MAGNETOM Terra; MAGNETOM Terra.X

K232322

Siemens Medical Solutions USA, Inc. · cleared 2024-03-22 · product code LNH · Radiology

Premarket evidence — what FDA accepted

Device typehardware with ml

source quote (p.7)

“Deep Resolve Boost is a novel deep learning-based image reconstruction algorithm for 2D TSE data, which reconstructs images from k-space raw-data.”

AlgorithmDeep Resolve Boost is a novel deep learning-based image reconstruction algorithm for 2D TSE data. Deep Resolve Gain is a reconstruction option which enables targeted denoising. Deep Resolve Sharp is a deep learning-based interpolation algorithm. Bias field correction (marketing name: Deep RxE) is a deep learning image filter.

source quote (p.7)

“Deep Resolve Boost is a novel deep learning-based image reconstruction algorithm for 2D TSE data, which reconstructs images from k-space raw-data. Deep Resolve Gain is a reconstruction option which enables targeted denoising, resulting in improved SNR of the scanned images. The functionality is available for specific pulse sequence types now. Deep Resolve Sharp is a deep learning-based interpolation algorithm which increases the perceived sharpness of the interpolated images. The functionality has been ported from the reference device MAGNETOM Vida to the subject devices MAGNETOM Terra and MAGNETOM Terra.X. Bias field correction (marketing name: Deep RxE) is a deep learning image filter.”

Adaptive (vs locked)FDA source did not state this

PCCPFDA source did not state this

Cybersecurity addressedYes

source quote (p.8)

“Provide secure MR scanner setup for DoD (Department of Defense) Information Assurance compliance.”

Validation studies (5)

Retrospective clinical

n=26,473 images

endpoints: peak signal-to-noise ratio (PSNR); structural similarity index (SSIM); visual inspection for potential artefacts

Retrospective clinical

n=13,977 images

endpoints: peak signal-to-noise ratio (PSNR); structural similarity index (SSIM); perceptual loss; visual rating; image sharpness by intensity profile comparisons

Retrospective clinical

n=143,947 images

endpoints: normalized root mean square error (NRMSE); standard deviation; RMS error; image homogeneity; image quality rating

Prospective clinical

n=35 patients

endpoints: nerve stimulation thresholds

standards: IEC 60601-2-33

Reader study (MRMC)

sample size not stated

endpoints: evaluation of observed artifacts and concerns

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

100

recalls in product code, 24mo

510

MAUDE reports in code, 12mo

+5%

vs code's own 3-yr baseline

drift signals on this device

recall_reason_pattern
Software/algorithm-related recall in product code LNH (Philips North America, initiated 2026-04-14): "The potential for stiffness value errors when a specific range of image reconstruction parameters is used in combination with Resoundant's algorithm, leading to the reconstruction " Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:98779
recall_reason_pattern
Software/algorithm-related recall in product code LNH (Philips North America, initiated 2025-12-03): "The potential for stiffness value errors when viewing exported MR Elastography (MRE) stiffness maps to viewer Picture Archiving and Communication System (PACS)." Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:98111

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).