device Name: uMR Ultra Regulation Name: Magnetic resonance diagnostic device Regulatory Class: II Product Code: LNH Review Panel: Radiology Page - 1 of 45 {5} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING # Reference device#1 510(k) Number: K243122 Device Name: uMR Omega Regulation Name: Magnetic resonance diagnostic device Regulatory Class: II Product Code: LNH, MOS Review Panel: Radiology # Reference device#2 510(k) Number: K220332 Device Name: uWS-MR Regulation Name: Picture archiving and communications system Regulatory Class: II Product Code: LLZ, QIH Review Panel: Radiology # 5. Device Description uMR Astra is a 3T superconducting magnetic resonance diagnostic device with a 70cm size patient bore and 2 channel RF transmit system. 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. uMR Astra is designed to conform to NEMA and DICOM standards. A detailed comparison between the new and modified features included in the subject device and predicate device refers to Section 7. # 6. Indications for Use The uMR Astra 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. Page - 2 of 45 {6} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING # 7. Comparison of Technological Characteristics with the Predicate Device The differences from the predicate device are discussed in the comparison table in this submission as below. Table 1 Comparison of Indication for Use to Predicate device | ITEM | Proposed Device uMR Astra | Predicate Device uMR Ultra (K243547) | Remark | | --- | --- | --- | --- | | Indications for Use | The uMR Astra 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 Ultra 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. | Same | Table 2 Comparison to Predicate device | ITEM | Proposed Device uMR Astra | Predicate Device uMR Ultra (K243547) | Remark | | --- | --- | --- | --- | | General | | | | | Magnet system | | | | | Field Strength | 3.0 Tesla | 3.0 Tesla | Same | | Type of Magnet | Superconducting | Superconducting | Same | | Patient-accessible bore dimensions | 70 cm | 70 cm | Same | | Type of Shielding | Actively shielded, OIS technology | Actively shielded, OIS technology | Same | | Magnet Homogeneity | ≤2.30 ppm @ 50cm DSV ≤0.80 ppm @ 45cm DSV ≤0.38 ppm @ 40cm DSV ≤0.08 ppm @ 30cm DSV ≤0.02 ppm @ 20cm DSV | ≤2.30 ppm @ 50cm DSV ≤0.80 ppm @ 45cm DSV ≤0.38 ppm @ 40cm DSV ≤0.08 ppm @ 30cm DSV ≤0.02 ppm @ 20cm DSV ≤0.002 ppm @ 10cm DSV | Same | Page - 3 of 45 {7} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | ITEM | Proposed Device uMR Astra | Predicate Device uMR Ultra (K243547) | Remark | | --- | --- | --- | --- | | | ≤ 0.002 ppm @ 10cm DSV | | | | **Gradient system** | | | | | Max gradient amplitude | 50 mT/m | 100 mT/m | Note 1 | | Max slew rate | 200 T/m/s | 200 T/m/s | Same | | Shielding | active | active | Same | | Cooling | water | water | Same | | **RF system** | | | | | Resonant frequencies | 128.23 MHz | 128.23 MHz | Same | | Number of transmit channels | 2 | 2 | Same | | Amplifier peak power per channel | 20 kW | 20 kW | Same | | Maximum number of receive channels | 144 | 192 | Note 2 | | **RF Coils** | | | | | Volume Transmit Coil | Yes | Yes | Same | | Breast Coil - 12 | Yes | Yes | Same | | Breast Coil - 24 | Yes | Yes | Same | | SuperFlex Large - 12 | Yes | Yes | Same | | UHD SuperFlex Large - 24 | Yes | Yes | Same | | SuperFlex Small - 12 | Yes | Yes | Same | | UHD SuperFlex Small - 24 | Yes | Yes | Same | | SuperFlex Body - 24 | Yes | Yes | Same | | SuperFlex Whole Body - 48 | Yes | Yes | Same | | Head Coil - 16 | Yes | Yes | Same | | Head & Neck Coil - 24 | Yes | Yes | Same | | Head & Neck Coil - 48 | Yes | Yes | Same | | UHD SuperFlex Neck & Chest - 24 | Yes | Yes | Note 3 | | Infant Coil - 24 | Yes | Yes | Same | | Small Loop Coil | Yes | Yes | Same | | Wrist Coil - 12 | Yes | Yes | Same | | Wrist Coil - 24 | Yes | Yes | Same | Page - 4 of 45 {8} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | ITEM | Proposed Device uMR Astra | Predicate Device uMR Ultra (K243547) | Remark | | --- | --- | --- | --- | | Tx/Rx Head Coil | Yes | Yes | Same | | Shoulder Coil - 12 | Yes | Yes | Same | | Shoulder Coil - 24 | Yes | Yes | Same | | Spine Coil - 48 | Yes | Yes | Same | | Tx/Rx Knee Coil - 24 | Yes | Yes | Same | | Foot & Ankle Coil - 24 | Yes | Yes | Same | | Carotid Coil - 8 | Yes | Yes | Same | | Temporomandibular Joint Coil - 4 | Yes | Yes | Same | | UHD Head & Neck Coil - 72 | Yes | No | Note 4 | | UHD Neuro-Cardiovascular Coil - 96 | Yes | No | Note 5 | | Patient table | | | | | Maximum supported patient weight | 310 kg | 310 kg | Same | | Accessories | | | | | Vital Signal Monitor | ECG, Rrespiration and pulse module uVWMERP Wireless gating trigger unit uMVRX | ECG, Rrespiration and pulse module uVWMERP Wireless gating trigger unit uMVRX | Same | | | Respiration module mmw101 (optional) | Respiration module mmw101 (optional) | Same | | Tilt Support | Yes | Yes | Same | | Positioning Couch-top | Yes | Yes | Same | | Coil Support | Yes | Yes | Same | | Patient Bore Projector | Yes | Yes | Same | | uVision | | | | | Body Part Recognization | Yes | Yes | Same | | Hand Gesture Recognization | Yes | Yes | Same | | Safety | | | | | Electrical Safety | Comply with ES 60601-1 | Comply with ES 60601-1 | Same | | EMC | Comply with IEC 60601-1-2 | Comply with IEC 60601-1-2 | Same | Page - 5 of 45 {9} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | ITEM | Proposed Device uMR Astra | Predicate Device uMR Ultra (K243547) | Remark | | --- | --- | --- | --- | | Max SAR for Transmit Coil | Comply with IEC 60601-2-33 | Comply with IEC 60601-2-33 | Same | | Max dB/dt | Comply with IEC 60601-2-33 | Comply with IEC 60601-2-33 | Same | | Biocompatibility | Comply with ISO 10993 series | Comply with ISO 10993 series | Same | | Surface Heating | NEMA MS 14 | NEMA MS 14 | Same | | Imaging Features | | | | | 4D CEMRA 4D Contrast Enhancement Magnetic Resonance Angiography | Yes | Yes | Same | | 4D NCEMRA 4D Non-Contrast-Enhanced Magnetic Resonance Angiography | Yes | Yes | Same | | ARMS Acquisition and Reconstruction with Motion Suppression | Yes | Yes | Same | | ARMS DWI ARMS Diffusion-Weighted Imaging | Yes | Yes | Same | | 3D ASL 3D Arterial Spin Labeling | Yes | Yes | Same | | Multi-PLD ASL Multiple Post-Labeling-Delay Arterial Spin Labeling | Yes | Yes | Same | | bFAST | Yes | Yes | Same | | BOLD Blood Oxygenation Level Dependent imaging | Yes | Yes | Same | | Brain Perfusion | Yes | Yes | Same | | cDWI computed Diffusion-Weighted Imaging | Yes | Yes | Same | | CEST