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Record W2565105442 · doi:10.1002/mp.12080

Evaluation of accuracy and precision in polymer gel dosimetry

2016· article· en· W2565105442 on OpenAlexafffundabout
Evan Maynard, Michelle Hilts, Emily Heath, Andrew Jirasek

Bibliographic record

VenueMedical Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaOkanagan University CollegeCarleton UniversityBC Cancer AgencyUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDosimeterDosimetryCalibrationMonte Carlo methodCalibration curveReproducibilityMaterials scienceNuclear medicineBiomedical engineeringOpticsPhysicsMathematicsMedicineStatisticsDetection limit

Abstract

fetched live from OpenAlex

PURPOSE: To assess the overall reproducibility and accuracy of an X-ray computed tomography (CT) polymer gel dosimetry (PGD) system and investigate what effects the use of generic, interbatch, and intrabatch gel calibration have on dosimetric and spatial accuracy. METHODS: A N-isopropylacrylamide (NIPAM)-based gel formulation optimized for X-ray CT gel dosimetry was used, and the results over four different batches of gels were analyzed. All gels were irradiated with three 6 MV beams in a calibration pattern at both the bottom and top of the dosimeter. Postirradiation CT images of the gels were processed using background subtraction, image averaging, adaptive mean filtering, and remnant artifact removal. The gel dose distributions were calibrated using a Monte Carlo (Vancouver Island Monte Carlo system) calculated dose distribution of the calibration pattern. Using the calibration results from all gels, an average or "generic" calibration curve was calculated and this generic calibration curve was used to calibrate each of the gels within the sample. For each of the gels, the irradiation pattern at the bottom of the dosimeter was also calibrated using the irradiation pattern at the top of the dosimeter to evaluate intragel calibration. RESULTS: Comparison of gel measurements with Monte Carlo dose calculations found excellent dosimetric accuracy when using an average (or generic) calibration with a mean dose discrepancy of 1.8% in the low-dose gradient region which compared to a "best-case scenario" self-calibration method with a mean dose discrepancy of 1.6%. The intragel calibration method investigated produced large dose discrepancies due to differences in dose response at the top and bottom of the dosimeter, but the use of a dose-dependent correction reduced these dose errors. Spatial accuracy was found to be excellent for the average calibration method with a mean distance-to-agreement (DTA) of 0.63 mm and 99.6% of points with a DTA < 2 mm in high-dose gradient regions. This compares favorably to the self-calibration method which produced a mean DTA of 0.61 mm and 99.8% of points with a DTA < 2 mm. Gamma analysis using a 3%/3 mm criterion also found good agreement between the gel measurement and Monte Carlo dose calculation when using either the average calibration or self-calibration methods (96.8% and 98.2%, respectively). CONCLUSIONS: An X-ray CT PGD system was evaluated and found to have excellent dosimeteric and spatial accuracy when compared to Monte Carlo dose calculations and the use of generic and interbatch calibration methods were found to be effective. The establishment of the accuracy and reproducibility of this system provides important information for clinical implementation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.342
Teacher spread0.323 · 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 designOther design
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

Citations27
Published2016
Admission routes3
Has abstractyes

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