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Spectroscopic lineshape correction by QUECC: Combined QUALITY deconvolution and eddy current correction

2000· article· en· W2058020816 on OpenAlexaff
Robert Bartha, Dick Drost, Ravi S. Menon, Peter Williamson

Bibliographic record

VenueMagnetic Resonance in Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsWestern UniversityRobarts Clinical Trials
FundersDivision of Electrical, Communications and Cyber Systems
KeywordsDeconvolutionResidualSIGNAL (programming language)Eddy currentNuclear magnetic resonanceDistortion (music)ChemistryPhysicsComputational physicsComputer scienceOpticsAlgorithm

Abstract

fetched live from OpenAlex

Lineshape distortion due to residual eddy currents and magnetic field inhomogeneities are often present in short echo time (1)H spectroscopic data. Lineshape correction methods such as QUALITY deconvolution and eddy current correction (ECC), which use a separate reference spectrum for lineshape correction, have shortcomings when unsuppressed water is chosen as the reference. This paper outlines a method of integrating both techniques to overcome these limitations while still using unsuppressed water as the reference signal. This hybrid lineshape correction technique (QUECC) is demonstrated in vivo using stimulated echo acquisition mode (STEAM) localized 4.0 Tesla data. Metabolite quantification precision increased by an average of 7%-46% compared to QUALITY deconvolution (depending on filtering) and by an average of 6% compared to ECC.

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)0.003
metaresearch head score (Gemma)0.008
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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.352
Teacher spread0.333 · 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
GenreMethods

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

Citations90
Published2000
Admission routes1
Has abstractyes

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