Chemical Exchange Saturation Transfer | Yes | Yes | Same | | DB SWI Dark Blood of Susceptibility Weighted Imaging | Yes | Yes | Same | Page - 6 of 45 {10} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | ITEM | Proposed Device uMR Astra | Predicate Device uMR Ultra (K243547) | Remark | | --- | --- | --- | --- | | DCE Dynamic Contrast Enhancement | Yes | Yes | Same | | DTI Diffusion Tensor Imaging | Yes | Yes | Same | | Multiband | Yes | Yes | Same | | FACT Fat Analysis and Calculation Technique | Yes | Yes | Same | | 4D Flow 4D Flow Quantification | Yes | Yes | Same | | 2D Flow 2D Flow Quantification | Yes | Yes | Same | | FSE DWI FSE Diffusion-Weighted Imaging | Yes | Yes | Same | | FSP+ Fast Spoiled Gradient Echo Plus | Yes | Yes | Same | | MARS+ Metal Artifact Reduction Suppression Plus | Yes | Yes | Same | | MATRIX Modulated flip Angle Technique in Refocused Imaging with eXtended echo train | Yes | Yes | Same | | MicroView | Yes | Yes | Same | | MQD MultiQuant Decipher | Yes | Yes | Same | | 3D MRCP 3D Magnetic Resonance CholangioPancreatography | Yes | Yes | Same | | MRE Magnetic Resonance Elastography | Yes | Yes | Same | | Respiratory Navigator | Yes | Yes | Same | | NCEMRA Non-Contrast Enhanced Magnetic Resonance Angiography | Yes | Yes | Same | | PASS Pair-echo Acquisition of Steady State | Yes | Yes | Same | Page - 7 of 45 {11} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | ITEM | Proposed Device uMR Astra | Predicate Device uMR Ultra (K243547) | Remark | | --- | --- | --- | --- | | SNAP Simultaneous Non-contrast Angiography and intraPlaque hemorrhage | Yes | Yes | Same | | SWI Susceptibility Weighted Imaging | Yes | Yes | Same | | SWI+ Susceptibility Weighted Imaging Plus | Yes | Yes | Same | | T1rho T1rho Quantitative Mapping Imaging | Yes | Yes | Same | | tFAST | Yes | Yes | Same | | TRASS Time Reversed Acquisition of Steady State | Yes | Yes | Same | | uCS united Compressed Sensing | Yes | Yes | Same | | uCSR united Compressed Sensing with Radial Acquisition | Yes | Yes | Same | | uFreeR UIH Free Radial imaging | Yes | Yes | Same | | uSWIFT Susceptibility Weighted Imaging with Fast Technique | Yes | Yes | Same | | UTE Ultra-short Time of Echo | Yes | Yes | Same | | WFI Water Fat Imaging | Yes | Yes | Same | | QScan | Yes | Yes | Same | | Myocardial Perfusion Imaging | Yes | Yes | Same | | Cardiac Function | Yes | Yes | Same | | Myocardial Tagging | Yes | Yes | Same | | Myocardial Mapping | Yes | Yes | Same | | MRCA Magnetic Resonance Coronary Angiography | Yes | Yes | Same | Page - 8 of 45 {12} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | ITEM | Proposed Device uMR Astra | Predicate Device uMR Ultra (K243547) | Remark | | --- | --- | --- | --- | | Silicone-Only Imaging | Yes | Yes | Same | | WAVE | Yes | No | Note 6 | | Workflow | | | | | EasyScan | Yes | Yes | Same | | MoCap Motion Capture | Yes | Yes | Same | | EasyBolus | Yes | Yes | Same | | Auto Bolus Tracker | Yes | Yes | Same | | Breast Biopsy | Yes | Yes | Same | | Inline CEST | Yes | Yes | Same | | TI Scout | Yes | Yes | Same | | EasyFACT | Yes | Yes | Same | | EasyCrop | Yes | Yes | Same | | ImageGuard | Yes | Yes | Same | | Inline Stitching | Yes | Yes | Same | | Image Reconstruction | | | | | ACS | Yes | Yes | Same | | DeepRecon | Yes | Yes | Same | | t-ACS | Yes | Yes | Same | | AiCo | Yes | Yes | Same | | SparkCo | Yes | Yes | Same | | Imaging Processing | | | | | Inline Cardiac Function | Yes | Yes | Same | | Inline MRS | Yes | Yes | Same | | Inline CPR | Yes | Yes | Same | | Inline BOLD | Yes | Yes | Same | | Inline DTI | Yes | Yes | Same | | Inline Perfusion | Yes | Yes | Same | | Inline ECV | Yes | Yes | Same | | Inline MOCO | Yes | Yes | Same | | Inline Maps | Yes | Yes | Same | | Spectroscopy | | | | | CSI MRS Head | Yes | Yes | Same | Page - 9 of 45 {13} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | ITEM | Proposed Device uMR Astra | Predicate Device uMR Ultra (K243547) | Remark | | --- | --- | --- | --- | | Chemical Shift Imaging Magnetic Resonance Spectroscopy Head | | | | | CSI MRS Prostate Chemical Shift Imaging Magnetic Resonance Spectroscopy Prostate | Yes | Yes | Same | | SVS MRS Head Single-Voxel Spectroscopy Magnetic Resonance Spectroscopy Head | Yes | Yes | Same | | SVS MRS Breast Single-Voxel Spectroscopy Magnetic Resonance Spectroscopy Breast | Yes | Yes | Same | | SVS MRS Liver Single-Voxel Spectroscopy Magnetic Resonance Spectroscopy Liver | Yes | Yes | Same | | SVS MRS Prostate Single-Voxel Spectroscopy Magnetic Resonance Spectroscopy Prostate | Yes | Yes | Same | | Other Functions | | | | | EasyRegister | Yes | Yes | Same | | Implant Safety Mode | Yes | Yes | Same | | uRemote Assistant | Yes | Yes | Same | | Note 1 | The max gradient amplitude of the proposed device is smaller than that of the predicate device. Peripheral nerve stimulation and cardiac stimulation was controlled according to IEC 60601-2-33. The difference did not raise new safety and effectiveness concerns. | | --- | --- | Page - 10 of 45 {14} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | Note 2 | The number of receive channels of the proposed device is less than that of the predicate device. The difference did not raise new safety and effectiveness concerns. | | --- | --- | | Note 3 | UHD SuperFlex Neck & Chest - 24 is identical to UHD SuperFlex Free - 24 of the predicate device. The only difference between them is the name of the coil. The difference did not raise new safety and effectiveness concerns. | | Note 4 | The intended use of UHD Head & Neck Coil - 72 is equivalent to Head &Neck Coil - 48 of the predicate device. 