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510(k) Data Aggregation
(118 days)
Magnetic Resonance Imaging (MRI) is a noninvasive technique used for diagnostic imaging. MRI with its soft tissue contrast capability enables the healthcare professional to differentiate between various soft tissues, for example, fat, water, and muscle, but can also visualize bone structures.
Depending on the region of interest, contrast agents may be used.
The MR system may also be used for imaging during interventional procedures and radiation therapy planning.
The PET images and measures the distribution of PET radiopharmaceuticals in humans to aid the physician in determining various metabolic (molecular) and physiologic functions within the human body for evaluation of diseases and disorders such as, but not limited to, cardiovascular disease, neurological disorders, and cancer.
The integrated system utilizes the MRI for radiation-free attenuation correction maps for PET studies. The integrated system provides inherent anatomical reference for the fused MR and PET images due to precisely aligned MR and PET image coordinate systems.
BIOGRAPH One with software Syngo MR XB10 includes new and modified hardware and software compared to the predicate device, Biograph mMR with software syngo MR E11P-AP01. A high level summary of the new and modified hardware and software is provided below:
Hardware
New Hardware
- Gantry offset phantom
- SDB (Smart Distribution Box)
New Coils
- BM Contour XL Coil
- BM Head/Neck Pro PET-MR Coil
- BM Spine Pro PET-MR Coil
- Transfer of up-to-date RF coils from the reference device MAGNETOM Vida.
Modified Hardware
- Main components such as:
- Detector cassettes / DEA
- Phantom holder
- Gantry tube
- Backplane
- Magnet and cabling
- Gradient coil
- MaRS (measurement and reconstruction system)
- MI MARS
- PET electronics
- RF transmitter TBX3 3T (TX Box 3)
- Other components such as:
- Cover
- Filter plate
- Patient table
- RFCEL_TEMP
Modified Coils
- Body coil
- Transfer of up-to-date RF coils from the reference device MAGNETOM Vida with some improvements.
Software
New Features and Applications
- Fast Whole-Body workflows
- Fast Head workflow
- myExam PET-MR Assist
- CS-Vibe
- myExam Implant Suite
- DANTE blood suppression
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS without diffusion function
- BioMatrix Motion Sensor
- RF pulse optimization with VERSE
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- ASNR recommended protocols for imaging of ARIA
- Open Workflow
- Ultra HD-PET
- "MTC Mode"
- OpenRecon 2.0
- Deep Resolve Boost for TSE
- GRE_PC
- The following functions have been migrated for the subject device without modifications from MAGNETOM Skyra Fit and MAGNETOM Sola Fit:
- 3D Whole Heart
- Ghost reduction (Dual polarity Grappa (DPG))
- Fleet Reference Scan
- AutoMate Cardiac (Cardiac AI Scan Companion)
- Complex Averaging
- SPACE Improvement: high bandwidth IR pulse
- SPACE Improvement: increase gradient spoiling
- The following function has been migrated for the subject device without modifications from MAGNETOM Free.Max:
- myExam Autopilot Spine
- The following functions have been migrated for the subject device without modifications from MAGNETOM Sola:
- myExam Autopilot Brain
- myExam Autopilot Knee
- Transfer of further up-to-date SW functions from the reference devices.
New Software / Platform
- PET-Compatible Coil Setup
- Select&GO
- PET-MR components communication
Modified Features and Applications
- HASTE_CT
- FL3D_VIBE_AC
- PET Reconstruction
- Transfer of further up-to-date SW functions from the reference devices with some improvements.
Modified Software / Platform
- Several software functions have been improved. Which are:
- PET Group
- PET Viewing
- PET RetroRecon
- PET Status and Tune-up/QA
Other Modifications and / or Minor Changes
- Indications for use
- Contraindications
- SAR parameter
- Off-Center Planning Support
- Flip Angle Optimization (Lock TR and FA)
- Inline Image Filter
- Marketing bundle "myExam Companion"
- ID Gain
- Automatic System Shutdown (ASS) sensor (Smoke Detector)
- Patient data display (PDD)
The FDA 510(k) Clearance Letter for BIOGRAPH One refers to several AI/Deep Learning features. However, the provided document does not contain explicit acceptance criteria for these AI features in a table format, nor does it detail a comparative effectiveness study (MRMC study) for human readers. It primarily focuses on demonstrating non-inferiority to the predicate device through various non-clinical tests.
Below is an attempt to extract and synthesize the information based on the provided text, while acknowledging gaps in the information regarding specific acceptance criteria metrics and clinical studies.
