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The Ascent3T Neonatal Magnetic Resonance Imaging System (Ascent3T) is a whole-body magnetic resonance scanner designed for neonates and infants. The system can produce cross-sectional images of the internal structure of the head, body or extremities in any orientation.

Images produced by the Ascent3T show the spatial distribution of protons exhibiting magnetic resonance. Images produced by the Ascent3T, when interpreted by a trained physician, may provide information useful in diagnosis.

The Ascent3T Neonatal Magnetic Resonance Imaging System is suitable for neonates and infants weighing up to 9kg (19.8 lbs).

The Ascent3T Neonatal Magnetic Resonance Imaging System (Ascent3T) is a high-field magnetic resonance imaging system, appropriately sized and optimized for the neonate and infant population, with a format that allows siting near point of care. The Ascent3T presents a solution for the technical limitations associated with using an adult-size MRI system and provides clinicians with an improved ability to visualize and diagnose disease in the neonatal patient population.

The Ascent3T is equipped with a small format superconducting magnet designed for neonate applications. The system is designed to operate at 3.0 Tesla and achieves a high level of homogeneity over a 24cm diameter spherical volume using passive shims. The magnet requires a minimal amount of helium and no quench pipe. These features, in combination with the size and weight of the magnet, support near-patient siting.

The Ascent3T patient table is detachable and can serve as a patient transport device. The patient table includes a tabletop cradle with features for securing the patient during scanning. The patient table is mobile, providing flexibility in workflow based on institutional needs and preferences.

The Ascent3T contains a menu of pulse sequences intended to provide the user with a variety of sequences useful for producing images for diagnostic purposes.

Key Features of the Ascent3T:

• 3T superconducting magnet with 25cm patient bore.
• Minimal helium capacity with no quench pipe required.
• Gradient system: 80 mT/m maximum amplitude per axis, 300 mT/m/ms slew rate per axis.
• Real-time SAR monitoring and alerts with Normal and First-Level Controlled Operating Modes.
• Capable of producing images in axial, sagittal, coronal, and oblique orientations.
• Accommodates neonates and infants weighing up to 9 kg (19.8 lbs).
• Detachable, mobile patient table with built-in safety features.

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The 3.0T AIR™ 32CH HNA is a receive-only RF Coil designed for use with GE HealthCare 3.0T MRI systems. The coil is indicated for high-resolution magnetic resonance imaging (MRI) of the head and brain. When used with a Posterior Array in the MRI System patient table it also includes neck, cervical spine, neurovascular structures, upper thoracic spine, and brachial plexus imaging. The nucleus detected is hydrogen.

The 3.0T AIR 32CH HNA is a receive-only radio frequency coil engineered to deliver optimal signal-to-noise ratio, uniform anatomical coverage, and high acceleration capabilities, including multiband imaging. It is intended for high-resolution magnetic resonance imaging (MRI) of the head and brain. When used in conjunction with the Posterior Array in the MRI system's patient table, it also supports imaging of the neck, cervical spine, neurovascular structures, upper thoracic spine, and brachial plexus. The nucleus detected is hydrogen. This coil is compatible with GE HealthCare 3.0T MRI systems.

The 3.0T AIR 32CH HNA is designed to be used by a Registered MRI Technologist in a hospital or clinical setting. The Registered MRI Technologist will operate the scanner from the control room. If the patient is claustrophobic another MRI Technologist or clinical staff member may stay in the magnet room with the patient.

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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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The Philips Neurovascular MR Coil is intended to be used in conjunction with a Philips 1.5T or 3T Magnetic Resonance Scanner to produce diagnostic images of the anatomy of interest that can be interpreted by a trained physician.

A general description of the subject devices, which are part of the Philips Neurovascular MR Coil Family is given below:

The dS Base 1.5T and dS Base 3.0T are lightweight, 8-channel, phased-array coil that are designed to be used independently or in combination with the dS Head or dS HeadNeck coil. The dS Base coil also can be used for extremities and pediatric examinations or can be combined with the dS Posterior coil which is integrated in the patient support of the compatible Magnetic Resonance (MR) Systems for total spine imaging. The dS Base coil can stay on the table for most examinations without exchanging coils, thereby, improving workflow. These coils are connected to the flex connect socket of the compatible MR System.