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 5 | The intended use of UHD Neuro-Cardiovascular Coil - 96 is equivalent to Head & Neck Coil - 48 of the predicate device. The first difference between them is that the number of channels of the receiver coil. The second difference is that the UHD Neuro-Cardiovascular Coil - 96 is a comprise of UHD Head & Neck Coil - 72 and UHD SuperFlex Neck & Chest -24, both of which can be used alone. The differences did not raise new safety and effectiveness concerns. | | Note 6 | WAVE is an acceleration technique making full use of the coil sensitivity of the three dimensions by using sinusoidal gy and gz gradients with a π/2 phase shift between the waveforms to incur a corkscrew trajectory in k-space. The new function did not raise new safety and effectiveness concerns. | Table 3 Comparison to reference device#1 | ITEM | Proposed Device uMR Astra | Reference Device#1 uMR Omega (K243122) | Remark | | --- | --- | --- | --- | | Imaging Features | | | | | MTP MulTiPlex | Yes | Yes | Same | | QSM Quantitative Susceptibility Mapping | Yes | Yes | Same | Table 4 Comparison to reference device#2 | ITEM | Proposed Device uMR Astra | Reference Device#2 uWS-MR (K220332) | Remark | | --- | --- | --- | --- | | Image Reconstruction | | | | | Inner View | Yes | Yes | Same | | Fusion | Yes | Yes | Same | Page - 11 of 45 {15} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING # 8. Performance Date The following testing was conducted on uMR Astra and were provided in support of the substantial determination. ## Non-Clinical Testing Non-clinical testing including image performance tests were conducted for uMR Astra 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) [Including Amendment2(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. - 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 62464-1 Edition 2.0: 2018-12, Magnetic resonance equipment for medical imaging Part 1: Determination of essential image quality parameters. - IEC /TS 60601-4-2: 2024, Medical electrical equipment - Part 4-2: Guidance and interpretation - Electromagnetic immunity: performance of medical electrical equipment and medical electrical systems - 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 Page - 12 of 45 {16} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING - 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 ## Software - IEC 62304:2006+AMD1:2015 CSV Consolidated version, Medical device software - Software life cycle processes - NEMA PS 3.1-3.20(2023e): Digital Imaging and Communications in Medicine (DICOM) Set - 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 10993-11:2017, Edition 3.0, Biological evaluation of medical devices - Part 11: Tests for systemic toxicity - ISO 10993-23: 2021, Edition 1.0, Biological evaluation of medical devices - Part 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. Page - 13 of 45 {17} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING > Various testing has been conducted (such as performance testing for ACS, DeepRecon, EasyScan, t-ACS, AiCo, SparkCo, ImageGuard, EasyCrop, EasyBolus, Easy FACT, TI Scout, Inline MOCO, Inline Cardiac Function, Inline ECV, uVision and EasyRegister). > Sample clinical images for all clinical sequences and coils were reviewed by three U.S. board-certified radiologists 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 ### 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. ACS has already received FDA cleared for the uMR Ultra system, with the approval number K243547. In addition, we have conducted additional validation on the uMR Astra system with 749 samples from 25 volunteers, with diverse demographic distributions covering various genders, age groups, ethnicity, and BMI groups. | Subjects' Characteristics | Total(N=25) | | --- | --- | | Gender | Number | | Male | 15 | | Female | 10 | | Age | | | 18-28 | 5 | | 29-40 | 7 | | >41 | 13 | | Ethnicity | | | White | 4 | | Asian | 16 | | Black | 5 | | Body Mass Index (BMI) | | {18} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING 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 the table below. | Evaluation Item | Evaluation Method | Criteria | Results | | --- | --- | --- | --- | | Image SNR | Calculating NRMSE for both ACS and CS images under the same acceleration factors and protocol parameters | The ratio of error: NRMSE(ACS)/NRMSE(CS) is always less than 1. | Pass | | Image Resolution | Calculating SSIM for both ACS and CS images under the same acceleration factors and protocol parameters | The ratio: SSIM(ACS)/SSIM(CS) is always less than 1. | Pass | | Image Contrast | Comparing the ROI signal intensities between images acquired with fully sampled and ACS images of same tissues. | Measurements differences on ACS and fully sampled images of same structures under 5% is acceptable. | Pass | | Local 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 | | Performance comparison between uMR Astra and uMR Ultra | PSNR measurements on fully sampled, ACS and CS images on uMR Astra and uMR Ultra | PSNR measurements difference under 5% | 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. Page - 15 of 45 {19} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING In addition, ACS images were evaluated by American Board of Radiologists certificated physicians, covering a range of protocols and body parts. Clinical 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. - DeepRecon DeepRecon is a deep-learning based image processing algorithm for intelligent image de-noising and K-space-interpolation based image super-resolution. The training data of DeepRecon were collected from 264 volunteers. Each subject was scanned by UIH MRI systems for multiple body parts and clinical protocols, resulted in a total of 165,837 samples. In terms of the ground truth and input images in training dataset, the multiple-averaged images with high-resolution and high SNR were collected as the ground-truth images. The input images were generated from the ground-truth images by sequentially reducing the SNR and resolution of the ground-truth images. All data were manually quality controlled before included for training. The DeepRecon had already received FDA cleared for the uMR Ultra system, with the approval number K243547. Validation on the uMR Astra system was conducted with 25 volunteers with diverse demographic distributions covering various genders, age groups, ethnicity, and BMI groups. | Subjects' Characteristics | Total(N=25) | | --- | --- | | Gender | Number | | Male | 16 | | Female | 9 | | Age | | | 18-28 | 10 | | 29-50 | 12 | | >50 | 3 | | Ethnicity | | | White | 10 | | Asian | 10 | | Black | 5 | | Body Mass Index (BMI) | | | < 18.5 | 10 | | 18.5-24.9 | 12 | | > 24.9 | 3 | Page - 16 of 45 {20} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING 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 had no overlap with the training data and were completely independent. No clinical subgroups and confounders had been defined for the datasets. Both NADR (without DeepRecon) and DeepRecon results were generated for each sample, and NADR and DeepRecon results were evaluated based on metrics referenced in magnetic resonance literature, such as image SNR, image uniformity, image contrast and structure measurement. The acceptance criteria for performance testing and the corresponding testing results can be found in the table below. | Evaluation Item | Acceptance