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The MAGNETOM system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images, spectroscopic images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These images and/or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

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

The subject device, MAGNETOM Avanto Fit with software syngo MR XA70A, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Avanto Fit with syngo MR XA50A (K220151).

A high-level summary of the new and modified hardware and software is provided below:

For MAGNETOM Avanto Fit with syngo MR XA70:

Hardware

New Hardware:
myExam 3D Camera
BM Head/Neck 20

Modified Hardware:
Sanaflex (cushions for patient positioning)

Software

New Features and Applications:
myExam Autopilot Brain
myExam Autopilot Knee
3D Whole Heart
HASTE_interactive
GRE_PC
Open Recon
Deep Resolve Gain
Fleet Reference Scan
Physio logging
complex averaging
AutoMate Cardiac
Ghost Reduction
BLADE diffusion
Beat Sensor
Deep Resolve Sharp
Deep Resolve Boost and Deep Resolve Boost (TSE)
Deep Resolve Boost HASTE
Deep Resolve Boost EPI Diffusion

Modified Features and Applications:
SPACE improvement (high band)
SPACE improvement (incr grad)
Brain Assist
Eco power mode
myExam Angio Advanced Assist (Test Bolus)

The subject device, MAGNETOM Skyra Fit with software syngo MR XA70A, consists of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Skyra Fit with syngo MR XA50A (K220589).

A high-level summary of the new and modified hardware and software is provided below:

For MAGNETOM Skyra Fit with syngo MR XA70:

Hardware

New Hardware:
myExam 3D Camera

Modified Hardware:
Sanaflex (cushions for patient positioning)

Software

New Features and Applications:
Beat Sensor
HASTE_interactive
GRE_PC
3D Whole Heart
Deep Resolve Gain
Open Recon
Ghost Reduction
Fleet Reference Scan
BLADE diffusion
HASTE diffusion
Physio logging
complex averaging
Deep Resolve Swift Brain
Deep Resolve Sharp
Deep Resolve Boost and Deep Resolve Boost (TSE)
Deep Resolve Boost HASTE
Deep Resolve Boost EPI Diffusion
AutoMate Cardiac
SVS_EDIT

Modified Features and Applications:
SPACE improvement (high band)
SPACE improvement (incr grad)
Brain Assist
Eco power mode
myExam Angio Advanced Assist (Test Bolus)

The subject device, MAGNETOM Sola Fit with software syngo MR XA70A, 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 XA51A (K221733).

A high-level summary of the new and modified hardware and software is provided below:

For MAGNETOM Sola Fit with syngo MR XA70:

Hardware

New Hardware:
myExam 3D Camera

Modified Hardware:
Sanaflex (cushions for patient positioning)

Software

New Features and Applications:
GRE_PC
3D Whole Heart
Ghost Reduction
Fleet Reference Scan
BLADE diffusion
Physio logging
Open Recon
Complex averaging
Deep Resolve Sharp
Deep Resolve Boost and Deep Resolve Boost (TSE)
Deep Resolve Boost HASTE
Deep Resolve Boost EPI Diffusion
AutoMate Cardiac
Implant suite

Modified Features and Applications:
SPACE improvement (high band)
SPACE improvement (incr grad)
Brain Assist
Eco power mode

The subject device, MAGNETOM Viato.Mobile with software syngo MR XA70A, 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 XA51A (K240608).

A high-level summary of the new and modified hardware and software is provided below:

For MAGNETOM Viato.Mobile with syngo MR XA70:

Hardware

New Hardware:
n.a.

Modified Hardware:
Sanaflex (cushions for patient positioning)

Software

New Features and Applications:
GRE_PC
3D Whole Heart
Ghost Reduction
Fleet Reference Scan
BLADE diffusion
Physio logging
Open Recon
Complex averaging
Deep Resolve Sharp
Deep Resolve Boost and Deep Resolve Boost (TSE)
Deep Resolve Boost HASTE
Deep Resolve Boost EPI Diffusion
AutoMate Cardiac
Implant suite

Modified Features and Applications:
SPACE improvement (high band)
SPACE improvement (incr grad)
Brain Assist
Eco power mode

Furthermore, the following minor updates and changes were conducted for the subject devices:

Low SAR Protocol minor update (for all subject devices but MAGNETOM Skyra Fit): the goal of the SAR adaptive protocols was to be able to perform knee, spine, heart and brain examinations with 50% of the max allowed SAR values in normal mode for head and whole-body SAR. The SAR reduction was achieved by parameter adaptations like Flip angle, TR, RF Pulse Type, Turbo Factor, concatenations. For cardiac clinically accepted alternative imaging contrasts are used (submitted with K232494).