The dS Head 1.5T and dS Head 3.0T are 7-channel receive only coils, which are designed to be used in combination with the dS Base (bottom) coil to create a 15-channel coil with or without the dS Posterior coil which is integrated in the patient support of the compatible MR Systems for total spine imaging. The coil is most frequently used for imaging the head/brain and its vasculature. The coil can also be used for extremities and pediatric examinations. These coils are available in both 1.5T and 3.0T and are connected to the flex connect socket of the compatible MR System.

The dS HeadNeck 1.5T and dS HeadNeck 3.0T are 8-channel receive only coils, which are designed to be used in combination with the dS Base (bottom) coil to create a 16-channel coil with or without the dS Posterior coil which is integrated in the patient support of the compatible MR Systems for total spine imaging. The coil is used for brain, head, neck, cervical spine, and neurovascular imaging. The coil also can be used for extremities and pediatric examinations. These coils are available in both 1.5T and 3.0T and are connected to the flex connect socket of the compatible MR System

The dS HeadNeck coil can be used either alone or in combination with the dS Anterior coil and dS Posterior coil. When used in combination with the dS Anterior coil and dS Posterior coil for Whole Body exams this is called a 'Whole Body solution.

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AIR Recon DL is a deep learning based reconstruction technique that is available for use on GE HealthCare 1.5T, 3.0T, and 7.0T MR systems. AIR Recon DL reduces noise and ringing (truncation artifacts) in MR images, which can be used to reduce scan time and improve image quality. AIR Recon DL is intended for use with all anatomies, and for patients of all ages. Depending on the anatomy of interest being imaged, contrast agents may be used.

AIR Recon DL is a software feature intended for use with GE HealthCare MR systems. It is a deep learning-based reconstruction technique that removes noise and ringing (truncation) artifacts from MR images. AIR Recon DL is an optional feature that is integrated into the MR system software and activated through purchasable software option keys. AIR Recon DL has been previously cleared for use with 2D Cartesian, 3D Cartesian, and PROPELLER imaging sequences.

The proposed device is a modified version of AIR Recon DL that includes a new deep-learning phase correction algorithm for applications that create multiple intermediate images and combine them, such as Diffusion Weighted Imaging where multiple NEX images are collected and combined. This enhancement is an optional feature that is integrated into the MR system software and activated through an additional purchasable software option key (separate from the software option keys of the predicate device).

This document describes the acceptance criteria and the studies conducted to prove the performance of the AIR Recon DL device, as presented in the FDA 510(k) clearance letter.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Nonclinical Testing:
  • Phantom testing was conducted for the DLPC and PC-ARDL models. No specific sample size (number of phantom scans) is provided, but it implies a sufficient number for evaluation.
• In-Vivo Performance Testing:
  • ADC Accuracy: Diffusion-weighted brain images were acquired at 1.5T with b-values = 50, 400, 800, 1200 s/mm². The number of subjects is not explicitly stated, but it's referred to as "diffusion images" and "diffusion-weighted brain images."
  • Low-Contrast Detectability: Raw data from 4 diffusion-weighted brain scans were used.
• Quantitative Post Processing (Repeatability Study):
  • 6 volunteers were recruited. 2 volunteers scanned on a 1.5T scanner, 4 on a 3T scanner.
  • Scanned anatomical regions included brain, spine, abdomen, pelvis, and breast.
  • Each sequence was repeated 4 times.
  • Data Provenance: The document states "in-vivo data" and "volunteer scanning was performed simulating routine clinical workflows." This suggests prospective scanning of human subjects, likely in a controlled environment. The country of origin is not specified, but given the FDA submission, it's likely U.S. or international data meeting U.S. standards. The statement "previously acquired de-identified cases" for the Clinical Image Quality Study refers to retrospective data for that specific study, but the volunteer scanning for repeatability appears prospective.
• Clinical Image Quality Study:
  • 34 datasets of previously acquired de-identified cases.
  • Data Provenance: "previously acquired de-identified cases" indicates retrospective data. The country of origin is not specified.

