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Record W3013911346 · doi:10.1002/nbm.4299

Demonstration and suppression of respiration‐related artifacts in Bloch–Siegert shift‐based <i>B</i><sub>1</sub><sup>+</sup> maps of the human brain

2020· article· en· W3013911346 on OpenAlexafffund
Omer Oran, L. Martyn Klassen, Haçène Serrai, Ravi S. Menon

Bibliographic record

VenueNMR in Biomedicine · 2020
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsRespirationPhysicsNuclear magnetic resonanceHuman brainNeuroscienceMedicineBiologyAnatomy

Abstract

fetched live from OpenAlex

Respiration‐induced movement of the chest wall and internal organs causes temporal B 0 variations extending throughout the brain. This study demonstrates that these variations can cause significant artifacts in maps obtained at 7 T with the Bloch–Siegert shift (BSS) mapping technique. To suppress these artifacts, a navigator correction scheme was proposed. Two sets of experiments were performed. In the first set of experiments, phase shifts induced by respiration‐related B 0 variations were assessed for five subjects at 7 T by using a gradient echo (GRE) sequence without phase‐encoding. In the second set of experiments, maps were acquired using a GRE‐based BSS pulse sequence with navigator echoes. For this set, the measurements were consecutively repeated 16 times for the same imaging slice. These measurements were averaged to obtain the reference map. Due to the periodicity of respiration‐related phase shifts, their effect on the reference map was assumed to be negligible through averaging. The individual maps of the 16 repetitions were calculated with and without using the proposed navigator scheme. These maps were compared with the reference map. The peak‐to‐peak value of respiration‐related phase shifts varied between subjects. Without navigator correction, the interquartile range of percentage error in varied between 4.0% and 8.3% among subjects. When the proposed navigator scheme was used, these numbers were reduced to 2.5% and 2.9%, indicating an improvement in the precision of GRE‐based BSS mapping at high magnetic fields.

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)0.001
metaresearch head score (Gemma)0.006
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.033
GPT teacher head0.266
Teacher spread0.232 · 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
Published2020
Admission routes2
Has abstractyes

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