Acceptance Criteria and Study Details for BIOGRAPH One AI Features
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state numerical acceptance criteria in a dedicated table format. Instead, it describes performance in terms of achieving "convergence of the training" and "improvements compared to conventional parallel imaging," or confirming "very similar metrics" to the predicate. The "acceptance criteria" are implied by these statements and the successful completion of the described tests.
| AI Feature | Implied Acceptance Criteria (Performance Goal) | Reported Device Performance |
|---|---|---|
| Deep Resolve Boost for FL3D_VIBE & SPACE | Convergence of training and improvement compared to conventional parallel imaging for SSIM, PSNR, and MSE; no negative impact on image quality. | Quantitative evaluations of SSIM, PSNR, and MSE metrics showed a convergence of the training and improvements compared to conventional parallel imaging. Inspection of test images did not reveal any negative impact to image quality. Function used for faster acquisition or improved image quality. |
| Deep Resolve Sharp for FL3D_VIBE & SPACE | Improvements across quality metrics (PSNR, SSIM, perceptual loss), increased edge sharpness, reduced Gibb's artifacts. | Characterized by several quality metrics (PSNR, SSIM, perceptual loss). Tests show increased edge sharpness and reduced Gibb's artifacts. |
| Deep Resolve Boost for TSE (First Mention) | Very similar metrics (PSNR, SSIM, LPIPS) to predicate/modified network, outperforming conventional GRAPPA. No negative visual impact. | Evaluation on test dataset confirmed very similar metrics (PSNR, SSIM, LPIPS) for the predicate and modified network, with both outperforming conventional GRAPPA. Visual evaluations confirmed no negative impact to image quality. Function used for faster acquisition or improved image quality. |
| Deep Resolve Boost for TSE (Second Mention) | Statistically significant reduction of banding artifacts, no significant changes in sharpness/detail, no difference in clinical suitability (radiologist evaluation). | Statistically significant reduction of banding artifacts with no significant changes in sharpness and detail visibility. Radiologist evaluation revealed no difference in suitability for clinical diagnostics between updated and cleared predicate network. |
2. Sample Sizes Used for Test Set and Data Provenance
The document primarily describes a validation dataset which serves as the "test set" for the AI models during development, and an additional "test dataset" for specific evaluations.
-
Deep Resolve Boost for FL3D_VIBE and SPACE:
- Test Set Description: The "collaboration partners (testing)" data is mentioned as the source for testing, implying an external, independent test set. No specific number for this test set is provided beyond the 1265 measurements for training/validation.
- Sample Size (Validation/Training): 27,679 3D patches from 1265 measurements.
- Data Provenance: "in-house measurements (training and validation) and collaboration partners (testing)." The country of origin is not specified but is likely Germany (Siemens Healthineers AG) and/or China (Siemens Shenzhen Magnetic Resonance LTD.) where the manufacturing is listed.
- Retrospective/Prospective: "Input data was retrospectively created from the ground truth by data manipulation and augmentation." This indicates retrospective data use.
-
Deep Resolve Sharp for FL3D_VIBE and SPACE:
- Test Set Description: The document states, "The high-resolution datasets were split to 70% training and 30% validation datasets before training to ensure independence of them." This implies the 30% validation dataset is used as the test set.
- Sample Size (Validation/Training): 27,679 3D patches from 1265 measurements (split into 70% training and 30% validation).
- Data Provenance: "in-house measurements (training and validation) and collaboration partners (testing)."
- Retrospective/Prospective: "Input data was retrospectively created from the ground truth by data manipulation." This indicates retrospective data use.
-
Deep Resolve Boost for TSE (First Mention - General Performance):
- Test Set Description: The "evaluation on the test dataset" is mentioned. The validation set is 30% of the 500 measurements.
- Sample Size (Validation/Training): Approximately 13,000 high resolution 3D patches from 500 measurements (split into 70% training and 30% validation).
- Data Provenance: "in-house measurements."
- Retrospective/Prospective: "Input data was retrospectively created from the ground truth by data manipulation." This indicates retrospective data use.
-
Deep Resolve Boost for TSE (Second Mention - Banding Artifacts):
- Test Set Description: "Additional test dataset for banding artifact reduction: more than 2000 slices." This dataset was acquired after the release of the predicate network.
- Sample Size: More than 2000 slices.
- Data Provenance: "in-house measurements and collaboration partners."
- Retrospective/Prospective: Not explicitly stated for this specific additional dataset, but the training/validation data for the predicate was retrospective.
3. Number of Experts and Qualifications for Ground Truth
-
Radiologist Evaluation for Deep Resolve Boost for TSE (Second Mention): The document mentions "the radiologist evaluation revealed no difference in suitability for clinical diagnostics."
- Number of Experts: Not specified (singular "radiologist" used, but typically multiple are implied for such evaluations).
- Qualifications: "Radiologist." No specific years of experience or subspecialty are mentioned.
-
Other features: For Deep Resolve Boost/Sharp for FL3D_VIBE and SPACE, and Deep Resolve Boost for TSE (first mention), the ground truth is derived directly from acquired image data (see section 7). No independent human expert ground truth establishment for these.
4. Adjudication Method (for Test Set)
-
Radiologist Evaluation for Deep Resolve Boost for TSE (Second Mention): The adjudication method is not specified in the document (e.g., 2+1, 3+1). It only states "the radiologist evaluation."
-
Other features: Adjudication methods are not applicable as human experts were not establishing ground truth for objective metrics.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Was an MRMC study done? No, the document does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance is compared. The evaluation for Deep Resolve Boost for TSE mentions "radiologist evaluation" but not in a comparative MRMC study context.
- Effect Size: Not applicable, as no MRMC study was performed.
6. Standalone (Algorithm Only) Performance
- Was standalone performance done? Yes, the performance testing for all Deep Resolve features (Boost and Sharp for FL3D_VIBE, SPACE, and TSE) was conducted "algorithm only" by evaluating metrics like PSNR, SSIM, MSE, and LPIPS, and then visual inspection/radiologist evaluation. These refer to the algorithm's direct output performance.