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

• Nonclinical Testing: Ground truth established through phantom measurements and expected physical properties (e.g., signal bias, noise floor). No human experts involved in establishing ground truth here.
• In-Vivo Performance Testing:
  • ADC Accuracy: "Average ADC values were measured from regions of interest in the lateral ventricles." This implies expert selection of ROIs, but the number of experts is not specified. The ground truth for ADC is the expected isotropic Gaussian diffusion in these regions.
  • Low-Contrast Detectability: "The contrast ratio and contrast-to-noise ratio for each of the inserts were measured." This is a quantitative measure, not explicitly relying on expert consensus for ground truth on detectability, but rather on the known properties of the inserted synthetic objects.
• Quantitative Post Processing:
  • ADC Repeatability: Ground truth for repeatability is based on quantitative measurements and statistical analysis (coefficient of variability). ROI placement would typically be done by an expert, but the number is not specified.
  • Phase Correction Effectiveness: Ground truth is based on the theoretical expectation of signal distribution in real/imaginary channels after ideal phase correction.
• Clinical Image Quality Study:
  • One (1) U.S. Board Certified Radiologist was used.
  • Qualifications: "U.S. Board Certified Radiologist." No explicit number of years of experience is stated, but Board Certification indicates a high level of expertise.

4. Adjudication Method for the Test Set

• Nonclinical/Phantom Testing: No explicit adjudication method described beyond passing defined acceptance criteria for quantitative metrics.
• In-Vivo Performance Testing: Quantitative measurements (ADC values, contrast ratios, CNR) were used. Paired t-tests were conducted, which is a statistical comparison method, not an adjudication process as typically defined for expert readings.
• Quantitative Post Processing: Quantitative measurements and statistical analysis (coefficient of variability, comparison of real/imaginary channels).
• Clinical Image Quality Study: A single U.S. Board Certified Radiologist made the assessment. There is no stated adjudication method described, implying a single-reader assessment for clinical image quality.

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

• An MRMC comparative effectiveness study was not explicitly described as a formal study design in the provided text.
• The "Clinical Image Quality Study" involved only one radiologist, so it does not qualify as an MRMC study.
• There is no reported effect size of how much human readers improve with AI vs. without AI assistance. The study rather focused on the AI-reconstructed images' standalone diagnostic quality.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Yes, performance was evaluated in a standalone manner.
  • Nonclinical Testing: Phantom studies directly evaluate the algorithm's output against known physical properties and defined metrics.
  • In-Vivo Performance Testing: ADC accuracy and low-contrast detectability were measured directly from the reconstructed images, which is a standalone evaluation of the algorithm's quantitative output.
  • Quantitative Post Processing: Repeatability and effectiveness of phase correction in real/imaginary channels are algorithm-centric evaluations.
  • Even the clinical image quality study, while involving a human reader, assessed the standalone output of the algorithm (AIR Recon DL with Phase Correction) for diagnostic quality.

7. Type of Ground Truth Used

• Expert Consensus: Not explicitly stated as the primary ground truth for quantitative metrics, but one radiologist's assessment served as the primary clinical ground truth.
• Pathology: Not used as ground truth in the provided study descriptions. While some datasets "included pathological features such as prostate cancer... hepatocellular carcinoma," the assessment by the radiologist was on "diagnostic quality" of the images, not a comparison against pathology reports for definitive disease identification.
• Outcomes Data: Not used as ground truth.
• Other:
  • Physical Properties/Known Standards: For phantom testing (e.g., signal bias, noise floor, SNR, sharpness), and for theoretical expectations of ADC values in specific regions (lateral ventricles).
  • Known Synthetic Inserts: For low-contrast detectability.
  • Theoretical Expectations: For phase correction effectiveness (signal in real, noise in imaginary).

8. Sample Size for the Training Set

• The document does not provide any specific sample size for the training set used for the deep learning models (DLPC and PC-ARDL). It only states that the models are "deep learning-based."

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

• The document does not provide any information on how the ground truth for the training set was established. It only describes the testing of the final, trained models.

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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.

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.

Ask a specific question about this device

The Swoop Portable MR Imaging System is a portable, ultra-low field magnetic resonance imaging device for producing images that display the internal structure of the head where full diagnostic examination is not clinically practical. When interpreted by a trained physician, these images provide information that can be useful in determining a diagnosis.

The Swoop System is portable, ultra-low field MRI device that enables visualization of the internal structures of the head using standard magnetic resonance imaging contrasts. The main interface is a commercial off-the-shelf device that is used for operating the system, providing access to patient data, exam setup, exam execution, viewing MRI image data for quality control purposes, and cloud storage interactions. The system can generate MRI data sets with a broad range of contrasts. The Swoop system user interface includes touch screen menus, controls, indicators, and navigation icons that allow the operator to control the system and to view imagery. The Swoop System image reconstruction algorithm utilizes deep learning to provide improved image quality for T1W, T2W, FLAIR, and DWI sequences.

