Search Results

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:

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.

Ask a specific question about this device

The uPMR 790 system combines magnetic resonance diagnostic devices (MRDD) and Position Tomography (PET) scanners that provide registration 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 that display internal anatomical structure and/or function of the head, body and extremities. Contrast agents may be used depending on the region 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.

The provided text describes modifications to the uPMR 790 system, focusing on new software features and updated specifications. The key study related to an AI module is the "WFI based head MRAC" (Water Fat Imaging based PET head attenuation correction).

Here's an breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test set for WFI-based head MRAC (AI module): 27 subjects (17 male, 10 female; Age: 15-78).
  • Provenance: All subjects were Chinese.
  • Data Independence: Test data from Center 2 (n=12) was initially excluded from the training set and from completely different subjects. Data from Center 1 (n=10) was collected almost 2 years after the training data's imaging date, also with different subjects. This confirms the test data was completely independent from the training data (prospective data collection from geographically and temporally distinct sources relative to the training set). The document implies a retrospective collection of these test cases from these two centers for the purpose of this validation.

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

• This information is not explicitly stated in the provided text for the WFI-based head MRAC study. The ground truth method is described as "three-compartment segmentation from CT images of the same person," and "image intensity threshold" for further segmentation, implying an objective, image-based ground truth rather than expert consensus on a subjective scale.

4. Adjudication Method for the Test Set

• Adjudication method is not applicable and not mentioned, as the ground truth for the AI module's performance was established via CT segmentation and image intensity thresholds rather than human readers.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted or reported in the provided text. The study focuses on the standalone performance of the AI module for attenuation correction, compared against CT-based ground truth (CTAC). There is no mention of human readers assisting with the AI or a comparison of human readers with and without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Yes, a standalone performance evaluation was done for the "WFI based head MRAC" AI module. The performance was measured by comparing the SUVmean error of the AI-generated attenuation correction maps against CTAC (CT-based attenuation correction), which is considered the reference standard.

7. The Type of Ground Truth Used

• For the WFI-based head MRAC AI module, the ground truth used was "three-compartment segmentation from CT images of the same person" and subsequent separation using "image intensity threshold." This can be classified as a radiological/imaging-based ground truth derived from an existing gold standard imaging modality (CT).

8. The Sample Size for the Training Set

• Training dataset: Not explicitly stated as a single number. The text provides demographic information: Gender (76 male, 54 female), Age (17-83), Ethnicity (Chinese). This implies a total of 130 subjects in the training set (76 + 54).

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

• The ground truth for the training set for the WFI-based head MRAC AI module was established using "pairs of WFI images and three-compartment segmentation from CT images of the same person." This means that for each subject in the training set, both WFI MR images and corresponding CT images were acquired, and the CT images were segmented into three compartments (air, cortical bone, mixed compartment) to serve as the ground truth for attenuation correction mapping. The AI module (Convolution Neural Network) was then trained to generate these segmentation masks from the WFI images.

Ask a specific question about this device

The uPMR 790 system combines magnetic resonance diagnostic devices (MRDD) and Positron Emission Tomography (PET) scanners that provide registration 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 region 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.

The provided text is a 510(k) summary for the uPMR 790, a combined Magnetic Resonance Diagnostic Device (MRDD) and Positron Emission Tomography (PET) scanner. This document describes the device, its intended use, and the modifications made from a previously cleared predicate device (K183014).

Based on the provided information, the 510(k) pertains to modifications of an existing device and does not describe a study involving detailed acceptance criteria and performance metrics for an AI/algorithm-driven device in the manner requested.

Here's a breakdown of why and what information is available:

1. Table of Acceptance Criteria and Reported Device Performance:

• Not Applicable. The document describes a device modification and its non-clinical testing. It does not present specific acceptance criteria in terms of accuracy, sensitivity, specificity, or other performance metrics for an AI or algorithm, nor does it report the device's performance against such criteria. The testing focused on demonstrating that the modified hardware (magnet, PET gantry, coils) maintains the expected functionality and safety of the device.

Reasons for Not Applicable:

• The 510(k) is for a combined MR/PET imaging system (hardware), not an AI/software diagnostic tool.
• The "performance data" section focuses on hardware-related tests (e.g., NEMA NU 2 for PET, Signal to Noise Ratio, Geometric Distortion, Magnetic Field Homogeneity for MR) to ensure the modified device functions similarly to its predicate. These are engineering specifications, not clinical performance metrics for an algorithm's diagnostic accuracy.

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

• Not Applicable for AI/algorithm performance. The document states, "No clinical testing was conducted on the proposed devices." The non-clinical testing involved hardware measurements and phantom studies, not a test set of patient data to evaluate an algorithm's diagnostic performance.
• Data Provenance: Not applicable as there was no clinical testing on data.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• Not Applicable. Since no clinical testing was conducted on patient data, there was no need for experts to establish ground truth for a test set.