Implementation of image sorting prepare for PACS (submitted with K231560).

Implementation of improved DICOM color support (submitted with K232494).

Needle intervention AddIn was added all subject device (submitted with K232494).

Inline Image Filter switchable for users: in the subject device, users have the ability to switch the "Inline image filter" (implicite Filter) on or off. This filter is an image-based filter that can be applied to specific pulse sequence types. The function of the filter remains unchanged from the previous device MAGNETOM Sola with syngo MR XA61A (K232535).

SVS_EDIT is newly added for MAGNETOM Skyra Fit, but without any changes (submitted with K203443)

Brain Assist received an improvement and is identical to that of snygo MR XA61A (K232535)

Open Recon is introduced for all systems. The function of Open Recon remains unchanged from the previous submissions (submitted with K221733).

Lock TR and FA in Bold received a minor UI update

Implant Suite is newly introduced for MAGNETOM Sola Fit and MAGNETOM Viato.Mobile, but without any changes (submitted with K232535)

myExam Autopilot Brain and myExam Autopilot Knee are newly introduced for the subject device MAGNETOM AVANTO Fit and are unchanged from previous submissions (submitted with K221733).

myExam Angio Advanced Assist (Test Bolus) received a bug fixing and minimal UI improvements.

The provided text is an FDA 510(k) clearance letter for various MAGNETOM MRI Systems. While it details new and modified software and hardware features, it does not include specific acceptance criteria or a study that "proves the device meets the acceptance criteria" in terms of performance metrics like sensitivity, specificity, or accuracy for a diagnostic task.

Instead, the document focuses on demonstrating substantial equivalence to predicate devices. This is achieved by:

• Stating that the indications for use are the same.
• Listing numerous predicate and reference devices.
• Detailing hardware and software changes.
• Mentioning non-clinical tests like software verification and validation, sample clinical images, and image quality assessment to show that the new features maintain an "equivalent safety and performance profile" to the predicate devices.
• Referencing scientific publications for certain features to support their underlying principles and utility.
• Briefly describing the training and validation data for two AI features: Deep Resolve Boost and Deep Resolve Sharp, but without performance acceptance criteria or detailed results.

Therefore, much of the requested information cannot be extracted from this document because it is not a study report detailing clinical performance against predefined acceptance criteria for a specific diagnostic outcome.

However, I can extract the information related to the AI features as best as possible from the "AI Features/Applications training and validation" section (Page 16).

Acceptance Criteria and Study Details (Limited to AI Features)

1. Table of Acceptance Criteria and Reported Device Performance

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

• Deep Resolve Boost:
  • Test Set Sample Size: Not explicitly stated as a separate "test set" size. The document mentions "training and validation data" for over 25,000 TSE slices, over 10,000 HASTE slices (for refinement), and over 1,000,000 EPI Diffusion slices. It's unclear what proportion of this was used specifically for final testing, or if the "validation" mentioned includes the final performance evaluation.
  • Data Provenance: Retrospective, described as "Input data was retrospectively created from the ground truth by data manipulation and augmentation." Country of origin is not specified.
• Deep Resolve Sharp:
  • Test Set Sample Size: Not explicitly stated as a separate "test set" size. The document mentions "training and validation" on more than 10,000 high resolution 2D images. Similar to Deep Resolve Boost, it's unclear what proportion was specifically for final testing.
  • Data Provenance: Retrospective, described as "Input data was retrospectively created from the ground truth by data manipulation." Country of origin is not specified.

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

This information is not provided in the document. The definition of "ground truth" for the AI features refers to the acquired datasets themselves rather than expert-labeled annotations. Visual comparisons are mentioned as part of the evaluation, but without details on expert involvement or qualifications.