The subject Swoop System described in this submission includes software modifications related to the pulse sequences.

Here's a breakdown of the acceptance criteria and study details for the Swoop® Portable MR Imaging® System, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Study Details for Advanced Reconstruction Validation (DWI Sequence - updated in this submission)

This section focuses specifically on the studies conducted to validate the Advanced Reconstruction models for the updated DWI sequence. Performance analysis and validation for T1/T2/FLAIR models were leveraged from predicate devices, so this analysis covers the new data.

1. Performance Analysis

• Sample Size:
  • Test Set (DWI): 8 patients, 31 images.
  • Data Provenance: Not explicitly stated, but includes data from 6 different sites. Countries of origin are not specified. The study is retrospective, utilizing existing MRI data.
• Ground Truth Establishment (Test Set):
  • Number of Experts: Not applicable for quantitative metrics (NMSE, SSIM). Quantitative metrics were reference-based, comparing reconstructed images to ground truth target images (Swoop data, high field images, and synthetic contrast images).
  • Qualifications of Experts: N/A.
• Adjudication Method: N/A.
• MRMC Comparative Effectiveness Study: No, this was a standalone performance analysis comparing Advanced Reconstruction to Linear Reconstruction against a reference standard.
• Standalone Performance: Yes. The algorithm's output was compared to ground truth images using quantitative metrics.
• Type of Ground Truth: Reference images, including Swoop data, high field images, and synthetic contrast images. Test input data (synthetic k-space) was generated from these target images.
• Training Set Sample Size: Not explicitly stated for this particular updated DWI model. The document states "None of these test images were used in model training," implying a separate training set, but its size is not provided.
• Ground Truth Establishment (Training Set): Not explicitly stated, but generally for deep learning reconstruction, the training data would include raw k-space data paired with corresponding reference images (often higher quality, known good reconstructions, or synthetic data).

2. Contrast-to-Noise Ratio (CNR) Validation

• Sample Size:
  • Test Set (DWI): 12 patients, 45 images, 145 Regions of Interest (ROIs).
  • Data Provenance: Not explicitly stated, but includes data from 5 different sites. Countries of origin are not specified. Retrospective.
• Ground Truth Establishment (Test Set):
  • Number of Experts: At least one.
  • Qualifications of Experts: An American Board of Radiology (ABR) certified radiologist reviewed the annotations for accuracy.
• Adjudication Method: Not explicitly stated as a formal adjudication method (like 2+1), but radiologists reviewed ROI accuracy.
• MRMC Comparative Effectiveness Study: No, this was a standalone quantitative comparison of CNR between Advanced Reconstruction and Linear Reconstruction.
• Standalone Performance: Yes. The algorithm's output was quantitatively measured and compared to the linear reconstruction, using expert-annotated ROIs for pathology.
• Type of Ground Truth: Expert-annotated regions of interest (ROIs) encompassing pathologies, reviewed for accuracy by an ABR-certified radiologist.
• Training Set Sample Size: Not explicitly stated.
• Ground Truth Establishment (Training Set): Not explicitly stated.

3. Advanced Reconstruction Image Validation (Radiologist Review)

• Sample Size:
  • Test Set (DWI): 34 patients, 34 sets of DWI images (102 individual images when considering b=0, trace-weighted/single direction, and ADC).
  • Data Provenance: Not explicitly stated, but includes data from 8 different sites. Countries of origin are not specified. Retrospective by nature of rating existing images.
• Ground Truth Establishment (Test Set): Ground truth for rating was established by consensus of the clinical reviewers' assessments on a Likert scale. There wasn't an independent "definitive" ground truth for image quality beyond the expert reviews.
• Number of Experts: Four.
• Qualifications of Experts: External, ABR-certified radiologists representing clinical users.
• Adjudication Method: Not explicitly stated if there was a formal adjudication if reviewers disagreed. Instead, they rated independently, and median scores were used for evaluation.
• MRMC Comparative Effectiveness Study: This study had elements of an MRMC study by using multiple readers (4 radiologists) to rate multiple cases (34 image sets) with and without the AI assistance (Advanced vs. Linear Reconstruction, though not exactly "assisted" as in "human + AI" vs. "human only").
  • Effect Size: Advanced Reconstruction achieved a median score of 2 (the most positive rating scale value) in all categories. This indicates a significant improvement in perceived image quality and diagnostic consistency compared to Linear Reconstruction (which would be analogous to "without AI assistance" in this context), as the criteria required only a median score ≥1 in one category for "better performance."
• Standalone Performance: Partially. While radiologists rated the images, their input constituted the performance metric. It's not a purely algorithmic standalone performance against a fixed ground truth.
• Type of Ground Truth: Expert consensus ratings (Likert scale) on image quality attributes and diagnostic consistency.
• Training Set Sample Size: Not explicitly stated.
• Ground Truth Establishment (Training Set): Not explicitly stated.

