(201 days)
The MAGNETOM system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images, spectroscopic images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These images and/or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.
The MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.
The subject device, MAGNETOM Cima.X with software syngo MR XA61A, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Vida with syngo MR XA50A (K213693).
A high-level summary of the new and modified hardware and software is provided below:
For MAGNETOM Cima.X with syngo MR XA61:
Hardware
New Hardware:
→ 3D Camera
Modified Hardware:
- → Host computers ((syngo MR Acquisition Workplace (MRAWP) and syngo MR Workplace (MRWP)).
- → MaRS (Measurement and Reconstruction System).
- → Gradient Coil
- → Cover
- → Cooling/ACSC
- → SEP
- → GPA
- → RFCEL Temp
- → Body Coil
- → Tunnel light
Software
New Features and Applications:
- -> GRE_PC
- → Physio logging
- -> Deep Resolve Boost HASTE
- → Deep Resolve Boost EPI Diffusion
- → Open Recon
- -> Ghost reduction (DPG)
- -> Fleet Ref Scan
- → Manual Mode
- → SAMER
Modified Features and Applications:
- → BEAT_nav (re-naming only).
- → myExam Angio Advanced Assist (Test Bolus).
- → Beat Sensor (all sequences).
- → Stimulation monitoring
- -> Complex Averaging
Additionally, the pulse sequence MR Fingerprinting (MRF) (K213805) is now available for the subject device MAGNETOM Cima.X with syngo MR XA61A.
The provided text is a 510(k) Summary for a medical device (MAGNETOM Cima.X) and outlines how the device, particularly its AI features, meets acceptance criteria through studies.
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are implied by the performance characteristics used to evaluate the AI features. The reported device performance is presented in terms of quality metrics and visual evaluations.
| Acceptance Criterion (Implied) | Reported Device Performance |
|---|---|
| Deep Resolve Boost (TSE, HASTE, EPI Diffusion) | |
| Image quality (e.g., aliasing artifacts, sharpness, denoising levels) | Characterized by: |
- Peak Signal-to-Noise Ratio (PSNR)
- Structural Similarity Index (SSIM)
- Evaluated by visual comparisons to assess aliasing artifacts, image sharpness, and denoising levels. |
| Deep Resolve Sharp | |
| Image quality (e.g., sharpness) | Characterized by: - Peak Signal-to-Noise Ratio (PSNR)
- Structural Similarity Index (SSIM)
- Perceptual loss
- Verified and validated by in-house tests, including visual rating and evaluation of image sharpness by intensity profile comparisons of reconstructions with and without Deep Resolve Sharp. |
2. Sample Sizes Used for the Test Set and Data Provenance
The document does not explicitly delineate a separate "test set" with a dedicated sample size after the training and validation phase for Deep Resolve Boost and Deep Resolve Sharp. Instead, it seems the "validation" mentioned in the context of training and validation data encompasses the evaluation of device performance.
-
Deep Resolve Boost:
- TSE: More than 25,000 slices (used for training and validation).
- HASTE: Pre-trained on TSE dataset and refined with more than 10,000 HASTE slices (used for training and validation).
- EPI Diffusion: More than 1,000,000 slices (used for training and validation).
- Data Provenance: Retrospectively created from acquired datasets. The document does not specify the country of origin.
-
Deep Resolve Sharp:
- Sample Size: More than 10,000 high-resolution 2D images (used for training and validation).
- Data Provenance: Retrospectively created from acquired datasets. The document does not specify the country of origin.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The document does not mention the use of experts to establish ground truth for the test set of the AI features. The "visual comparisons" and "visual rating" described are internal evaluations for feature performance but are not linked to expert-established ground truth for a formal test set described as such.
4. Adjudication Method for the Test Set
Not applicable, as no external expert-adjudicated test set is explicitly described for the AI features. The evaluations mentioned (visual comparisons, visual rating) appear to be internal.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No MRMC comparative effectiveness study is mentioned in the provided text for the AI features. The document focuses on the technical performance of the AI algorithms rather than their impact on human reader performance.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done
Yes, the performance evaluation for Deep Resolve Boost and Deep Resolve Sharp appears to be standalone algorithm performance. The metrics (PSNR, SSIM, perceptual loss) and visual comparisons/ratings are related to the image quality produced by the algorithm itself, without direct assessment of human-in-the-loop performance.
7. The Type of Ground Truth Used
- Deep Resolve Boost: The acquired datasets (MRI raw data or images) were considered the "ground truth" for training and validation. Input data for the AI was then retrospectively created from this ground truth by data manipulation and augmentation (discarding k-space lines, lowering SNR, mirroring k-space data) to simulate different acquisition conditions.
- Deep Resolve Sharp: The acquired datasets (high-resolution 2D images) were considered the "ground truth" for training and validation. Low-resolution input data for the AI was retrospectively created from this ground truth by cropping k-space data, so the high-resolution data served as the output/ground truth.
8. The Sample Size for the Training Set
The document combines training and validation data, so the sample sizes listed in point 2 apply:
- Deep Resolve Boost:
- TSE: More than 25,000 slices
- HASTE: More than 10,000 HASTE slices (refined)
- EPI Diffusion: More than 1,000,000 slices
- Deep Resolve Sharp: More than 10,000 high-resolution 2D images
9. How the Ground Truth for the Training Set Was Established
- Deep Resolve Boost: "The acquired datasets (as described above) represent the ground truth for the training and validation." This implies that the raw, original MRI data or images acquired under standard, full-sampling conditions were considered the reference. The AI was then trained to recover information from artificially degraded or undersampled versions of this ground truth.
- Deep Resolve Sharp: "The acquired datasets represent the ground truth for the training and validation." Similar to Deep Resolve Boost, the original high-resolution acquired 2D images were used as the ground truth. Low-resolution data was then derived from these high-resolution images to create the input for the AI, with the original high-resolution images serving as the target output (ground truth).
§ 892.1000 Magnetic resonance diagnostic device.
(a)
Identification. A magnetic resonance diagnostic device is intended for general diagnostic use to present images which reflect the spatial distribution and/or magnetic resonance spectra which reflect frequency and distribution of nuclei exhibiting nuclear magnetic resonance. Other physical parameters derived from the images and/or spectra may also be produced. The device includes hydrogen-1 (proton) imaging, sodium-23 imaging, hydrogen-1 spectroscopy, phosphorus-31 spectroscopy, and chemical shift imaging (preserving simultaneous frequency and spatial information).(b)
Classification. Class II (special controls). A magnetic resonance imaging disposable kit intended for use with a magnetic resonance diagnostic device only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.