Criteria | Test Result | Results | | --- | --- | --- | --- | | Image SNR | DeepRecon images achieve higher SNR compared to NADR images | NADR: 321.82 | PASS | | | | DeepRecon: 457.44 | | | Image uniformity | Uniformity difference between DeepRecon images and NADR images under 5% | 0.06% | PASS | | Image contrast | Intensity difference between DeepRecon images and NADR images under 5% | 0.5% | PASS | | Structure measurement | Measurements on NADR and DeepRecon images of same structures, measurement difference under 5% | 0% | PASS | The DeepRecon had been validated to provide image de-nosing and super-resolution processing using various ethnicities, age groups, BMIs, and pathological variations. In addition, DeepRecon images were evaluated by American Board of Radiologists certificated physicians, covering a range of protocols and body parts. Clinical protocols varioued contrast such as T1, T2, T1Flair, T2Flair, PD, STIR, etc. The evaluation reports from radiologists verified that DeepRecon meets the requirements of clinical diagnosis. All DeepRecon images were rated with equivalent or higher scores in terms of diagnosis quality. - EasyScan EasyScan is a workflow feature that automatically locates slice groups. This function is based on deep learning algorithms, which identify, locate or segment specific tissue structures in images, and calculate the position and orientation of slice groups to achieve automatic placement of slice groups. The output results need to be manually confirmed by the user, and manual movement of the lamellar group position is supported. Page - 17 of 45 {21} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING # Acceptance Criteria To verify the EasyScan of the algorithm, the subjective evaluation method was used. Pass with auto positioning (P1): The auto slice positioning meets the user's requirement. Pass with user adjustment (P2): The slice positioning requires further adjustment by the user. Fail (F): Auto position not generated or cannot be adjusted afterwards. Test pass criteria: No Fail cases and auto position success rate P1/(P1+P2+F) exceeds 80% # Testing Data Information The EasyScan has undergone performance testing on 444 cases from 116 subjects with diverse demographic distributions covering various genders, age groups and ethnicities. The EasyScan has undergone performance testing on various body parts 13 body parts (include head, abdomen, spine, tspine, lspine, shoulder, cardiac, knee, pelvis, hip, ankle, breast, thorax) | Subjects' Characteristics | Total(N=116) | | --- | --- | | Gender | Number | | Male | 68 | | Female | 48 | | Age | | | <29 | 43 | | 29-40 | 25 | | >41 | 48 | | Magnetic field strength (T) | | | 1.5 | 24 | | 3 | 92 | | Ethnicity | | | Black | 31 | | White | 56 | | Asian | 29 | # Equipments and Protocols: The data were acquired from 1.5T and 3T magnetic resonance imaging equipment from UIH. The data were easyscout data. # Clinical Subgroups: EasyScan is a workflow function that allows automatic slice positioning for imaging. The positioning can also be adjusted manually by user. The final positioning effect is equivalent to manual operation without EasyScan feature. And no clinical subgroups Page - 18 of 45 {22} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING and confounders have been defined for the datasets ## Testing & Training Data Independence The testing dataset was collected independently from the training dataset, with separated subjects and during different time periods. Therefore, the testing data is entirely independent and does not share any overlap with the training data. ## Summary The pass criteria of EasyScan feature is 99.6%, and the results evaluated by the licensed MRI technologist with U.S. credentials. Therefore, EasyScan meets the criteria for safety and effectiveness, and EasyScan can meet the requirements for automatic positioning locates slice groups. - **t-ACS** t-ACS (temporal AI-assisted Compressed Sensing) is a dynamic magnetic resonance (MR) imaging technique, which utilizes the low-rank characteristics of time dimension, physical model and deep learning priors. t-ACS technique reconstructs multi-phase MR data and outputs multi-phase images. ## Performance test In order to validate the performance of t-ACS algorithm, i) quantification test, ii) local structure measurement and iii) temporal image performance test were conducted on test data for all three t-ACS application types. i) Quantification test was conducted based on MAE, PSNR and SSIM, which were global metrics for evaluating difference or similarity between two images. ii) Local structure measurement was conducted: sizes of the same structure of t-ACS images and fully sampled images were measured by distance measurement tool of the UIH image processing software on uMR Astra system. iii) The motion-time curve was used in temporal image performance test and was obtained by delineating ROIs in each phase of the image, calculated the average signal intensity within the regions, and then plotted the values on a coordinate graph with frame number on the horizontal axis and temporal variation on the vertical axis. Additionally, the average signal values within ROIs were subjected to consistency measurement through Bland-Altman analysis. ## Test Result Firstly, the performance test of t-ACS includes two parts: (1) AI Module Test (focused on the AI module alone). (2) t-ACS Integration Test (encompassing the t-ACS framework as a whole, as described in submitted t-ACS performance evaluation report). Page - 19 of 45 {23} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING Then, all the test results are summarized as follows: (1) AI Module Test AI prediction (AI module output) was much closer to the reference comparing to the AI module input images in all three t-ACS application types. (2) t-ACS Integration Test 1) A better consistency between t-ACS and the reference than that between CS and the reference was shown in all t-ACS application types. 2) No large structural difference appeared between t-ACS and the reference in all t-ACS application types. 