(147 days)
The uPMR 790 system combines magnetic resonance diagnostic devices (MRDD) and Positron Emission Tomography (PET) scanners that provide registration and fusion of high resolution physiologic and anatomic information, acquired simultaneously and iso-centrically. The combined system maintains independent functionality of the MR and PET devices, allowing for single modality MR and/or PET imaging. The MR is intended to produce 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. Contrast agents may be used depending on the reqion of interest of the scan. The PET provides distribution information of PET radiopharmaceuticals within the human body to assist healthcare providers in assessing the metabolic and physiological functions. The combined system utilizes the MR for radiation-free attenuation correction maps for PET studies. The system provides inherent anatomical reference for the fused PET and MR images due to precisely aligned MR and PET image coordinate systems.
The uPMR 790 system is a combined Magnetic Resonance Diagnostic Device (MRDD) and Positron Emission Tomography (PET) scanner. It consists of components such as PET detector, 3.0T superconducting magnet, RF power amplifier, RF coils, gradient power amplifier, gradient coils, patient table, spectrometer, computer, equipment cabinets, power distribution system, internal communication system, vital signal module, and software etc.
The uPMR 790 system provides simultaneous acquisition of high resolution metabolic and anatomic information from PET and MR. PET detectors are integrated into the MR bore for simultaneous, precisely aligned whole body MR and PET acquisition. The PET subsystem supports Time of Flight (ToF). The system software is used for patient management, data management, scan control, image reconstruction, and image archive. The uPMR 790 system is designed to conform to NEMA and DICOM standards.
This traditional 510(k) is to request modifications for the cleared uPMR 790(K222540). The modifications performed on the uPMR 790 (K222540) in this submission are due to the following changes that include:
- (1) Addition of RF coils: SuperFlex Body 24, SuperFlex Large -12, SuperFlex Small -12.
- (2) Addition and modification of pulse sequences:
- (a) New sequences: gre fine, fse arms dwi, fse dwi, fse mars sle, grase, gre_bssfp_ucs, gre_fq, gre_pass, gre_quick_4dncemra, gre_snap, gre_trass, gre_rufis, epi_dwi_msh, svs_wfs, svs_stme.
- (b) Added Associated options for certain sequences: QScan, MultiBand, Silicon-Only Imaging, MoCap-Monitoring, T1rho, CEST, Inline T2 mapping, CASS, inline FACT, uCSR, FSP+, whole heart coronary angiography imaging, mPLD (Only output original control/labeling images and PDw(Proton Density weighted) images, no quantification images are output).
- (c) Name change of certain sequences: gre ute(old name: gre ute sp), svs_press(old name: press),svs_steam(old name: steam), csi_press(old name: press), csi hise(old name: hise).
- (3) Addition of MR imaging processing methods: 2D Flow, 4D Flow, SNAP, CEST, T1rho, FSP+, CASS, PASS, Inline T2 Mapping and DeepRecon.
- (4) Addition and modification of PET imaging processing methods:
- (a) The new PET imaging processing methods: Hyper DPR (also named HYPER AiR) and Digital Gating (also named Self Gating).
- (b) The modified method: HYPER Iterative.
- (5) Addition of MR image reconstruction methods: AI-assisted Compressed Sensing (ACS).
- (6) Addition and modification of workflow features:
- (a) The new workflow features: EasyCrop, MoCap-Monitoring and QGuard-Imaging.
- (b) The modified workflow feature: EasyScan.
- (7) Addition Spectroscopy: Liver Spectroscopy, Breast Spectroscopy.
- (8) Additional function: MR conditional implant mode.
The provided text does not contain detailed acceptance criteria for the uPMR 790 device in the format of a table, nor does it describe a specific study proving the device meets these criteria in a comparative effectiveness study or standalone performance study as would typically be presented for an AI/ML medical device.
The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed clinical study report with specific performance metrics against acceptance criteria.
However, based on the information available, I can extract and infer some aspects related to acceptance criteria and the performance study:
Inferred Acceptance Criteria and Reported Device Performance (based on provided text):
The device is an integrated MR-PET system. The modifications primarily involve new RF coils, pulse sequences, imaging processing methods, and workflow features. The performance data section describes non-clinical testing to verify that the proposed device met design specifications and is Substantially Equivalent (SE) to the predicate device.
While explicit quantitative acceptance criteria are not tabulated, the text implies that the performance of the modified device (uPMR 790) must be at least equivalent to, or better than, the predicate and reference devices regarding image quality and functionality.
Specifically for the new or modified features related to AI/ML (DeepRecon and ACS), the implicit acceptance criteria appear to be:
- DeepRecon:
- Equivalence in performance to DeepRecon on the uMR Omega.
- Better performance than NADR (No DeepRecon) in SNR and resolution.
- Maintenance of image qualities (contrast, uniformity).
- Significantly same structural measurements between DeepRecon and NADR images.
- ACS:
- Equivalence in performance to ACS on the uMR Omega (K220332).
- Better performance than CS in SNR and resolution.
- Maintenance of image qualities (contrast, uniformity) compared to fully sampled data (golden standard).
- Significantly same structural measurements between ACS and fully sampled images.
