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Record W1409354379 · doi:10.1118/1.4924969

SU‐E‐T‐606: Performance of MR‐Based 3D FXG Dosimetry for Preclinical Irradiation

2015· article· en· W1409354379 on OpenAlexaffabout
Mattea Welch, David A. Jaffray

Bibliographic record

VenueMedical Physics · 2015
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsDosimetryNuclear medicineIrradiationMaterials scienceMedical imagingBiomedical engineeringMedicineRadiologyPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Purpose: Technological advances have revolutionized preclinical radiation research to enable precise radiation delivery in preclinical models. Kilovoltage x‐rays and complex geometries in preclinical radiation studies challenge conventional dosimetry methods. Previously developed gel‐based dosimetry provides a viable means of accommodating complex geometries and accurately reporting dose at kV energies. This paper will describe the development and evaluation of gel‐based ferrous xylenol‐orange (FXG) dosimetry using a 7T preclinical imaging system. Methods: To confirm water equivalence, Zeff values were calculated for the FXG material, water and ICRU defined soft tissue. Proton T1 relaxivity response in FXG was measured using a preclinical 7T MR and a small animal irradiator for a dose range of 1–22 Gy. FXG was contained in 50 ml centrifuge tubes and irradiated with a 225 kVp x‐ray beam at a nominal dose rate of 2.3 Gy/min. Pre and post irradiation maps of the T1 relaxivity were collected using variable TR spin‐echo imaging (TE 6.65 ms; TR 500, 750, 1000, 1500, 2000, 3000 and 5000 ms) with 2 mm thick slices, 0.325 mm/pixel, 3 averages and an acquisition time of 26 minutes. A linear fit to the change in relaxation rate (1/T1) for the delivered doses reported the gel sensitivity in units of ms‐1Gy‐1. Irradiation and imaging studies were repeated using three batches of gel over 72 hrs. Results: FXG has a Zeff of 3.8 for the 225 kVp spectrum used; differing from water and ICRU defined soft tissue by 0.5% and 2.5%, respectively. The average sensitivity for the FXG dosimeter was 31.5 ± 0.7 ms‐1Gy‐1 (R2 = 0.9957) with a y‐intercept of −29.4 ± 9.0 ms‐1. Conclusion: Preliminary results for the FXG dosimeter properties, sensitivity, and dose linearity at preclinical energies is promising. Future work will explore anatomically relevant tissue inclusions to test MR performance. Student funding provided by The Terry Fox Foundation Strategic Initiative for Excellence in Radiation Research for the 21st Century at CIHR and the Gifford Ontario Student Opportunity Trust Fund.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.072
GPT teacher head0.400
Teacher spread0.328 · 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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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