3) The motion-time curves and Bland-Altman analysis showed the consistency between t-ACS and the reference based on simulated and real acquired data in all t-ACS application types. ## Data Information The training and validation datasets were collected from 108 volunteers, each volunteer was scanned by UIH MRI scanners for multiple body parts and clinical protocols, resulting in a large number of samples. Fully-sampled k-space data were collected and transformed into image domain as reference. The input data were generated by sub-sampling the fully-sampled k-space data. All data were manually quality controlled before included for training. t-ACS has undergone performance testing on 741 cases from 34 volunteers covering various genders, age groups, ethnicities and BMI groups as shown in the tables below. | Subjects' Characteristics | Total(N=34) | | --- | --- | | Gender | Number | | Male | 18 | | Female | 16 | | Age | | | 18-28 | 11 | | 29-40 | 12 | | >=41 | 11 | | Ethnicity | | | White | 10 | | Black | 9 | | Asian | 15 | | BMI | | | <= 24.9 | 14 | | >24.9 | 20 | {24} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | Body part /Phantom | Dynamic MRI scan applications | Number of cases | | --- | --- | --- | | HEAD | Type I: Non-periodic physiological movement | 92 | | SPINE | Type I: Non-periodic physiological movement | 93 | | HIP | Type I: Non-periodic physiological movement | 64 | | CARDIAC | Type II: Cardiac periodic movement | 30 | | KNEE | Type I: Non-periodic physiological movement | 94 | | ABDOMEN | Type III: Contrast enhancement | 65 | | | Type I: Non-periodic physiological movement | 86 | | PELVIS | Type III: Contrast enhancement | 30 | | | Type I: Non-periodic physiological movement | 88 | | ANKLE | Type I: Non-periodic physiological movement | 69 | | PHANTOM | Type I: Non-periodic physiological movement | 30 | ## Equipments and Protocols The test data were acquired by uMR Astra scanners, which covered representative protocols in clinical practice such as T1, T2 and PD with and without fat saturation. ## Clinical Subgroups and Confounders No clinical subgroups and confounders have been defined for these datasets. ## Independence of Training and Testing Data The testing data were collected independently from the training data, with different time periods. Therefore, the testing data were entirely independent and didn't share any overlap with the training data. The t-ACS on uMR Astra was shown to perform better than traditional Compressed Sensing in the sense of discrepancy from fully sampled images and PSNR using images from various age groups, BMIs, ethnicities and pathological variations. The structure measurements on paired images verified that same structures of t-ACS and reference were significantly the same. And t-ACS integration tests in all applications proved that t-ACS had good agreement with the reference. - AiCo In clinical magnetic resonance imaging (MRI) scans, patient movement can produce artifacts in the image that can affect diagnosis. AiCo (AI-based Motion Correction) is Page - 21 of 45 {25} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING a k-space based technique to suppress motion artifacts. When AiCo parameter is enabled, motion-corrected images will be generated, while the original images will also be retained. AiCo is not a diagnostic function, no clinical subgroups have been defined for the datasets. The training dataset for the AI module in AiCo was collected from various anatomies, image contrasts, and acceleration factors. It includes data from 114 volunteers. Each participant was scanned using UIH MRI systems across multiple body parts and clinical protocols, resulting in a total of 140,000 images. All data were quality-controlled before being included in the training process. The AiCo had already received FDA cleared for the uMR Ultra system, with the approval number K243547. AiCo has undergone performance evaluation on 24 healthy volunteers. The testing dataset was collected independently from the training dataset, with separated subjects and during different time periods. In the context of AiCo performance evaluation, the gold standard reference data refers to motionless data collected from the same individual during the same time period. The instruction during gold standard data collection is to remain still, while motion data is obtained using various movement instructions specific to different body parts and protocols. Each volunteer was scanned by UIH MRI systems for multiple body parts and clinical protocols, resulting in 112 samples, which cover representative protocols in clinical practice such as T1, T2, PD with and without fat saturation. The demographic distribution was listed in table below. | Subjects' Characteristics | Total(N=24) | | --- | --- | | Gender | Number | | Male | 12 | | Female | 12 | | Age | | | 18-28 | 8 | | 29-40 | 11 | | >41 | 5 | | Body Mass Index (BMI) | | | Under and healthy weight (<24.9) | 10 | | Overweight and obesity (>24.9) | 14 | | Ethnicity | | | White | 7 | | Black | 5 | | Asian | 12 | {26} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | Body Parts | Number of Cases | | --- | --- | | Head | 10 | | Neck | 11 | | Shoulder | 10 | | Spine | 12 | | Thorax | 6 | | Abdomen | 7 | | Cardiac | 6 | | Pelvis | 14 | | Hip | 8 | | Knee | 10 | | Ankle | 10 | | Upper Extremity | 8 | | Total | 112 | The performance evaluation of AiCo consists of two parts: The Quantification Test and the Local Structural Measurements Test. The Quantification Test quantitatively compares the PSNR and SSIM values of images processed by AiCo against the original images. Meanwhile, the Local Structural Measurements Test assesses the structural dimensions of images before and after AiCo processing, focusing on the ability of AiCo to retain image details. Results indicate that AiCo images exhibit improved PSNR and SSIM compared to the originals in the Quantification Test, with no significant structural differences from the gold standard in the Local Structural Measurements, across all gender, age, BMI, and ethnicity groups. - SparkCo SparkCo (Spark artifact Correction) is a feature that can correct the detected spark artifacts and reduce the appearance of spark artifacts for clinical review. 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 the detected spark artifacts and reduce the appearance of spark artifacts for clinical review. 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. Page - 23 of 45 {27} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING 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 below. 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 below. | Subjects' Characteristics | Total(N=15) | | --- | --- | | Gender | | | Male | 9 | | Female | 6 | | Age | | | 18-29 | 2 | | 30-44 | 8 | | 45-64 | 4 | | >=65 | 0 | | Ethnicity | | | Asian | 15 | | Body Mass Index (BMI) | | | Underweight (<18.5) | 2 | | Healthy weight (18.5-24.9) | 10 | | Overweight (25.0-29.9) | 3 | | Obesity (>=30.0) | 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. | 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 below shows. {28} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | 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 | 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 2. 