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Record W2094291606 · doi:10.1088/1742-6596/3/1/008

Contrast mechanisms in magnetic resonance imaging

2004· article· en· W2094291606 on OpenAlexaff
Martin Lepage, John C. Gore

Bibliographic record

VenueJournal of Physics Conference Series · 2004
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsDosimeterIrradiationPolymerRelaxation (psychology)Materials scienceNuclear magnetic resonanceIonizing radiationDosimetryPolymerizationProtonMagnetic resonance imagingRadiochemistryRadiationChemistryOpticsNuclear physicsNuclear medicinePhysicsRadiology

Abstract

fetched live from OpenAlex

The first publications describing gel dosimetry used magnetic resonance imaging to detect changes in the proton longitudinal relaxation rate of a gel infused with ferrous ions and irradiated with ionizing radiation. Later, different gel dosimeter systems were proposed that are based on the free-radical polymerization of monomers dispersed in a gel matrix. In these polymer gels, changes in transverse relaxation rates were shown to be dependent on the absorbed dose. More recently, contrast in MR images based on the exchange of magnetization between polymer and water protons, following saturation of the polymer protons, has been exploited in polymer gel dosimeters. In addition, variations in relaxation times in the rotating frame (T1ρ) have been shown to produce contrast in MR images of irradiated polymer gel dosimeters. The signal and contrast in MR images may be manipulated to reflect a variety of these and other processes within an irradiated sample, An attempt is made here to provide an overview of the main different types of MRI contrast that may be used in gel dosimetry and, where possible, to relate this contrast to the nature of the chemical processes and structural changes that occur within the gels following the absorption of ionizing radiation.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.293

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)0.0000.000

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 head0.015
GPT teacher head0.282
Teacher spread0.266 · 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 designTheoretical or conceptual
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

Citations17
Published2004
Admission routes1
Has abstractyes

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