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Record W2737269897 · doi:10.1002/mrm.26839

Susceptibility‐induced distortion correction in hyperpolarized echo planar imaging

2017· article· en· W2737269897 on OpenAlexaff
Jack J. Miller, Angus Lau, Damian J. Tyler

Bibliographic record

VenueMagnetic Resonance in Medicine · 2017
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsSunnybrook Health Science Centre
FundersEngineering and Physical Sciences Research CouncilBritish Heart FoundationNational Institute for Health and Care ResearchNvidia
KeywordsDistortion (music)Nuclear magnetic resonanceEcho-planar imagingPhysicsMagnetic resonance imagingEcho (communications protocol)k-spacePlanarSpin echoComputer scienceFourier transformMedicineRadiology

Abstract

fetched live from OpenAlex

Purpose Echo planar imaging is an attractive rapid imaging readout that can image hyperpolarized compounds in vivo. By alternating the sign of the phase encoding gradient waveform, spatial offsets arising from uncertain frequency shifts can be determined. We show here that blip‐reversed echo planar imaging can also be used to correct for susceptibility and B 0 inhomogeneity effects that would otherwise produce image‐domain distortion in the heart. Methods Previously acquired blip‐reversed cardiac 3D‐Spectral‐Spatial echo planar imaging volumetric timecourses of hyperpolarized [1‐ 13 C]pyruvate were distortion corrected by a deformation field estimated by reconstructing signal‐to‐noise ratio (SNR)‐weighted progressively subsampled temporally summed images of each metabolite. Results Reconstructing blip‐reversed data as proposed produced volumetric timecourses that overlaid with proton reference images more consistently than without such corrections. Conclusion The method proposed may form an attractive method to correct for image‐domain distortions in hyperpolarized echo planar imaging experiments. Magn Reson Med 79:2135–2141, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.311
Teacher spread0.291 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations19
Published2017
Admission routes1
Has abstractyes

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