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 need to be higher than the spark images. 2. Spark artifacts need to be reduced or corrected after enable the SparkCo. | 1. The average PSNR of spark-corrected images is 1.6 higher than the spark images. 2. The images with spark artifacts were successfully corrected after enable the SparkCo. | In summary, SparkCo meets the criteria for safety and effectiveness, and can used to detect and correct spark artifacts for improving image quality. - ImageGuard In clinical magnetic resonance imaging (MRI) scans, patient movement can produce artifacts in the image; ImageGuard is a workflow function, which is expected for automatic monitoring of MR images for motion artifacts and providing real-time prompts to assist technicians in image quality control. And the prompts do not affect scanning process. Acceptance Criteria To verify the ImageGuard of the algorithm, the subjective evaluation method was used. The test pass criteria was: Success rate P/(P+F) exceeds 90%. Pass(P): There are two scenarios which meet the pass requirements, including (1) The volunteers move, the image quality does not meet the users’ requirements, and the prompt appears; (2) The volunteers do not move, the image quality meets the users’ requirements, and the prompt does not appear. Fail (F): There are two scenarios which the tests will fail, including (1) The volunteers move, the image quality does not meet the users’ requirements, and the prompt does not appear; (2) The volunteers do not move, the image quality meets the users’ requirements, and Page - 25 of 45 {29} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING the prompt appears. Test pass criteria: Success rate P/(P+F) exceeds 90%. ## Testing Data Information The ImageGuard has undergone performance testing with 191 cases from 80 subjects with diverse demographic distributions covering various genders, age groups and ethnicities. | Subjects' Characteristics | Total(N=80) | | --- | --- | | Gender | Number | | Male | 45 | | Female | 35 | | Age | | | ≤25 | 14 | | 26-50 | 47 | | ≥51 | 19 | | Magnetic field strength (T) | | | 1.5 | 31 | | 3 | 49 | | Ethnicity | | | White | 35 | | Black | 21 | | Asian | 24 | ## Equipments and Protocols: The data were acquired from 1.5T and 3T magnetic resonance imaging equipment from UIH. The data were FSE and GRE sequence data, including T2, T1, PD contrast. ## Clinical Subgroups: ImageGuard is a workflow function, which is expected for automatic monitoring of MR images for motion artifacts and providing real-time prompts to assist technicians in image quality control. And the prompts do not affect scanning process. And no clinical subgroups and confounders have been defined for the datasets. ## Testing & Training Data Independence The testing dataset was collected independently from the training dataset, with separated subjects and during different time periods. Therefore, the testing data is entirely independent and does not share any overlap with the training data. Page - 26 of 45 {30} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING # Summary The pass criteria of ImageGuard feature is 100%, and the results evaluated by the licensed MRI technologist with U.S. credentials. Therefore, ImageGuard meets the criteria for safety and effectiveness. ## EasyCrop EasyCrop is a function that enables automatic cropping of images scanned with the MRA images to simplify the workflow, which allows users to obtain interference-free MIP images and automatically rotated MIP images with different angles when the scan is completed and images are generated. After enabling the EasyCrop function, the original images of MRA images will still be saved. ## Acceptance Criteria To verify the EasyCrop of the algorithm, the subjective evaluation method was used. The test pass criteria was: No Fail cases and pass rate P1/ (P1+P2 +F) exceeds 90%. Pass(P1): The other peripheral tissues are cropped, and the cropped images meet the users' requirements. Pass(P2): The cropped images do not meet the users' requirements and the users can re-crop the original images in review3D. Fail (F): EasyCrop does not operate successfully, or the original images are not saved Test pass criteria: No Fail cases and pass rate P1/ (P1+P2 +F) exceeds 90%. ## Testing Data Information The EasyCrop has undergone performance testing on 5 intended imaging body parts (head vessels, carotid vessels, renal vessels, pancreaticobiliary and lower extremity vessels) with diverse demographic distributions covering various genders, age groups. | Subjects' Characteristics | Total(N=65) | | --- | --- | | Gender | Number | | Male | 37 | | Female | 28 | | Age | | | <29 | 23 | | 29-40 | 9 | | >41 | 33 | | Magnetic field strength (T) | | | 1.5T | 18 | | 3.0T | 47 | {31} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | Ethnicity | | | --- | --- | | Asian | 12 | | Black | 19 | | White | 34 | ## Equipments and Protocols: The data were acquired from 1.5T and 3T magnetic resonance imaging equipment from UIH. The data were FSE and GRE sequence data. ## Clinical Subgroups EasyCrop is a function that enables automatic cropping of images scanned with the MRA images to simplify the workflow, after enabling the EasyCrop function, the original images of MRA images will still be saved. Therefore, no clinical subgroups and confounders have been defined for the datasets. ## Testing & Training Data Independence The testing dataset was collected independently from the training dataset, with separated subjects and during different time periods. Therefore, the testing data is entirely independent and does not share any overlap with the training data. The pass criteria of EasyCrop feature is 100%, and the results evaluated by the licensed MRI technologist with U.S. credentials. Therefore, EasyCrop meets the criteria for safety and effectiveness, and EasyCrop can meet the requirements for automatic cropping. - **EasyBolus** EasyBolus is a workflow function that achieved full-process automation of enhanced scanning through EasyScan and Auto Bolus Tracker. EasyBolus is a combination of the aforementioned features, with only new interface parts added, and the algorithm parts