4. Adjudication method for the test set

This information is not provided in the document. While "visual comparisons" and "visual rating" are mentioned, no specific adjudication method (e.g., 2+1, 3+1) is described.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No, a MRMC comparative effectiveness study demonstrating human reader improvement with AI assistance is not described in this document. The focus of the AI features (Deep Resolve Boost and Deep Resolve Sharp) is on image quality enhancement (denoising, sharpness) and reconstruction rather than assisting human readers in a diagnostic task that can be quantified by an effect size.

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

Yes, the evaluation of Deep Resolve Boost and Deep Resolve Sharp, based on metrics like PSNR, SSIM, and perceptual loss, and "visual comparisons" or "visual rating" appears to be an assessment of the algorithm's performance in enhancing image quality in a standalone capacity, without direct human-in-the-loop interaction for diagnosis.

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

• Deep Resolve Boost: "The acquired datasets (as described above) represent the ground truth for the training and validation." This implies the original, full-quality, unaltered MRI scan data. Further, "Input data was retrospectively created from the ground truth by data manipulation and augmentation. This process includes further under-sampling of the data by discarding k-space lines, lowering of the SNR level by addition Restricted of noise and mirroring of k-space data."
• Deep Resolve Sharp: "The acquired datasets represent the ground truth for the training and validation." Similar to Boost, this refers to original, high-resolution MRI scan data. For training, "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."

8. The sample size for the training set

• Deep Resolve Boost:
  • TSE: more than 25,000 slices
  • HASTE (for refinement): more than 10,000 HASTE slices
  • EPI Diffusion: more than 1,000,000 slices
• Deep Resolve Sharp: more than 10,000 high resolution 2D images.

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

• Deep Resolve Boost: The ground truth was established by the "acquired datasets" themselves (full-quality MRI scans). The training input data was then derived from this ground truth by simulating degraded images (e.g., under-sampling, adding noise).
• Deep Resolve Sharp: Similarly, the ground truth was the "acquired datasets" (high-resolution MRI scans). The training input data was derived by cropping k-space data to create corresponding low-resolution inputs.

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The MAGNETOM system is indicated for use as a magnetic device (MRDD) that produces transverse, sagittal, coronal and oblique cross sectional images, and that displays the internal structure and/or function of the head or extremities. Other physical parameters derived from the images may also be produced. Additionally, the MAGNETOM system is intended to produce Sodium images for the head and Phosphorus spectroscopic images and/or spectra for whole body, excluding the head. 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.

MAGNETOM Terra and MAGNETOM Terra.X with software syngo MR XA60A include new and modified hardware and software compared to the predicate device, MAGNETOM Terra with software syngo MR E12U. A high level summary of the new and modified hardware and software is provided below: Hardware: New Hardware (Combiner (pTx to sTx), MC-PALI, GSSU control unit, 8Tx32Rx Head coil), Modified Hardware (Main components such as: Upgrade of GPA, New Host computer hardware, New MaRS computer hardware, Upgrade the SEP, The new shim cabinet ASC5 replaces two ACS4 shim cabinets; Other components such as: RFPA, Use of a common MR component which provides basic functionality that is required for all MAGNETOM system types, The multi-nuclear (MNO) option has been modified, OPS module, Cover with UI update on PDD). Software: New Features and Applications (Static B1 shimming, TrueForm (1ch compatibility mode), Deep Resolve Boost, Deep Resolve Gain, Deep Resolve Sharp, Bias field correction (marketing name: Deep RxE), The new BEAT pulse sequence type, BLADE diffusion, The PETRA pulse sequence type, TSE DIXON, The Compressed Sensing (CS) functionality is now available for the SPACE pulse sequence type, The Compressed Sensing (CS) functionality is now available for the TFL pulse sequence type, IDEA, The Scientific Suite), Modified Features and Applications (EP2D DIFF and TSE with SliceAdjust, The Turbo Flash (TFL)), Modified Software / Platform (Stimulation monitoring, "dynamic research labeling"), Other Modifications and / or Minor Changes (Intended use, SAR Calculation and Weight limit reduction for 31P/1H TxRx Flex Loop Coil, X-upgrade for MAGNETOM Terra to MAGNETOM Terra.X, Provide secure MR scanner setup for DoD (Department of Defense) -Information Assurance compliance).

