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NMR mechanisms in gel dosimetry

2009· article· en· W2136975184 on OpenAlexaff
L J Schreiner

Bibliographic record

VenueJournal of Physics Conference Series · 2009
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsRelaxometryDosimetryDosimeterMedical physicsRelaxation (psychology)RadiochemistryNuclear magnetic resonanceMagnetic resonance imagingMaterials scienceNuclear medicineChemistryMedicinePhysicsRadiologySpin echo

Abstract

fetched live from OpenAlex

Nuclear magnetic resonance was critical to the development of gel dosimetry, as it established the potential for three dimensional dosimetry with chemical dosimeter systems through magnetic resonance imaging [1]. In the last two decades MRI has served as the gold standard for imaging, while NMR relaxometry has played an important role in the development and understanding of the behaviour of new gel dosimetry systems. Therefore, an appreciation of the relaxation mechanisms determining the NMR behaviour of irradiated gel dosimeters is important for a full comprehension of a considerable component of the literature on gel dosimetry. A number of excellent papers have presented this important theory, this brief review will highlight some of the salient points made previously [1–5]. The spin relaxation of gel dosimeters (which determines the dose dependence in most conventional MR imaging) is determined principally by the protons on water molecules in the system. These water protons exist in different environments, or groups (see Figure 1): on bulk water, on water hydrating the chemical species that are being modified under irradiation, and on water hydrating the gel matrix used to spatially stabilize the dosimeter (e.g., gelatin, agarose, etc). The spin relaxation depends on the inherent relaxation rate of each spin group, that is, on the relaxation rate which would be observed for the specific group if it were isolated. Also, the different water environments are not isolated from each other, and the observed relaxation rate also depends on the rate of exchange of magnetization between the groups, and on the fraction of protons in each group. In fact, the water exchanges quickly between the environments, so that relaxation is in what is usually termed the fast exchange regime. In the limit of fast exchange, the relaxation of the water protons is well characterized by a single exponential and hence by a single apparent relaxation rate. In irradiated gel dosimeters this observed rate is a function of the absorbed dose. In this review I will first develop the fast exchange model for the spin lattice relaxation Fricke gel dosimeter system, as this is conceptually the simpler system. Fundamental concepts such as relaxivity (which specifies the ability of some species to enhance the relaxation of water protons) and chemical yield will be presented. The linear dose relationship for Fricke gel dosimeters, and the reduced dose sensitivity of Fricke dosimeters containing chelators, will be explained. The model will then be extended to the spin-spin relaxation of polymer gel systems and the main differences discussed. The reasons for the enhanced dose sensitivy and dynamic range for spin-spin (R2) rather than spin-lattice (R1) relaxation will be presented.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.004
Scholarly communication0.0010.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.023
GPT teacher head0.312
Teacher spread0.289 · 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
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".

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Citations4
Published2009
Admission routes1
Has abstractyes

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