are completely reused. The EasyScan algorithm is an artificial intelligence algorithm that utilizes neural networks, while the Auto Bolus Tracker is a traditional algorithm that does not use AI. ## Acceptance Criteria The details of the automatic positioning can be found in the EasyScan test report, and the results of the automatic triggering can be found in the Auto Bolus Tracker report. To test the combination of EasyScan and Auto Bolus Tracking features performs as expected, the subjective evaluation method was used. The evaluation results for EasyBolus are carried out by certified professionals in the United States. Page - 28 of 45 {32} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING The test pass criteria was: No Fail cases and success rate P1+P2/(P1+P2+F) exceeds 100%. Pass with level 1 (P1): The monitoring point positioning meets the user's requirements and the frame difference between the frame of auto bolus tracker and the result judged by experienced MRI technologists frame is less than or equal to 1 frame. Pass with level 2 (P2): The monitoring point positioning meets the user's requirements and the frame difference between the frame of auto bolus tracker and the result judged by experienced MRI technologists is 2 frames. Fail (F): Auto position not generated or cannot be adjusted afterwards or the frame difference between the frame of auto bolus tracker and the result judged by experienced MRI technologists is larger than 2 frames. ## Testing Data Information The EasyBolus has undergone performance testing on 20 subjects | Subjects' Characteristics | Total(N=20) | | --- | --- | | Gender | Number | | Male | 12 | | Female | 8 | | Age | | | <60 | 11 | | >60 | 9 | | Protocol | | | neck_easy_scout | 20 | | BolusTracker_cor | 20 | | Magnetic field strength (T) | | | 3.0 | 20 | | Ethnicity | | | Asia | 20 | The algorithm used in EasyScan is AI-based, and the test data for EasyScan incorporates information on various genders, ages, and ethnicities. For further details, please refer to the EasyScan summary. We have conducted tests across different genders, ages, and ethnicities. In contrast, Auto Bolus Tracker is not AI-driven; it is solely concerned with variations in image brightness, making it unrelated to the gender, age, or ethnicity of the test data. Therefore, no special considerations were made regarding these factors during the testing process. ## Performance Testing Summary Pass with level 1 (P1) = 80% Page - 29 of 45 {33} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING Pass with level 2 (P2) = 20% Total Failure Rate = 0% Pass = 100% ## Testing & Training Data Independence The testing dataset was collected independently from the training dataset, with separated subjects and during different time periods. Therefore, the testing data is entirely independent and does not share any overlap with the training data. ### EasyFACT EasyFACT workflow, based on the FACT sequence, automatically places the ROI (Regions of Interest) of 5 suitable locations on the liver. ### Acceptance Criteria The validation type and acceptance criteria is shown in the table below. | Validation Type | Acceptance Criteria | | --- | --- | | Passing Rate | Satisfied and Acceptable ratio (S+A)/(S+A+F) exceeds 95%. Satisfied (S): Five ROIs are placed within the liver parenchyma, avoiding the liver borders and vascular structures. Acceptable (A): Fewer than five ROIs are placed within the liver parenchyma, avoiding the liver borders and vascular structures. Failure (F): ROIs are positioned on liver borders or vascular structures, or no ROIs are placed. | ## Testing Data Information The distribution of volunteer dataset used for validation is listed in the table below. A total of 25 cases from 25 volunteers were used. | Subjects' Characteristics | Total(N=25) | | --- | --- | | Gender | Number | | Male | 20 | | Female | 5 | | Age | | | <30 | 6 | | [30,40) | 7 | | [40,50) | 6 | | [50,60) | 3 | | [60,70) | 2 | | >=70 | 1 | | Weight (kg) | | | <80 | 14 | Page - 30 of 45 {34} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | [80, 90) | 4 | | --- | --- | | >=90 | 7 | | Ethnicity | | | Asian | 11 | | White | 9 | | Black | 5 | ## Performance Testing Summary Subjective evaluation results of 25 volunteers: satisfied and acceptable ratio is 100%. Meanwhile, the subgroup analysis shows that the EasyFACT workflow has good generalization in different subgroups. | Gender | Satisfied (S) | Acceptable (A) | Failure (F) | Satisfied and Acceptable Ratio | | --- | --- | --- | --- | --- | | Male | 100% | 0% | 0% | 100% | | Female | 100% | 0% | 0% | 100% | | Age | | | | | | <30 | 100% | 0% | 0% | 100% | | [30,40) | 100% | 0% | 0% | 100% | | [40,50) | 100% | 0% | 0% | 100% | | [50,60) | 100% | 0% | 0% | 100% | | [60,70) | 100% | 0% | 0% | 100% | | >=70 | 100% | 0% | 0% | 100% | | Weight (kg) | | | | | | <80 | 100% | 0% | 0% | 100% | | [80, 90) | 100% | 0% | 0% | 100% | | >=90 | 100% | 0% | 0% | 100% | | Ethnicity | | | | | | Asian | 100% | 0% | 0% | 100% | | White | 100% | 0% | 0% | 100% | | Black | 100% | 0% | 0% | 100% | ## Testing & Training Data Independence The training data used for the training of EasyFACT is independent of the data used to test the algorithm. ### TI Scout TI Scout is a function that automatically detects the TI frame which has the darkest ventricular myocardium of the TI Scout image, allowing users to achieve the best inversion time (TI). ### Acceptance Criteria Test procedure, acceptance method, and acceptance criteria is shown in the table Page - 31 of 45 {35} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING below. | Validation Type | Acceptance Criteria | | --- | --- | | Error between TI frame output by algorithm and gold standard | The average frame difference between the frame of auto-calculated TI and the gold standard frame is less than or equal to 1 frame, and the maximum frame difference is less than or equal to 2 frames. | ## Testing Data Information A total of 27 patients were used as the test data. The distribution is as the following table. | Subjects' Characteristics | Total(N=27) | | --- | --- | | Gender | Number | | Male | 18 | | Female | 9 | | Age | | | <18 | 1 | | 18-28 | 4 | | 29-40 | 7 | | >41 | 15 | | Protocol | | | Tlscout_sax | 27 | | BMI (kg/m(2)) | | | <18.5 | 1 | | [18.5, 25) | 10 | | >=25 | 11 | | Unknown | 5 | | Magnetic field strength (T) | | | 1.5 | 18 | | 3 | 9 | | Ethnicity | | | Asian | 19 | | White | 8 | ## Performance Testing Summary According to the subgroup analysis in the table