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Record W2973892278 · doi:10.1002/mp.13818

Dose–response linearization in radiochromic film dosimetry based on multichannel normalized pixel value with an integrated spectral correction for scanner response variations

2019· article· en· W2973892278 on OpenAlexafffund
Saad Aldelaijan, Slobodan Dević, Pavlos Papaconstadopoulos, Hamed Bekerat, Robert A. Cormack, Jan Seuntjens, Ivan Buzurović

Bibliographic record

VenueMedical Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsMcGill UniversityJewish General HospitalMontreal Neurological Institute and Hospital
FundersNatural Sciences and Engineering Research Council of CanadaKing Faisal Specialist Hospital and Research Centre
KeywordsScannerDosimetryCalibrationImaging phantomOpticsPixelNoise (video)Absorbed doseComputer scienceMaterials sciencePhysicsMathematicsNuclear medicineComputer visionStatisticsImage (mathematics)Medicine

Abstract

fetched live from OpenAlex

Purpose To introduce a model that reproducibly linearizes the response from radiochromic film (RCF) dosimetry systems at extended dose range. To introduce a correction method, generated from the same scanned images, which corrects for scanner temporal response variation and scanner bed inhomogeneity. Methods Six calibration curves were established for different lot numbers of EBT3 GAFCHROMIC™ film model based on four EPSON scanners [10000XL (2 units), 11000XL, 12000XL] at three different centers. These films were calibrated in terms of absorbed dose to water based on TG51 protocol or TRS398 with dose ranges up to 40 Gy. The film response was defined in terms of a proposed normalized pixel value ( ) as a summation of first‐order equations based on information from red, green, and blue channels. The fitting parameters of these equations are chosen in a way that makes the film response equal to dose at the time of calibration. An integrated set of correction factors (one per color channel) was also introduced. These factors account for the spatial and temporal changes in scanning states during calibration and measurements. The combination of and this “fingerprint” correction formed the basis of this new protocol and it was tested against net optical density ( ) single‐channel dosimetry in terms of accuracy, precision, scanner response variability, scanner bed inhomogeneity, noise, and long‐term stability. Results Incorporating multichannel features (RGB) into the normalized pixel value produced linear response to absorbed dose (slope of 1) in all six RCF dosimetry systems considered in this study. The “fingerprint” correction factors of each of these six systems displayed unique patterns at the time of calibration. The application of to all of these six systems could achieve a level of accuracy of ± 2.0% in the dose range of interest within modeled uncertainty level of 2.0%–3.0% depending on the dose level. Consistent positioning of control and measurement film pieces and integrating the multichannel correction into the response function formalism mitigated possible scanner response variations of as much as ± 10% at lower doses and scanner bed inhomogeneity of ± 8% to the established level of uncertainty at the time of calibration. The system was also able to maintain the same level of accuracy after 3 and 6 months post calibration. Conclusions Combining response linearity with the integrated correction for scanner response variation lead to a sustainable and practical RCF dosimetry system that mitigated systematic response shifts and it has the potential to reduce errors in reporting relative information from the film response.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.273
Teacher spread0.267 · 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
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".

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Citations11
Published2019
Admission routes2
Has abstractyes

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