In summary, for the updated DWI sequence validation:

• Test Set Sample Sizes:
  • Performance Analysis: 8 patients, 31 images
  • CNR Validation: 12 patients, 45 images, 145 ROIs
  • Image Validation: 34 patients, 34 image sets (102 images)
• Data Provenance: Retrospective, multiple sites (6 for performance, 5 for CNR, 8 for image validation via different Swoop System models), countries not specified.
• Expert Reviewers: An ABR-certified radiologist for ROI accuracy in CNR validation, and four external ABR-certified radiologists for the image quality review.
• Ground Truth: Varied from reference images, to expert-annotated ROIs, to expert consensus ratings.
• Training Set Details: Minimal information provided regarding the training set's size or ground truth establishment in this document. The focus here is on the validation of the updated DWI model.

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MAGNETOM Free.Max:
The MAGNETOM MR system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross-sectional images that display, 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 may also be produced. Depending on the region of interest, contrast agents may be used. These images and the physical parameters derived from the images when interpreted by a trained physician or dentist trained in MRI yield information that may assist in diagnosis.

The MAGNETOM MR system may also be used for imaging during interventional procedures when performed with MR-compatible devices such as MR Safe biopsy needles.

When operated by dentists and dental assistants trained in MRI, the MAGNETOM MR system must only be used for scanning the dentomaxillofacial region.

MAGNETOM Free.Star:
The MAGNETOM MR system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross-sectional images that display, 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 may also be produced. Depending on the region of interest, contrast agents may be used. These images and the physical parameters derived from the images when interpreted by a trained physician yield information that may assist in diagnosis.

The subject devices MAGNETOM Free.Max and MAGNETOM Free.Star with software version syngo MR XA80A, consists of new and modified hardware and software features comparing to the predicate device MAGNETOM Free.Max and MAGNETOM Free.Star with software version syngo MR XA60A (K231617).

New hardware features (Only for MAGNETOM Free.Max):

• Dental coil
• High-end host
• syngo Workplace

Modified hardware features:

• MaRS
• Select&GO Display (TPAN_3G)

New Pulse Sequences/ Software Features / Applications:

Only for MAGNETOM Free.Max:

• EP_SEG_FID_PHS
• EP2D_FID_PHS
• EP_SEG_PHS
• GRE_Proj
• GRE_PHS
• myExam Dental Assist
• Select&GO Dental
• Slice Overlapping

For both MAGNETOM Free.Max and MAGNETOM Free.Star:

• Eco Power Mode
• Extended Gradient Eco Mode
• System Startup Timer

Modified Features and Applications:

• myExam RT Assist (only for MAGNETOM Free.Max)
• Deep Resolve for HASTE
• Deep Resolve for EPI Diffusion
• Select&GO for dental (only for MAGNETOM Free.Max)
• Select&GO extension: Patient Registration and Start Scan
• SPACE improvement: MTC prep module

Other Modifications and Minor Changes:

• MAGNETOM Free.Max Dental Edition marketing bundle (only for MAGNETOM Free.Max)
• MAGNETOM Free.Max RT Pro Edition marketing bundle (only for MAGNETOM Free.Max)
• Off-Center Planning Support
• ID Gain

The provided FDA 510(k) clearance letter and summary for MAGNETOM Free.Max and MAGNETOM Free.Star (K251822) offer high-level information regarding the devices and their comparison to predicate devices. However, it does not explicitly detail acceptance criteria (performance metrics with pass/fail thresholds) or a specific study proving the device meets those criteria for the overall device clearance.

The document primarily focuses on demonstrating substantial equivalence to predicate devices for general MR diagnostic imaging. The most detailed performance evaluation mentioned is for the AI feature "Deep Resolve Boost." Therefore, the response will focus on the information provided regarding Deep Resolve Boost, and address other points based on what is stated and what is not.

