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Record W4394060141 · doi:10.1002/nbm.5151

Robust dual‐angle T1 measurement in magnetization transfer spectroscopy by time‐optimal control

2024· article· en· W4394060141 on OpenAlexaff
Christina Graf, Rudolf Stollberger, Armin Rund, Martina Schweiger, Clemens Diwoky

Bibliographic record

VenueNMR in Biomedicine · 2024
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of British Columbia
FundersAustrian Science Fund
KeywordsMagnetizationMagnetization transferSpectroscopyDual (grammatical number)Materials scienceNuclear magnetic resonance spectroscopyTransfer (computing)Nuclear magnetic resonancePhysicsComputer scienceMagnetic fieldMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Summary Magnetization transfer spectroscopy relies heavily on the robust determination of relaxation times of nuclei participating in metabolic exchange. Challenges arise due to the use of surface RF coils for transmission (high variation) and the broad resonance band of most X nuclei. These challenges are particularly pronounced when fast mapping methods, such as the dual‐angle method, are employed. Consequently, in this work, we develop resonance offset and robust excitation RF pulses for 31 P magnetization transfer spectroscopy at 7T through ensemble‐based time‐optimal control. In our approach, we introduce a cost functional for designing robust pulses, incorporating the full Bloch equations as constraints, which are solved using symmetric operator splitting techniques. The optimal control design of the RF pulses developed demonstrates improved accuracy, desired phase properties, and reduced RF power when applied to dual‐angle mapping, thereby improving the precision of exchange‐rate measurements, as demonstrated in a preclinical in vivo study quantifying brain creatine kinase activity.

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: none
Teacher disagreement score0.931
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.284
Teacher spread0.264 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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