Table of Inferred Acceptance Criteria and Reported Device Performance:
| Feature/Metric | Acceptance Criteria (Inferred) | Reported Device Performance |
|---|---|---|
| Overall Device | Substantial Equivalence (SE) to predicate device (K222540) in performance, safety, and effectiveness. | Found to have a safety and effectiveness profile similar to the predicate device. |
| Image Performance | Meet all design specifications; generate diagnostic quality images. | Diagnostic quality images in accordance with MR guidance. |
| DeepRecon (general) | Equivalent to DeepRecon on uMR Omega. | Performs equivalently to DeepRecon on uMR Omega. |
| DeepRecon (SNR/Resolution) | Better than NADR. | Performs better than NADR. |
| DeepRecon (Quality) | Maintain image qualities (contrast, uniformity). | Maintained image qualities (contrast, uniformity). |
| DeepRecon (Structures) | Significantly same structural measurements as NADR. | Significantly same structural measurements as NADR. |
| ACS (general) | Equivalent to ACS on uMR Omega (K220332). | Performs equivalently to ACS on uMR Omega. |
| ACS (SNR/Resolution) | Better than CS. | Performs better than CS. |
| ACS (Quality) | Maintain image qualities (contrast, uniformity) as compared to fully sampled data. | Maintained image qualities (contrast, uniformity) compared to fully sampled data. |
| ACS (Structures) | Significantly same structural measurements as fully sampled data. | Significantly same structural measurements as fully sampled images. |
Breakdown of the Study as described in the 510(k) Summary:
2. Sample size used for the test set and the data provenance:
-
DeepRecon:
- "The testing dataset for performance testing was collected independently from the training dataset, with separated subjects and during different time periods."
- The exact sample size (number of subjects/cases) for the DeepRecon test set is not specified beyond being "independent."
- Data Provenance: Implied to be from UIH MRI systems, likely from clinical or volunteer scans. No specific country of origin or retrospective/prospective nature is stated for the test datasets, but training data was "collected from 264 volunteers" and "165,837 cases" using "UIH MRI systems," which suggests internal company data, likely from China where the company is based. The testing data is independently collected.
-
ACS:
- "The training and test datasets are collected from 35 volunteers, including 24 males and 11 females, ages ranging from 18 to 60. The samples from these volunteers are distributed randomly into training and test datasets."
- "The validation dataset is collected from 15 volunteers, including 10 males and 5 females, whose ages range from 18 to 60."
- It specifies "35 volunteers" for training+test and "15 volunteers" for validation. The text states "testing dataset for performance testing was collected independently from the training dataset," which contradicts the "distributed randomly into training and test datasets" statement for the 35 volunteers. This requires clarification, but assuming the 35 volunteers contributed to both, the total number used for testing is not explicitly broken out from the 35. The "validation dataset" of 15 volunteers seems to be an additional independent test set.
- Data Provenance: Implied to be from UIH MRI systems. No specific country of origin or retrospective/prospective nature is stated.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Expert Review: "Sample clinical images for all clinical sequences and coils were reviewed by U.S. board-certified radiologist comparing the proposed device and predicate device."
- Number of experts: Not specified, only "radiologist" (singular or plural not clear).
- Qualifications: "U.S. board-certified radiologist." No mention of years of experience.
- Quantitative/Objective Ground Truth: For DeepRecon and ACS, ground truth was not established by experts but rather by specific technical methods:
- DeepRecon: "multiple-averaged images with high-resolution and high SNR were collected as the ground-truth images."
- ACS: "Fully-sampled k-space data were collected and transformed to image space as the ground-truth."
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- The document implies a technical assessment for AI performance (SNR, resolution, structural measurements). For the "U.S. board-certified radiologist" review, no specific adjudication method (e.g., 2+1 consensus) is mentioned.
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 involving human readers and AI assistance is described. The performance evaluation focuses on the technical imaging characteristics and comparison to the predicate device or baseline (NADR/CS). The "U.S. board-certified radiologist" review seems to be a qualitative assessment of diagnostic image quality rather than a structured MRMC study with quantitative outcomes.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, the performance tests for DeepRecon and ACS are described as standalone evaluations of the algorithms' effects on image quality (SNR, resolution, contrast, uniformity, structural measurements) by comparing them to NA (No Algorithm) or baseline (CS) methods.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- DeepRecon: "multiple-averaged images with high-resolution and high SNR" (objective, technical ground truth representing optimal image quality).
- ACS: "Fully-sampled k-space data" (objective, technical ground truth representing complete data).
- For the qualitative review by the radiologist, the "diagnostic quality images" from the predicate device implicitly served as a reference or ground truth for comparison.
8. The sample size for the training set:
- DeepRecon: "264 volunteers" resulting in "165,837 cases."
- ACS: "35 volunteers" (randomly distributed into training and test datasets). The exact split for training is not specified but is part of this 35.
9. How the ground truth for the training set was established:
- DeepRecon: "the multiple-averaged images with high-resolution and high SNR were collected as the ground-truth images." "All data were manually quality controlled before included for training."
- ACS: "Fully-sampled k-space data were collected and transformed to image space as the ground-truth." "All data were manually quality controlled before included for training."
In summary, the provided document focuses on demonstrating technical equivalence and improved image characteristics for the AI components (DeepRecon, ACS) through non-clinical testing against technically derived ground truths, rather than a clinical multi-reader study with expert consensus ground truth or outcomes data. The human reader involvement seems to be a qualitative review of diagnostic image quality rather than a formal MRMC study.
§ 892.1200 Emission computed tomography system.
(a)
Identification. An emission computed tomography system is a device intended to detect the location and distribution of gamma ray- and positron-emitting radionuclides in the body and produce cross-sectional images through computer reconstruction of the data. This generic type of device may include signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts, and accessories.(b)
Classification. Class II.