Acceptance Criteria and Device Performance (Focusing on Deep Resolve Boost)

Table 1. Deep Resolve Boost Performance Summary

Note: The document does not provide numerical thresholds or specific statistical methods used to define "significantly better" for SSIM and PSNR. The acceptance criteria are implicitly derived from the reported positive performance relative to conventional reconstruction.

Study Details for Deep Resolve Boost

1. Sample Size used for the test set and the data provenance:

   • Test Data: A "set of test data" was used for quantitative metrics (SSIM, PSNR) and visual evaluation. This test data was a "retrospectively undersampled copy of the test data" which was also used for conventional reconstruction.
   • Provenance: "In-house measurements and collaboration partners."
   • Retrospective/Prospective: The process of creating the test data by manipulating (undersampling) retrospectively acquired data indicates a retrospective approach.

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

   • Number of Experts: Not specified. The document states, "Visual evaluation was performed by qualified readers."
   • Qualifications of Experts: "Qualified readers." No further specific qualifications (e.g., years of experience, specialty) are provided.

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

   • Not specified. The document states, "Visual evaluation was performed by qualified readers." It does not mention whether multiple readers were used per case or how discrepancies were resolved.

4. 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, an MRMC comparative effectiveness study involving human readers with vs. without AI assistance was not explicitly described for the Deep Resolve Boost feature. The visual evaluation was focused on comparing images reconstructed with conventional methods versus Deep Resolve Boost, primarily to assess image quality attributes without explicit human performance metrics (e.g., diagnostic accuracy, reading time).

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

   • Yes, a standalone performance evaluation was done. The quantitative metrics (SSIM, PSNR) and the visual assessment of images reconstructed solely by the algorithm (Deep Resolve Boost) were performed to characterize the network's impact independently.

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

   • The "acquired datasets represent the ground truth for the training and validation." Input data for testing was "retrospectively created from the ground truth by data manipulation and augmentation." This implies that the raw, fully sampled, and high-quality MRI acquisitions are considered the ground truth against which the reconstructed images (conventional and Deep Resolve Boost) are compared. This is a technical ground truth rather than a clinical ground truth like pathology.

7. The sample size for the training set:

   • TSE: More than 25,000 slices.
   • HASTE: Pretrained on the TSE dataset and refined with more than 10,000 HASTE slices.
   • EPI Diffusion: More than 1,000,000 slices.

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

   • "The acquired datasets represent the ground truth for the training and validation."
   • Input data for training was "retrospectively created from the ground truth by data manipulation and augmentation." This included "further under-sampling of the data by discarding k-space lines, lowering of the SNR level by addition of noise and mirroring of k-space data."
   • This indicates that the ground truth for training was derived from high-quality, fully sampled MRI acquisitions, which were then manipulated to simulate lower quality inputs for the AI to learn from.

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The InkSpace Imaging Small Body Array is a receive-only coil, used for obtaining diagnostic images of general human anatomy, such as cardiac, hip, shoulder, liver, knee, and ankle in Philips 3.0T magnetic resonance imaging systems. These images, when interpreted by a trained physician, yield information that may assist in diagnosis.

The InkSpace Imaging Small Body Array is a phased array, receive-only coil designed to work on Philips 3.0 Tesla (3.0T) MRI scanners. It consists of 12 elements which are optimized to be lightweight and flexible to easily conform to a patient's anatomy, providing high signal-to-noise ratio. The elements are enclosed in a soft padded pouch which can be wiped clean. The array is designed for proton imaging at 3.0 Tesla and has an optimal field of view of 27x25cm.

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The uMR 680 system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces 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.

These images and the physical parameters derived from the images when interpreted by a trained physician yield information that may assist the diagnosis. Contrast agents may be used depending on the region of interest of the scan.

The uMR 680 is a 1.5T superconducting magnetic resonance diagnostic device with a 70cm size patient bore. It consists of components such as magnet, RF power amplifier, RF coils, gradient power amplifier, gradient coils, patient table, spectrometer, computer, equipment cabinets, power distribution system, internal communication system, and vital signal module etc. The uMR 680 Magnetic Resonance Diagnostic Device is designed to conform to NEMA and DICOM standards.

This special 510(k) is to request modifications for the cleared uMR 680(K243397). The modifications performed on the uMR 680 in this submission are due to the following changes that include:

(1) Addition of RF coils: Tx/Rx Head Coil.
(2) Addition of a mobile configuration.

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