7. Type of Ground Truth Used
- Deep Resolve Boost for FL3D_VIBE and SPACE: "The acquired datasets (as described above) represent the ground truth for the training and validation."
- Deep Resolve Sharp for FL3D_VIBE and SPACE: "The acquired datasets represent the ground truth for the training and validation." Input data was manipulated (cropped k-space) to create low-resolution input and high-resolution output/ground truth from the same dataset.
- Deep Resolve Boost for TSE (First Mention): "The acquired datasets represent the ground truth for the training and validation." Input data was manipulated (cropped k-space) to create low-resolution input and high-resolution output/ground truth from the same dataset.
- Deep Resolve Boost for TSE (Second Mention): "The acquired training/validation datasets... represent the ground truth for the training and validation." Input data was manipulated by undersampling k-space, adding noise, and mirroring k-space.
- Summary: The ground truth for all AI features was derived from acquired, high-resolution original image data (retrospectively manipulated to simulate inputs). For Deep Resolve Boost for TSE (second mention), there was also an implicit "expert consensus" or "expert reading" component for the "radiologist evaluation" regarding clinical suitability.
8. Sample Size for the Training Set
- Deep Resolve Boost for FL3D_VIBE and SPACE: 81% of 1265 measurements (for 27,679 3D patches).
- Deep Resolve Sharp for FL3D_VIBE and SPACE: 70% of 1265 measurements (for 27,679 3D patches).
- Deep Resolve Boost for TSE (First Mention): 70% of 500 measurements (for approx. 13,000 high resolution 3D patches).
- Deep Resolve Boost for TSE (Second Mention): More than 23,250 slices (93% of the combined training/validation dataset from K213693).
9. How the Ground Truth for the Training Set Was Established
- Deep Resolve Boost for FL3D_VIBE and SPACE: The "acquired datasets" represent the ground truth. "Input data was retrospectively created from the ground truth by data manipulation and augmentation. This process includes further undersampling of the data by discarding k-space lines as well as creating sub-volumes of the acquired data."
- Deep Resolve Sharp for FL3D_VIBE and SPACE: The "acquired datasets represent the ground truth." "Input data was retrospectively created from the ground truth by data manipulation. k-space data has been cropped such that only the center part of the data was used as input. With this method corresponding low-resolution data as input and high-resolution data as output / ground truth were created for training and validation."
- Deep Resolve Boost for TSE (First Mention): Similar to Deep Resolve Sharp for FL3D_VIBE and SPACE: "The acquired datasets represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation. k-space data has been cropped such that only the center part of the data was used as input. With this method corresponding low-resolution data as input and high-resolution data as output / ground truth were created for training and validation."
- Deep Resolve Boost for TSE (Second Mention): "The acquired training/validation datasets... represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation and augmentation. This process includes further undersampling of the data by discarding k-space lines, lowering of the SNR level by addition of noise and mirroring of k-space data."
In summary, for all AI features, the ground truth for training was established by using high-quality, originally acquired MRI data that was then retrospectively manipulated (e.g., undersampled, cropped, noise added) to create synthetic "lower quality" input data for the AI model to learn from, with the original high-quality data serving as the target output or ground truth.
Ask a specific question about this device
(105 days)
Intended Use / Indications for Use
Indications for Use for MAGNETOM Vida, MAGNETOM Lumina, MAGNETOM Vida Fit, MAGNETOM Sola, MAGNETOM Altea, MAGNETOM Sola Fit, MAGNETOM Viato.Mobile:
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.
Indications for Use for MAGNETOM Flow.Elite, MAGNETOM Flow.Neo, MAGNETOM Flow.Rise:
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, depending on optional local coils that have been configured with the system, 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 Vida with software Syngo MR XB10, 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 XA60A (K231560).
