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Record W2792098451 · doi:10.1002/mrm.27136

Simultaneous multislice acquisition without trajectory modification for hyperpolarized <sup>13</sup>C experiments

2018· article· en· W2792098451 on OpenAlexafffund
Angus Lau, Justin Y. C. Lau, Albert P. Chen, Charles H. Cunningham

Bibliographic record

VenueMagnetic Resonance in Medicine · 2018
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsMultisliceImaging phantomScannerNuclear magnetic resonancePulse sequenceImage resolutionPhysicsImage qualityAccelerationNuclear medicineSpiral (railway)OpticsIterative reconstructionBiomedical engineeringMaterials scienceComputer scienceArtificial intelligenceImage (mathematics)MathematicsMedicine

Abstract

fetched live from OpenAlex

Purpose To investigate the feasibility of performing large FOV hyperpolarized 13 C metabolic imaging using simultaneous multislice excitation. Methods A spectral‐spatial multislice excitation pulse was constructed by cosine modulation and incorporated into a 13 C spiral imaging sequence. Phantom and in vivo pig experiments were performed to test the feasibility of simultaneous multislice data acquisition and image reconstruction. In vivo cardiac‐gated images of hyperpolarized pyruvate, bicarbonate, and lactate were obtained at 1 × 1 × 1 cm 3 resolution over a 48 × 48 × 24 cm 3 FOV with 2‐fold acceleration in the slice direction. Sensitivity encoding was used for image reconstruction with both autocalibrated and numerically calculated coil sensitivities. Results Simultaneous multislice images obtained with 2‐fold acceleration were comparable to reference unaccelerated images. Retained SNR figures greater than 80% were achieved over the part of the image containing the heart. Conclusion This method is anticipated to enable large FOV imaging studies using hyperpolarized 13 C substrates, with an aim toward whole‐body exams that have to date been out of reach.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.847

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)0.0010.000

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 head0.026
GPT teacher head0.332
Teacher spread0.306 · 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 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

Citations11
Published2018
Admission routes2
Has abstractyes

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