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Record W4409958733 · doi:10.1002/nbm.70047

Compressed Sensing‐Accelerated Free‐Breathing Liver MRI at 7 T

2025· article· en· W4409958733 on OpenAlexaff
Mitra Tavakkoli, Bobby A. Runderkamp, Matthijs H. S. de Buck, Gustav J. Strijkers, Michael D. Noseworthy, Aart J. Nederveen, Matthan W.A. Caan, Wietske van der Zwaag

Bibliographic record

VenueNMR in Biomedicine · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekInternational Society for Magnetic Resonance in Medicine
Keywordsk-spaceImage qualityComputer scienceMagnetic resonance imagingReal-time MRIIterative reconstructionImage resolutionCompressed sensingNuclear medicineArtificial intelligenceComputer visionMedicineImage (mathematics)Radiology

Abstract

fetched live from OpenAlex

ABSTRACT Ultra‐high field MRI facilitates imaging at high spatial resolutions, which may become important for detailed anatomical and pathological assessment of the human liver. Therefore, we aimed to advance structural liver imaging at 7 T by implementing a high‐resolution, phase‐shimmed, free‐breathing liver scan. Six healthy participants underwent liver MRI scans at 7 T, utilizing an eight‐channel parallel transmission system for phase shimming. B0 mapping and Fourier phase‐encoded dual refocusing echo acquisition mode (PE‐DREAM) multichannel B1+ mapping were performed during breath‐holds at expiration. Prospectively undersampled golden‐angle pseudo‐spiral k‐space data were acquired under free breathing, enabling retrospective respiratory binning using self‐gating. Post‐binning, the simultaneous autocalibrating and k‐space estimation (SAKE) algorithm was employed for interpolation of a center of k‐space area, prior to estimation of receive coil sensitivity maps. Image reconstruction was performed on expiration‐phase data using compressed sensing, optimizing image quality by evaluating various regularization factors and numbers of respiratory bins. Finally, N4BiasFieldCorrection was applied to the resulting images. Expiration‐phase image reconstruction using four bins and regularization factor values of 10−2.5 (1.50 mm) and 10−2.33 (1.35 mm) were found to optimize the tradeoff between sharpness, SNR, and artifacts. The optimized protocol facilitated clear visualization of the liver, blood vessels, and surrounding structures at isotropic resolutions of 1.50 and 1.35 mm in 3.5 min, without B1+ inhomogeneity effects in the shimmed liver region. A comparison between low‐resolution fully sampled free‐breathing (3.5 min) and breath‐hold (19 s) acquisitions demonstrated comparable sharpness and SNR. To compare the 7 T data with 3 T MRI, 3 T scans were performed for two participants. 3 T reconstructions were done similarly to 7 T, excluding N4BiasFieldCorrection. Scan‐specific regularization optimization was performed for fair comparison. Compared to 3 T, 7 T showed superior vascular contrast with inflow effects not observed at 3 T. Fold‐over artifacts were present in 3 T scans but were minor at 7 T. 3 T and 7 T provided comparable results, with a much higher RF channel count at 3 T. In conclusion, high‐resolution expiration‐phase liver imaging at 7 T with homogeneous signal can be successfully achieved using a phase‐shimmed, free‐breathing protocol with a golden‐angle pseudo‐spiral sampling pattern technique and respiratory self‐gating. This approach allows detailed anatomical depiction without the limitations of breath‐holding, representing a significant advancement in ultra‐high field abdominal MRI.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.339
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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