A high-level summary of the new and modified hardware and software is provided below:
New Hardware:
- myExam 3D Camera
- BM Contour XL Coil
Modified Hardware:
- RF Transmitter TBX3 3T (TX Box 3)
- MaRS (Measurement and reconstruction system)
Software
New Features and Applications:
- Brachytherapy Support for use with MR conditional applicators
- CS Vibe
- myExam Implant Suite
- DANTE blood suppression
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS for BLADE without diffusion function
- BioMatrix Motion Sensor
- RF pulse optimization with VERSE
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- ASNR recommended protocols for imaging of ARIA
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- 3D Whole Heart
- Ghost reduction (Dual polarity Grappa (DPG))
- Fleet Reference Scan
- AutoMate Cardiac (Cardiac AI Scan Companion)
- Complex Averaging
- myExam Autopilot Spine
- myExam Autopilot Brain and myExam Autopilot Knee
- Open Workflow
Modified features and applications:
- GRE_PC
- myExam RT Assist workflow improvements
- Open Recon 2.0
- Deep Resolve Boost for TSE
- "MTC Mode" for SPACE
- SPACE Improvement: high bandwidth IR pulse
- SPACE Improvement: increase gradient spoiling
The subject device, MAGNETOM Lumina with software Syngo MR XB10, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Lumina with syngo MR XA60A (K231560). A high-level summary of the new and modified hardware and software is provided below:
New Hardware:
- myExam 3D Camera
- BM Contour XL Coil
Modified Hardware:
- RF Transmitter TBX3 3T (TX Box 3)
- MaRS (Measurement and reconstruction system)
Software
New Features and Applications:
- CS Vibe
- myExam Implant Suite
- DANTE blood suppression
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS for BLADE without diffusion function
- BioMatrix Motion Sensor
- RF pulse optimization with VERSE
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- Ghost reduction (Dual polarity Grappa (DPG))
- Fleet Reference Scan
- AutoMate Cardiac (Cardiac AI Scan Companion)
- Complex Averaging
- myExam Autopilot Spine
- myExam Autopilot Brain and myExam Autopilot Knee
- Compressed Sensing Cardiac Cine
- Open Workflow
Modified Features and Applications:
- GRE_PC
- Open Recon 2.0
- Deep Resolve Boost for TSE
- "MTC Mode" for SPACE
- SPACE Improvement: high bandwidth IR pulse
- SPACE Improvement: increase gradient spoiling
The subject device, MAGNETOM Lumina with software Syngo MR XB10, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Lumina with syngo MR XA60A (K231560). A high-level summary of the new and modified hardware and software is provided below:
New Hardware:
- myExam 3D Camera
- BM Contour XL Coil
Modified Hardware:
- RF Transmitter TBX3 3T (TX Box 3)
- MaRS (Measurement and reconstruction system)
Software
New Features and Applications:
- CS Vibe
- myExam Implant Suite
- DANTE blood suppression
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS for BLADE without diffusion function
- BioMatrix Motion Sensor
- RF pulse optimization with VERSE
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- Ghost reduction (Dual polarity Grappa (DPG))
- Fleet Reference Scan
- AutoMate Cardiac (Cardiac AI Scan Companion)
- Complex Averaging
- myExam Autopilot Spine
- myExam Autopilot Brain and myExam Autopilot Knee
- Compressed Sensing Cardiac Cine
- Open Workflow
Modified Features and Applications:
- GRE_PC
- Open Recon 2.0
- Deep Resolve Boost for TSE
- "MTC Mode" for SPACE
- SPACE Improvement: high bandwidth IR pulse
- SPACE Improvement: increase gradient spoiling
The subject device, MAGNETOM Vida Fit with software Syngo MR XB10, 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 XA60A (K231560).
A high-level summary of the new and modified hardware and software is provided below:
New Hardware:
- myExam 3D Camera
- Beat Sensor
- BM Contour XL Coil
Modified Hardware:
- RF Transmitter TBX3 3T (TX Box 3)
- MaRS (Measurement and reconstruction system)
- Host computers
Software
New Features and Applications:
- Brachytherapy Support for use with MR conditional applicators
- CS Vibe
- myExam Implant Suite
- DANTE blood suppression
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS for BLADE without diffusion function
- BioMatrix Motion Sensor
- RF pulse optimization with VERSE
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- ASNR recommended protocols for imaging of ARIA
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- GRE_PC
- Open Recon 2.0
- 3D Whole Heart
- Ghost reduction (Dual polarity Grappa (DPG))
- Fleet Reference Scan
- AutoMate Cardiac (Cardiac AI Scan Companion)
- myExam Autopilot Spine
- myExam Autopilot Brain and myExam Autopilot Knee
- Deep Resolve for EPI
- Deep Resolve for HASTE
- Physiologging
- Complex Averaging
- Open Workflow
Modified features and applications:
- myExam RT Assist workflow improvements
- Deep Resolve Boost for TSE
- "MTC Mode" for SPACE
- myExam Angio Advanced Assist (Test Bolus)
- SPACE Improvement: high bandwidth IR pulse
- SPACE Improvement: increase gradient spoiling
The subject device, MAGNETOM Sola with software Syngo MR XB10, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Sola with syngo MR XA61A (K232535).
A high-level summary of the new and modified hardware and software is provided below:
New Hardware:
- BM Contour XL Coil
Modified Hardware:
- MaRS (Measurement and reconstruction system)
Software
New Features and Applications:
- Brachytherapy Support for use with MR conditional applicators
- CS Vibe
- DANTE blood suppression
- BioMatrix Motion Sensor
- SPAIR FatSat Improvements: SPAIR "Abdomen&Pelvis" mode and SPAIR Breast mode
- RF pulse optimization with VERSE
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- ASNR recommended protocols for imaging of ARIA
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- 3D Whole Heart
- AutoMate Cardiac (Cardiac AI Scan Companion)
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS for BLADE without diffusion function
- Ghost reduction (Dual polarity Grappa (DPG))
- Fleet Reference Scan
- Deep Resolve Swift Brain
- myExam Autopilot Spine
- Open Workflow
- Complex Averaging
- Open Workflow
Modified features and applications:
- myExam RT Assist workflow improvements
- Deep Resolve Boost for TSE
- "MTC Mode" for SPACE
- myExam Angio Advanced Assist (Test Bolus)
- SPACE Improvement: high bandwidth IR pulse
- SPACE Improvement: increase gradient spoiling
The subject device, MAGNETOM Sola with software Syngo MR XB10, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Sola with syngo MR XA61A (K232535).
