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Record W4241057937 · doi:10.1002/9781118633953.ch20

Magnetic Field Inhomogeneity Effects and <i>T</i> <sub>2</sub> * Dephasing

2014· other· en· W4241057937 on OpenAlexaff
Robert W. Brown, Yu‐Chung N. Cheng, E. Mark Haacke, Michael R. Thompson, Ramesh Venkatesan

Bibliographic record

VenueMagnetic Resonance Imaging · 2014
Typeother
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDephasingVoxelDistortion (music)PhysicsField (mathematics)SIGNAL (programming language)Image (mathematics)Magnetic fieldNuclear magnetic resonanceComputer scienceComputational physicsStatistical physicsArtificial intelligenceMathematicsCondensed matter physicsQuantum mechanics

Abstract

fetched live from OpenAlex

This chapter discusses the image effects due to magnetic field inhomogeneities at the macroscopic level, where their scale is at least voxel sized, and the microscopic regime, where the volumes are much smaller than a voxel. It considers the effects of static field inhomogeneities on the image. Common artifacts of image distortion are analyzed in the linear field approximation. The chapter reviews the echo shifting and its effect on image phase. It describes the procedures for the reduction of signal loss and distortion. It introduces the examples of T2* signal loss and a means to predict T2* from susceptibility producing objects. The chapter discusses the method to correct geometric distortion.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.004
GPT teacher head0.247
Teacher spread0.243 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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