The provided text describes the acceptance criteria and supporting study for the AI features (Deep Resolve Boost, Deep Resolve Sharp, and Deep RxE) within the MAGNETOM Terra and MAGNETOM Terra.X devices.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

2. Automated unit-tests are set up to test the consistency of the generated output to a previously defined reference output.
3. During verification, the performance of the network is tested on a phantom against the ground truth with a maximal allowed NRMSE of 11% (for 2D network) and 8.7% (for 3D network).
4. The trained final network was used in the clinical study. (Implicit: Increases image homogeneity in a reproducible way on the receive profile, and images acquired with Deep RxE are rated better for image quality in the clinical study). | 1. During training, the loss, as the difference to a ground truth, is monitored and the training step with the lowest test loss is taken as the final trained network.
5. Automated unit-tests are set up to test the consistency of the generated output to a previously defined reference output.
6. During verification, the performance of the network is tested on a phantom against the ground truth with a maximal allowed NRMSE of 11% (11% for the 2D network and 8.7% for the 3D network were achieved).
7. The trained final network was used in the clinical study.
   The tests show that Deep RxE increases image homogeneity in a reproducible way on the receive profile. Images acquired with Deep RxE (DL bias field correction) are rated better for image quality than the ones acquired without it in the clinical study that was conducted. |

Note on Acceptance Criteria: The document directly states acceptance criteria for Deep RxE (e.g., NRMSE

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MAGNETOM Free.Max system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal, and oblique cross-sectional images that display 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 vield information that may assist in diagnosis.

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

MAGNETOM Free.Star system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal, and oblique cross-sectional images that display 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.

MAGNETOM Free.Max and MAGNETOM Free.Star with syngo MR XA60A include new and modified features compared to the predicate devices MAGNETOM Free.Max and MAGNETOM Free.Star with syngo MR XA50A (K220575, cleared on June 24, 2022).

Below is a high-level summary of the new and modified hardware and software features compared to the predicate devices MAGNETOM Free.Max and MAGNETOM Free.Star with syngo MR XA50A:

Hardware
New hardware features:

• Contour Knee coil
• Respiratory Sensor

Modified hardware features:

• myExam 3D Camera
• Host computer
• MaRS

Software
New Features and Applications:

• Injector coupling
• Respiratory Sensor Support
• myExam RT Assist (only for MAGNETOM Free.Max)
• myExam Autopilot Hip
• Deep Resolve Boost
• Complex Averaging
• HASTE_Interactive (only for MAGNETOM Free.Max)
• BEAT_Interactive (only for MAGNETOM Free.Max)
• Needle Intervention AddIn (only for MAGNETOM Free.Max)

Modified Features and Applications:

• Deep Resolve Sharp
• Deep Resolve Gain
• SMS Averaging

Other Modifications:

• Indications for Use (only for MAGNETOM Free.Max)
• MAGNETOM Free.Max RT Edition marketing bundle (only for MAGNETOM Free.Max)

The provided text describes information about the submission of the MAGNETOM Free.Max and MAGNETOM Free.Star MRI systems for FDA 510(k) clearance, and references a specific AI feature called "Deep Resolve Boost." However, it does not contain acceptance criteria or a detailed study proving the device meets specific performance criteria for the AI feature.

The section titled "Test statistics and test results" for Deep Resolve Boost (Table 1, page 7) mentions that the impact of the network was characterized by quality metrics such as PSNR and SSIM, and visual inspection. It also states: "After successful passing of the quality metrics tests, work-in-progress packages of the network were delivered and evaluated in clinical settings with cooperation partners." This suggests internal testing and evaluation, but does not provide the specific numerical acceptance criteria or the detailed results of these tests.

Therefore, I cannot fully complete the requested table and answer all questions due to the lack of this specific information in the provided document.

However, I can extract the available information regarding the AI feature "Deep Resolve Boost" as much as possible:

1. Table of acceptance criteria and the reported device performance:

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

• Test Set (Validation set for AI feature Deep Resolve Boost):
  • Sample Size: 1,874 2D slices.
  • Data Provenance: "in-house measurements and collaboration partners." The document does not specify the country of origin.
  • Retrospective or Prospective: Retrospectively created from ground truth by data manipulation and augmentation.

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

• Number of experts: Not specified.
• Qualifications of experts: The document states the "acquired datasets represent the ground truth for the training and validation," but it does not specify how this ground truth was established in terms of expert involvement for the test set. It mentions "clinical settings with cooperation partners" for evaluation, but this is distinct from ground truth establishment.