A high-level summary of the new and modified hardware and software is provided below:
New Hardware:
- BM Contour XL Coil
Modified Hardware:
- MaRS (Measurement and reconstruction system)
Software
New Features and Applications:
- Brachytherapy Support for use with MR conditional applicators
- CS Vibe
- DANTE blood suppression
- BioMatrix Motion Sensor
- SPAIR FatSat Improvements: SPAIR "Abdomen&Pelvis" mode and SPAIR Breast mode
- RF pulse optimization with VERSE
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- ASNR recommended protocols for imaging of ARIA
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- 3D Whole Heart
- AutoMate Cardiac (Cardiac AI Scan Companion)
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS for BLADE without diffusion function
- Ghost reduction (Dual polarity Grappa (DPG))
- Fleet Reference Scan
- Deep Resolve Swift Brain
- myExam Autopilot Spine
- Open Workflow
Modified features and applications:
- myExam Implant Suite
- GRE_PC
- myExam RT Assist workflow improvements
- Open Recon 2.0
- Deep Resolve Boost for TSE
- "MTC Mode" for SPACE
- SPACE Improvement: high bandwidth IR pulse
- SPACE Improvement: increase gradient spoiling
The subject device, MAGNETOM Altea with software Syngo MR XB10, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Altea with syngo MR XA61A (K232535).
A high-level summary of the new and modified hardware and software is provided below:
New Hardware:
- BM Contour XL Coil
Modified Hardware:
- MaRS (Measurement and reconstruction system)
Software
New Features and Applications:
- CS Vibe
- DANTE blood suppression
- BioMatrix Motion Sensor
- SPAIR FatSat Improvements: SPAIR "Abdomen&Pelvis" mode and SPAIR Breast mode
- RF pulse optimization with VERSE
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- AutoMate Cardiac (Cardiac AI Scan Companion)
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS for BLADE without diffusion function
- Ghost reduction (Dual polarity Grappa (DPG))
- Fleet Reference Scan
- Deep Resolve Swift Brain
- myExam Autopilot Spine
- Compressed Sensing Cardiac Cine
- Open Workflow
Modified features and applications:
- myExam Implant Suite
- GRE_PC
- myExam RT Assist workflow improvements
- Open Recon 2.0
- Deep Resolve Boost for TSE
- "MTC Mode" for SPACE
- SPACE Improvement: high bandwidth IR pulse
- SPACE Improvement: increase gradient spoiling
The subject device, MAGNETOM Sola Fit with software Syngo MR XB10, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Sola Fit with syngo MR XA70A (K250443).
A high-level summary of the new and modified hardware and software is provided below:
New Hardware:
- BM Contour XL Coil
Modified Hardware:
- MaRS (Measurement and reconstruction system)
- Host computers
Software
New Features and Applications:
- Brachytherapy Support for use with MR conditional applicators
- CS Vibe
- DANTE blood suppression
- BioMatrix Motion Sensor
- SPAIR FatSat Improvements: SPAIR "Abdomen&Pelvis" mode and SPAIR Breast mode
- RF pulse optimization with VERSE
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- ASNR recommended protocols for imaging of ARIA
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- myExam Implant Suite
- GRE_PC
- Open Recon 2.0
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS for BLADE without diffusion function
- Deep Resolve Swift Brain
- myExam Autopilot Spine
- Open Workflow
Modified features and applications:
- myExam RT Assist workflow improvements
- myExam Implant Suite
- Deep Resolve Boost for TSE
- "MTC Mode" for SPACE
- SPACE Improvement: high bandwidth IR pulse
- SPACE Improvement: increase gradient spoiling
The subject device, MAGNETOM Sola Fit with software Syngo MR XB10, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Sola Fit with syngo MR XA70A (K250443).
A high-level summary of the new and modified hardware and software is provided below:
New Hardware:
- BM Contour XL Coil
Modified Hardware:
- MaRS (Measurement and reconstruction system)
- Host computers
Software
New Features and Applications:
- Brachytherapy Support for use with MR conditional applicators
- CS Vibe
- DANTE blood suppression
- BioMatrix Motion Sensor
- SPAIR FatSat Improvements: SPAIR "Abdomen&Pelvis" mode and SPAIR Breast mode
- RF pulse optimization with VERSE
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- ASNR recommended protocols for imaging of ARIA
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- myExam Implant Suite
- GRE_PC
- Open Recon 2.0
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS for BLADE without diffusion function
- Deep Resolve Swift Brain
- myExam Autopilot Spine
- Open Workflow
Modified features and applications:
- myExam RT Assist workflow improvements
- myExam Implant Suite
- Deep Resolve Boost for TSE
- "MTC Mode" for SPACE
The subject device, MAGNETOM Viato.Mobile with software Syngo MR XB10, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Viato.Mobile with syngo MR XA70A (K250443).