4. Adjudication Method:

• Not Applicable. No clinical test set requiring adjudication was used.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• No. The document explicitly states: "No clinical testing was conducted on the proposed devices." Therefore, no MRMC study was performed.

6. Standalone Performance Study:

• No. The document explicitly states: "No clinical testing was conducted on the proposed devices." This implies no standalone (algorithm-only) performance testing on clinical data was performed either. The focus was on hardware modifications.

7. Type of Ground Truth Used:

• Not Applicable for AI/algorithm performance. As no clinical testing was performed, no ground truth from pathology, outcomes data, or expert consensus was used to evaluate an algorithm's performance. The "ground truth" in the context of the non-clinical testing would be the expected physical or electrical properties of the system, measured via phantoms and engineering tests.

8. Sample Size for the Training Set:

• Not Applicable. There is no mention of an AI/machine learning algorithm within the scope of this 510(k) that would require a training set. The device is a medical imaging hardware system.

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

• Not Applicable. As no training set for an AI/ML algorithm is mentioned, this information is not provided.

Summary of what the document does provide:

The document focuses on demonstrating substantial equivalence of a modified MR/PET imaging system (uPMR 790) to its predicate device (also uPMR 790, K183014) based on hardware changes. The modifications include:

• Introduction of a new magnet.
• Change in the PET gantry structure and RF shield.
• Introduction of six new receive coils and re-categorization of existing coils.

The non-clinical testing performed ensured that these hardware changes did not negatively impact the device's fundamental safety and performance characteristics, such as:

• Electrical safety (IEC 60601-1, -1-2, -2-33).
• PET performance measurements (NEMA NU 2).
• MR imaging quality (Signal to Noise Ratio, Geometric Distortion, Image Uniformity, Magnetic Field Homogeneity, Magnetic Field Decay).
• PET/MR Attenuation Correction.
• Acoustic Noise.
• Surface Heating of RF Receive Coils.

The conclusion is that the modified device remains substantially equivalent to the predicate, and introduces no new indications for use, technological characteristics, or potential hazards/safety risks.

Ask a specific question about this device

The uPMR 790 system combines magnetic resonance diagnostic devices (MRDD) and Positron Emission Tomography (PET) scanners that provide registration 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 region of interest of the scan. The PET provides distribution information of PET radiopharmaceuticals within the human body to assist healtheare 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.

The provided text does not contain specific acceptance criteria or details of a study that directly proves the device meets such criteria in the way a clinical performance study would for an AI-powered diagnostic device.

Instead, the document is a 510(k) summary for a combined MRDD and PET scanner (uPMR 790), asserting substantial equivalence to a predicate device (GE Healthcare's SIGNA PET/MR, K142098). The focus is on demonstrating that the new device has similar technological characteristics and performs as expected, not on specific diagnostic performance metrics (e.g., sensitivity, specificity) against a clinical ground truth.

Here's what can be inferred from the provided text regarding acceptance and testing:

1. A table of acceptance criteria and the reported device performance:

The document lists various non-clinical standards and tests that were conducted. These standards serve as "acceptance criteria" in the sense that the device must comply with them to be considered safe and effective. The "reported device performance" is a general statement that the device "performs as expected" and the "test results demonstrated that the device performs as expected and thus, it is substantially equivalent to the predicate devices." No specific quantitative performance metrics like sensitivity, specificity, or AUC are provided as acceptance criteria or results.

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

• Sample Size: Not specified for the "sample clinical images" provided.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). The text only mentions "Sample clinical images were provided."

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

This information is not provided. The phrase "diagnostic quality images" implies expert evaluation, but the number or qualifications of experts are not stated.

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

Not specified.

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:

This is not an AI-powered diagnostic device, but rather a combined imaging system (PET/MR). Therefore, an MRMC study comparing human readers with and without AI assistance is not applicable and was not conducted. The study is about the performance of the imaging device itself.

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

Not applicable, as this is an imaging device, not an algorithm. The "standalone" performance here refers to the device's ability to produce images according to technical standards.

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

For the clinical images, the "ground truth" seems to be implied by the assessment of "diagnostic quality images." This would likely involve expert interpretation, but explicit details (like expert consensus, pathology, or outcomes) are not given. For the non-clinical tests, the ground truth is against specific engineering and medical device standards (e.g., NEMA NU 2-2012 for PET performance, NEMA MS series for MR performance).

8. The sample size for the training set:

Not applicable. This device is not an AI algorithm that requires a training set. It is a hardware imaging system.

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

Not applicable, as this device is not an AI algorithm.

Ask a specific question about this device