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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.

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.

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