A high-level summary of the new and modified hardware and software is provided below:
New Hardware:
- BM Contour XL Coil
Modified Hardware:
- MaRS (Measurement and reconstruction system)
- Host computers
Software
New Features and Applications:
- CS Vibe
- DANTE blood suppression
- BioMatrix Motion Sensor
- SPAIR FatSat Improvements: SPAIR "Abdomen&Pelvis" mode and SPAIR Breast mode
- RF pulse optimization with VERSE
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- ASNR recommended protocols for imaging of ARIA
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- myExam Implant Suite
- GRE_PC
- Open Recon 2.0
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS for BLADE without diffusion function
- Deep Resolve Swift Brain
- myExam Autopilot Spine
- Open Workflow
Modified features and applications:
- myExam Implant Suite
- Deep Resolve Boost for TSE
- "MTC Mode" for SPACE
With the subject software version, Syngo MR XB10, we are also introducing the following new 1.5T devices, which are part of our MAGNETOM Flow. Platform:
MAGNETOM Flow.Elite
MAGNETOM Flow.Neo
MAGNETOM Flow.Rise
The subject device, MAGNETOM Flow.Elite, MAGNETOM Flow.Neo and MAGNETOM Flow.Rise with software Syngo MR XB10, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Sola with syngo MR XA61A (K232535).
A high-level summary of the new and modified hardware and software is provided below:
New Hardware:
- Magnet
- MREF (Magnet Refrigerator)
- Gradient system
- Gradient Coil
- RF System
- System Cover
- Patient Table
- MaRS (Measurement and Reconstruction System)
- Select&GO Display (TPAN_3G) and Control Panel (CPAN_2G)
- Body Coil
- Head/Neck Coil
- BM Head/Neck Coil (with ComfortSound)
- BM Contour S Coil
- BM Contour M Coil
- BM Contour L Coil
- BM Contour XL Coil
- Foot/Ankle Coil
- BM Spine Coil
- iTx Extremity 18 Flare
- Multi-Index MR-RT Positioning (a part of "RT Pro Edition" marketing bundle) (not available for MAGNETOM Flow.Rise)
Modified Hardware:
- Gradient Power Amplifier (GPA)
- SAR Monitoring
- In-Vivo Shim
Software
New Features and Applications:
- CS Vibe
- BioMatrix Motion Sensor
- SPAIR FatSat Improvements: SPAIR "Abdomen&Pelvis" mode and SPAIR Breast mode
- Deep Resolve Boost for FL3D_VIBE and SPACE
- Deep Resolve Sharp for FL3D_VIBE and SPACE
- Preview functionality for Deep Resolve Boost
- EP2D_FID_PHS
- EP_SEG_FID_PHS
- AutoMate Cardiac (Cardiac AI Scan Companion)
- DANTE blood suppression
- SMS Averaging for TSE
- SMS Averaging for TSE_DIXON
- SMS for BLADE without diffusion function
- Ghost reduction (Dual polarity Grappa (DPG))
- Fleet Reference Scan
- Deep Resolve Swift Brain
- Quick Protocols
- myExam Autopilot Spine
- Open Workflow
Modified features and applications:
- myExam Implant Suite
- GRE_PC
- myExam RT Assist workflow improvements (not available for MAGNETOM Flow.Rise)
- Open Recon 2.0
- Deep Resolve Boost for TSE
- "MTC Mode" for SPACE
- SPACE Improvement: high bandwidth IR pulse
- SPACE Improvement: increase gradient spoiling
New (general) Software / Platform / Workflow:
- Select&GO extension (coil-based Iso Centering, Patient Registration at the touch display, Start Scan at the touch display)
- New Startup-Timer
- myExam RT Assist (not available for MAGNETOM Flow.Rise)
- myExam Brain RT-Autopilot (not available for MAGNETOM Flow.Rise)
- Eco Power Mode Pro
Modified (general) Software / Platform:
- Improved Gradient ECO Mode Settings
Furthermore, the following minor updates and changes were conducted for the subject devices MAGNETOM Vida, MAGNETOM Lumina, MAGNETOM Vida Fit, MAGNETOM Sola, MAGNETOM Altea:
- Off-Center Planning Support
- Flip Angle Optimization (Lock TR and FA)
- Inline Image Filter
- Automatic System Shutdown (ASS) sensor (Smoke Detector)
- ID Gain (re-naming)
- Select&Go Display (Touch Display (TPAN))
- Marketing bundle "myExam Companion"
The following minor updates and changes were conducted for the subject devices MAGNETOM Sola Fit and MAGNETOM Viato.Mobile:
- Off-Center Planning Support
- Automatic System Shutdown (ASS) sensor (Smoke Detector)
- ID Gain (re-naming)
- Select&Go Display (Touch Display (TPAN))
- Marketing bundle "myExam Companion"
The following minor updates and changes were conducted for the subject devices MAGNETOM Flow.Elite, MAGNETOM Flow.Neo, MAGNETOM Flow.Rise:
- Off-Center Planning Support
- Flip Angle Optimization (Lock TR and FA)
- Inline Image Filter
- Automatic System Shutdown (ASS) sensor (Smoke Detector)
- ID Gain (re-naming)
- Marketing bundle "myExam Companion"
- Marketing Bundle "RT Pro Edition"(not available for MAGNETOM Flow.Rise)
This FDA 510(k) clearance letter pertains to several MAGNETOM MRI systems with software Syngo MR XB10. The document primarily focuses on demonstrating substantial equivalence to predicate devices through non-clinical testing of new and modified hardware and software features, particularly those involving Artificial Intelligence (AI) such as "Deep Resolve" functionalities.