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

• Not specified. The document states "acquired datasets represent the ground truth," suggesting pre-existing data or a different method of ground truth establishment than explicit reader adjudication for this AI feature.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• The document states "No clinical tests were conducted to support substantial equivalence for the subject device" (page 10). It mentions that "work-in-progress packages of the network were delivered and evaluated in clinical settings with cooperation partners," but this is not described as an MRMC comparative effectiveness study, nor are any results on human reader improvement reported.

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

• The performance of the "Deep Resolve Boost" AI feature was characterized by "quality metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)" and visual inspection, which suggests a standalone evaluation of the algorithm's output against a reference standard. Specific results are not provided.

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

• For Deep Resolve Boost: "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 implies that high-quality, likely clinical-grade, MRI scans acquired without the AI feature were considered the "ground truth" to which the AI-processed images were compared. It's not explicitly stated if this "ground truth" itself was established by expert consensus, but it infers it from high-quality clinical acquisition.

8. The sample size for the training set:

• For Deep Resolve Boost: 24,599 2D slices.

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

• "The acquired 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 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 "ground truth" was established by using full, high-quality MR images. The "input data" for the AI model (which the AI then "boosts") was intentionally degraded (under-sampled, noised) from this high-quality ground truth. The AI's task is to reconstruct the degraded input data back to resemble the original high-quality "ground truth."

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The MAGNETOM system is indicated for use as a magnetic 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 inages and/ or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician vield 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.

MAGNETOM Lumina and MAGNETOM Vida Fit with software syngo MR XA50A include new software compared to the predicate devices, MAGNETOM Vida Fit with software syngo MR XA20A (K192924) and MAGNETOM Lumina with syngo MR XA31A (K203443). This software and some hardware components are transferred from the reference device MAGNETOM Vida with software syngo MR XA50A (K213693) as well as an imaging feature from MAGNETOM Vida with software syngo MR XA11A (K181433). A high-level summary of the transferred hardware and software is provided below:

Hardware (Vida Fit only)
Transferred Hardware:

• The Nexaris Dockable Table is a new variant of the MR patient table which is used for intraoperative or interventional imaging. It enables the patient transfer between OR tables and the MR system without repositioning on the MR patient table and vice versa during interventional procedures and surgeries. Additionally, it can be used for diagnostic imaging.
• The Nexaris Head Frame holds up to two Ultra Flex Large 18 coils. It can be used for head imaging in combination with the Nexaris Dockable Table when the patient is positioned on the transfer board but not pinned in a head clamp.
• Transferred MaRS Computer
  Transferred Coil:
• The Nexaris Spine 36 is used in combination with and without transfer board for body imaging on the Nexaris Dockable Table.
  Transferred modifications for hardware:
• The Beat Sensor is a contact less method for generating cardiac triggers as an alternative to the already existing ECG or pulse triggers. It is based on a measurement of the modulation of a weak magnetic Pilot Tone, caused by conformation changes in conductive tissues.
  Software
  Transferred Features and Applications: Vida Fit only:
• SVS EDIT is a special variant of the SVS SE pulse sequence type, which acquires two different spectra (one with editing pulses on resonance, one with editing pulses off resonance) within a single sequence.
• BEAT FQ nav allows the user to make use of navigator echo based respiratory gating for flow imaging to acquire 4D flow data. Both navigator echo based respiratory gating as well as flow imaging are part of the predicate device already. New is merely the combination of both.
• The HASTE interactive pulse sequence type extends the existing HASTE pulse sequence type by offering the possibility to interactively change imaging parameters.
• GRE_WAVE is a special variant of the GRE pulse sequence type which allows larger acceleration factors, measuring one or two contrasts. GRE Wave results in higher signal-to-noise ratio for larger acceleration factors which can be leveraged to allow fast high-resolution 3D susceptibility-weighted imaging.
• The myExam Prostate Assist provides an assisted and quided workflow for prostate imaging. This automated workflow leads to higher reproducibility of slice angulation and coverage; this may support exams not having to be repeated.
• Iniector coupling is a software application that allows the connection of certain contrast agent injectors to the MR system for simplified, synchronized contrast injection and examination start.
  Lumina onlv:
• Compressed Sensing GRASP-VIBE is intended to be used in dynamic and/or non-contrast liver examinations to support patients who cannot reliably hold their breath for a conventional breath-hold measurement.
  Lumina and Vida Fit:
• Deep Resolve Swift Brain is a protocol for fast routine brain imaging primarily based on echo planar imaging (EPI) pulse sequences. Its main enablers are multi-shot (ms) EPI pulse sequence types and a deep learning-based image reconstruction.
• Deep Resolve Boost is a novel deep learning-based image reconstruction alqorithm for 2D TSE data, which reconstructs images from k-space raw-data.
• BLADE diffusion is a multi-shot imaging method based on TSE or TGSE (when EPI factor > 1) readout and a BLADE trajectory with diffusion preparation to enable diffusion weighted imaging with reduced sensitivity to B0 inhomogeneity and reduced T2 decay caused image blurring.
• HASTE diffusion (HASTE DIFF) is a single-shot imaging method based on TSE readout with diffusion preparation to enable diffusion weighted imaging with reduced sensitivity to B0 inhomogeneity.
  Transferred Modifications for Features and Applications:
  Vida Fit only:
• The AbsoluteShim mode is a shimming procedure based on a 3-echo gradient echo protocol.
• The 3D ASL sequence (tgse_asl) now provides relCBF maps, by implementing an additional M0 scan and performing the corresponding reconstruction method. It also provides BAT maps in multiple inversion time(multi-TI) imaging.
  Lumina and Vida Fit:
• Fast GRE RefScan: A speed-optimized reference scan for GRAPPA and SMS kernel calibration for echo planar imaging pulse sequence types.
• Static Field Correction is a reconstruction option reducing susceptibilityinduced distortions and intensity variations.
• Deep Resolve Sharp is an interpolation algorithm which increases the perceived sharpness of the interpolated images. Functionality is available for different pulse sequence types. (Newly transferred to Vida Fit)
• Deep Resolve Gain is a reconstruction option which improves the SNR of the scanned imaqes. Functionality is available for different pulse sequence types. (Newly transferred to Vida Fit)
• The myExam Angio Advanced Assist provides an assisted and quided workflow for peripheral angiography examination using care bolus. The main advantage of this new workflow is a simplified and improved planning procedure of multi-station peripherical angiography measurements.
  Other transferred Modifications and / or Minor Changes
  Vida Fit only:
• Elastography-AddIn synchronizes settings between the Elastography sequence and the active driver.
• HASTE MoCo is an image-based motion correction in the average-dimension for the HASTE pulse sequence type.
• Coil independent pulse sequences remove the coil information from the pulse sequences and generate this information during run-time from automatic coil detection and localization.
• The Needle Intervention AddIn provides a user interface for workflow improvement of MR-quided needle interventions under real-time imaging conditions. It supports planning a needle trajectory, laser-based localization of the entry point as well as automatic slice positioning.
• The PhaseRev Dot Addin/Component supports the measurement workflow of the user by automatically flipping the direction of the phase encoding gradient.
• The adjustment mode "offcenter" triggers a transmitter adjustment method that is specialized for offcenter imaging. The transmitter adjustment determines the RF voltage that is required to excite a certain B1 field.
  Lumina and Vida Fit:
• TSE MoCo is an image-based motion correction in the average-dimension for the TSE pulse sequence type.
• MR Breast Biopsy is improved with an automatic fiducial detection.

The provided text primarily focuses on the substantial equivalence of the MAGNETOM Lumina and MAGNETOM Vida Fit with syngo MR XA50A to predicate devices. It does not include detailed information regarding specific acceptance criteria, device performance metrics, or the study design (e.g., sample sizes, expert qualifications, ground truth methods) that would typically be found in a clinical or performance study report.

Therefore, I cannot extract the requested information about acceptance criteria and the study proving the device meets them from the given document.

The document states:

• "No additional clinical tests were conducted to support substantial equivalence for the subject devices." (Page 9)
• The primary testing conducted was "Verification and validation" of transferred hardware and software features against "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices / 21 CFR §820.30" (Page 9).
• The conclusion is that "the results from each set of tests demonstrate that the devices perform as intended and are thus substantially equivalent to the predicate devices to which they have been compared." (Page 9).

This indicates that the submission relies on demonstrating equivalence to previously cleared devices through non-clinical verification and validation, rather than presenting a de novo performance study with specific acceptance criteria.

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