below, it can be seen that the TI Scout algorithm performs as expected in different subgroups. | Gender | Number | Average frame difference | maximum frame difference | | --- | --- | --- | --- | | Male | 18 | 0.38 | 2 | | Female | 9 | 0.44 | 1 | Page - 32 of 45 {36} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | Age | | | | | --- | --- | --- | --- | | <18 | 1 | 0 | 0 | | 18-28 | 4 | 0.25 | 1 | | 29-40 | 7 | 0.57 | 2 | | > 41 | 15 | 0.33 | 1 | | Protocol | | | | | TIscout_sax | 27 | 0.37 | 2 | | BMI (kg/m(2)) | | | | | <18.5 | 1 | 0 | 0 | | [18.5, 25) | 10 | 0.7 | 2 | | >=25 | 11 | 0.27 | 1 | | Unknown | 5 | 0 | 0 | | Magnetic field strength (T) | | | | | 1.5 | 18 | 0.39 | 2 | | 3 | 9 | 0.33 | 1 | | Ethnicity | | | | | Asian | 19 | 0.47 | 2 | | White | 8 | 0.125 | 1 | ## Testing & Training Data Independence The training data used for the training of the TI Scout algorithm is independent of the data used to test the algorithm. - **Inline MOCO** 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. ## Acceptance Criteria The validation type and acceptance criteria is shown in the table below. | Validation Type | Acceptance Criteria | | --- | --- | | Dice | The average Dice coefficient of the left ventricular myocardium after motion correction is greater than 0.87. | Page - 33 of 45 {37} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING # Testing Data Information ## 1) Cardiac Perfusion Images Subgroup Information Sample Size: | 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. | 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 | | | >=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: | Dataset | Patients Number | Cases Number | | --- | --- | --- | | Testing Data | 33 | 182 | (Note: One patient may have multiple cases, because of the differences in slice location.) Page - 34 of 45 {38} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING ## 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. | Subgroup | Details of each subgroup | Number of cases | | --- | --- | --- | | 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 | | | 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 the proposed device algorithm has good generalization in different subgroups. | 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 | {39} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | Asian | 0.93 | | --- | --- | | 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 the proposed device algorithm has good generalization in different subgroups. | 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 | Page - 36 of 45 {40} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING # 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. # - Inline Cardiac Function # Inline ED/ES Phases Recognition Inline ED/ES phases recognition algorithm of the inline cardiac function is used to get the ED and ES Phases based on the automatic left ventricular volume segmentation. The performance testing for inline ED/ES phases recognition algorithm was performed on 95 cases. # Acceptance Criteria The validation type and acceptance criteria is shown in the table below. | 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. | # Testing Data Information The distribution and protocols for volunteer dataset used for validation is listed in the table below. A total of 95 cases from 56 volunteers were used. | 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 | Page - 37 of 45 {41} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | 3.0T | 36 | 66 | | --- | --- | --- | | Unknown | 10 | 10 | | Disease conditions | | | | NOR | 46 | 85 | | MINF | 2 | 2 | | DCM | 2 | 2 | | HCM | 5 | 5 | | ARV | 1 | 1 | | Ethnicity | | | | Asian | 22 | 60 | | White | 26 | 26 | | Black | 8 | 9 | ## Performance Testing Summary 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. | 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 | Page - 38 of 45 {42} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | White | 26 | 0.19 | | --- | --- | --- | | Black | 9 | 0.13 | ## Testing& Training Data Independence 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. ## Inline Cardiac Function Segmentation Based on short axis images of the heart, the Inline Cardiac Function Segmentation algorithm is used to segment the left ventricle (LV), right ventricle (RV), and left ventricular myocardium to achieve inline cardiac function. ## Acceptance Criteria The validation type and acceptance criteria is shown in the table below. | Validation Type | Acceptance Criteria | | --- | --- | | Similarity | To evaluate the Inline Cardiac Function Segmentation, we compared the results with those of the cardiac function application of uWS-MR workstation (cleared via K192601).66 test data were used, all of which were scanned well without severe artifacts. Analyze DICE of algorithm segmentation results between two devices to evaluate consistency. If dice>0.95, it is considered consistent between the two. | ## Testing Data Information We evaluated 66 cases to validate the algorithm. The distribution is as the following table. | Subjects' Characteristics | Total(N=66) | | --- | --- | | Gender | Number | | Male | 12 | | Female | 4 | | Unknown | 50 | | Age | | | 18-40 | 4 | | >41 | 12 | | Unknown | 50 | | BMI (kg/m(2)) | | | <18.5 | 3 | | [18.5, 25) | 20 | | >=25 | 37 | | Unknown | 6 | | Magnetic field strength (T) | | Page - 39 of 45 {43} Shanghai United Imaging Healthcare Co., Ltd. Tel: +86 (21) 67076888 Fax: +86 (21) 67076889 www.united-imaging.com UNITED IMAGING | 1.5 | 3 | | --- | --- | | 3 | 10 | | 5 | 3 | | Unknown | 50 | | Ethnicity | | | Asia | 16 | | White | 50 | | Disease conditions | | | Negative | 40 | | Positive | 10 | | Unknown | 16 | ## Performance Testing Summary The test set data validation results are as follows: | sample size of test set | Sample size whose results meet the design requirements | Sample size whose results do not meet the design requirements | | --- | --- | --- | | 66 | 66 | 0 | ## Testing & Training Data Independence The training data used for the training of the Inline Cardiac Function Segmentation algorithm is independent of the data used to test the algorithm. ### Inline ECV Inline ECV aims to calculate the pixel-wise ECV (extracellular volume fraction) images. We have native and post T1 mapping. In general, the native and post T1 mapping data are aligned. We also provide the registration to keep them aligned. Then we will segment a region in the left ventricle blood pool. Finally,…

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