Here's an analysis of the acceptance criteria and the studies that prove the devices meet them, specifically for the AI features:
1. Table of Acceptance Criteria and Reported Device Performance for AI Features
The document does not explicitly state "acceptance criteria" for the AI features in a numerical format that would typically be seen for a device's performance metrics (e.g., minimum sensitivity, specificity). Instead, the acceptance criteria are implicitly defined by the evaluation methods and the "Test result summary" for each Deep Resolve feature, which aim to demonstrate equivalent or improved image quality compared to conventional methods.
| AI Feature | Acceptance Criteria (Implied) | Reported Device Performance | Comments |
|---|---|---|---|
| Deep Resolve Swift Brain | - Quantitative quality metrics (PSNR, SSIM, NMSE) to demonstrate network impact.- Visual inspection to ensure no undetected artifacts.- Evaluation in clinical settings with collaboration partners. | - "Impact of the network has been characterized by several quality metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and normalized mean squared error (NMSE)."- "Images were inspected visually to ensure that potential artefacts are detected that are not well captured by the metrics."- "Work-in-progress packages of the network were delivered and evaluated in clinical settings with collaboration partners." | The results indicate successful performance in meeting these criteria, suggesting the AI feature performs as intended without negative impact on image quality and with acceptable quantitative metrics. |
| Deep Resolve Boost for FL3D_VIBE and Deep Resolve Boost for SPACE | - Quantitative evaluations (SSIM, PSNR, MSE) showing convergence of training and improvements over conventional parallel imaging.- Visual inspection to confirm no negative impact on image quality.- The function should allow for faster acquisition or improved image quality. | - "Quantitative evaluations of structural similarity index (SSIM), peak signal-to-noise ratio (PSNR) and mean squared error (MSE) metrics showed a convergence of the training and improvements compared to conventional parallel imaging."- "An inspection of the test images did not reveal any negative impact to the image quality."- "The function has been used either to acquire images faster or to improve image quality." | The results indicate successful performance, demonstrating quantitative improvements and confirming user benefit (faster acquisition or improved image quality) without negative visual impact. |
| Deep Resolve Sharp for FL3D_VIBE and Deep Resolve Sharp for SPACE | - Quantitative quality metrics (PSNR, SSIM, perceptual loss).- Rating and evaluation of image sharpness by intensity profile comparisons.- Demonstration of increased edge sharpness and reduced Gibb's artifacts. | - "The impact of the Deep Resolve Sharp network has been characterized by several quality metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and perceptual loss."- "The tests include rating and an evaluation of image sharpness by intensity profile comparisons of reconstruction with and without Deep Resolve Sharp. Both tests show increased edge sharpness and reduced Gibb's artifacts." | The results directly confirm improved image sharpness and reduced artifacts, meeting the implied performance criteria. |
| Deep Resolve Boost for TSE | - Similar metrics (PSNR, SSIM, LPIPS) to predicate (cleared) network, both outperforming conventional GRAPPA.- Statistically significant reduction of banding artifacts.- No significant changes in sharpness and detail visibility.- Radiologist evaluation confirming no difference in suitability for clinical diagnostics. | - "The evaluation on the test dataset confirmed very similar metrics in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and learned perceptual image patch similarity metrics (LPIPS) for the predicate and the modified network with both outperforming conventional GRAPPA as the reference."- "Visual evaluations confirmed statistically significant reduction of banding artifacts with no significant changes in sharpness and detail visibility."- "In addition, the radiologist evaluation revealed no difference in suitability for clinical diagnostics between updated and cleared predicate network." | This AI feature directly demonstrates equivalent or improved performance compared to the predicate, with specific mention of "radiologist evaluation" ensuring clinical suitability. |
2. Sample Size Used for the Test Set and Data Provenance
Since the document distinguishes between training, validation, and testing datasets, the "test set" here refers to the data used for final evaluation of the AI model's performance.
-
Deep Resolve Swift Brain:
- Test Set Sample Size: The document lists "Validation: 3,616 slices (1.5T validation); 6,048 slices (3T validation)" as part of the split. It also mentions "work-in-progress packages of the network were delivered and evaluated in clinical settings with collaboration partners," implying additional testing, but a specific numerical sample size for this external validation is not provided in detail. However, the initial splits serve as the primary "test set" for performance metrics mentioned.
- Data Provenance: "in-house measurement," implying retrospective data collected at Siemens' facilities. The document notes that "attributes like gender, age and ethnicity are not relevant to the training data" due to network architecture, but no specific country of origin is stated beyond "in-house."
-
Deep Resolve Boost for FL3D_VIBE and Deep Resolve Boost for SPACE:
- Test Set Sample Size: The document states 19% of 1265 measurements for validation. It also explicitly mentions "collaboration partners (testing)" indicating an external test set, but a specific numerical breakdown for this is not provided.
- Data Provenance: "in-house measurements (training and validation) and collaboration partners (testing)." This suggests a mix of retrospective data potentially from various countries where Siemens has collaboration, though specific locations are not listed.
-
Deep Resolve Sharp for FL3D_VIBE and Deep Resolve Sharp for SPACE:
- Test Set Sample Size: 30% of the 500 measurements are listed for validation, which serves as a test set. This equates to 150 measurements.
- Data Provenance: "in-house measurements," implying retrospective data from Siemens' research facilities. Specific country not mentioned.
-
Deep Resolve Boost for TSE:
- Test Set Sample Size: "Additional test dataset for banding artifact reduction: more than 2000 slices."
- Data Provenance: "in-house measurements and collaboration partners" for training/validation. The "additional test dataset for banding artifact reduction" likely follows the same provenance. Retrospective data.
3. Number of Experts Used and Qualifications for Ground Truth
The document does not explicitly state the number of experts used to establish ground truth or their specific qualifications (e.g., "radiologist with 10 years of experience") for any of the Deep Resolve features.
However, for Deep Resolve Boost for TSE, it mentions:
- "Visual evaluations confirmed statistically significant reduction of banding artifacts... "
- "In addition, the radiologist evaluation revealed no difference in suitability for clinical diagnostics..."
This indicates that radiologists were involved in the evaluation of the Deep Resolve Boost for TSE feature, presumably as experts to establish the clinical suitability. The exact number and their detailed qualifications are not provided. For other features, the ground truth is primarily based on the acquired raw data or manipulated versions of it, without explicit mention of expert review in the ground truth establishment process.
4. Adjudication Method (for the test set)
The document does not specify an adjudication method like "2+1" or "3+1" for establishing ground truth or evaluating the test set for any of the AI features. The ground truth for training and validation is derived from the "acquired datasets" which are considered the ground truth due to data manipulation and augmentation from these high-quality source images. For Deep Resolve Boost for TSE, a "radiologist evaluation" is mentioned, implying expert review without detailing a specific adjudication protocol.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to measure the improvement of human readers with AI assistance versus without AI assistance. The evaluations focus on the standalone performance of the AI algorithms in improving image quality metrics and, in one instance (Deep Resolve Boost for TSE, radiologist evaluation), the suitability for clinical diagnostics, rather than the impact on human reader performance.
6. Standalone (Algorithm Only) Performance
Yes, standalone (algorithm only) performance was done. The descriptions for each Deep Resolve feature focus entirely on the algorithm's performance in terms of quantitative image quality metrics (PSNR, SSIM, NMSE, MSE, LPIPS), visual inspection for artifacts, and improvements over conventional techniques. There is no mention of a "human-in-the-loop" component in the described performance evaluations for these AI features, except for the "radiologist evaluation" for Deep Resolve Boost for TSE which assessed clinical suitability of the output images, not reader performance with the AI.
7. Type of Ground Truth Used
-
For Deep Resolve Swift Brain, Deep Resolve Boost for FL3D_VIBE & SPACE, and Deep Resolve Sharp for FL3D_VIBE & SPACE:
- The ground truth used was the acquired datasets (raw MRI data). The input data for the AI models was then "retrospectively created from the ground truth by data manipulation and augmentation" (e.g., undersampling k-space, adding noise, cropping, creating sub-volumes, cropping k-space to simulate low-resolution input from high-resolution output). This means the AI models were trained to learn the mapping from manipulated (e.g., noisy, low-resolution, undersampled) inputs to the original, high-quality acquired image data.
-
For Deep Resolve Boost for TSE:
- Similar to above, the "acquired training/validation datasets" were considered the ground truth. Input data was generated by "data manipulation and augmentation" (e.g., discarding k-space lines, lowering SNR, mirroring k-space data).
In essence, the AI models are trained to restore or enhance images to resemble the high-quality, fully acquired MRI data that serves as the reference ground truth.
8. Sample Size for the Training Set
- Deep Resolve Swift Brain: 20,076 slices
- Deep Resolve Boost for FL3D_VIBE and Deep Resolve Boost for SPACE: 81% of 1265 measurements. (This equates to approximately 1024 measurements).
- Deep Resolve Sharp for FL3D_VIBE and Deep Resolve Sharp for SPACE: 70% of 500 measurements. (This equates to 350 measurements).
- Deep Resolve Boost for TSE: More than 23,250 slices (93% of the total dataset).
9. How the Ground Truth for the Training Set Was Established
For all Deep Resolve features, the ground truth for the training set was established from acquired MRI datasets (either "in-house measurements" or from "collaboration partners"). These acquired datasets are implicitly considered the "true" or "high-quality" images. The AI models are designed to process inputs that mimic suboptimal acquisition conditions (e.g., undersampled k-space, lower SNR, lower resolution) and generate outputs that match these high-quality acquired images, which serve as the ground truth for learning. The process involved:
- Retrospective creation: Input data was created retrospectively from the acquired ground truth data.
- Data manipulation and augmentation: This involved techniques such as:
- Discarding k-space lines (undersampling).
- Lowering the SNR level by adding Gaussian noise to k-space data.
- Uniformly-random cropping of training data.
- Creating sub-volumes of acquired data.
- Cropping k-space to generate low-resolution inputs corresponding to high-resolution ground truth.
- Mirroring of k-space data.
This approach demonstrates an unsupervised or self-supervised learning paradigm where the ground truth is derived directly from the complete and high-fidelity raw data, and the AI is trained to reconstruct or enhance images from degraded inputs to match this ideal ground truth.
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