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Record W3092696183 · doi:10.1002/mp.14540

Positional and angular tracking of HDR <sup>192</sup>Ir source for brachytherapy quality assurance using radiochromic film dosimetry

2020· article· en· W3092696183 on OpenAlexafffund
Saad Aldelaijan, Slobodan Dević, Hamed Bekerat, Pavlos Papaconstadopoulos, James Schneider, Jan Seuntjens, Robert A. Cormack, Ivan Buzurović

Bibliographic record

VenueMedical Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsMcGill UniversityJewish General HospitalMontreal Neurological Institute and Hospital
FundersKing Faisal Specialist Hospital and Research CentreNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsDosimetryBrachytherapyImaging phantomQuality assuranceTracking (education)Orientation (vector space)Computer sciencePhysicsNuclear medicineOpticsMaterials scienceMathematicsGeometryRadiation therapy

Abstract

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Purpose To quantify and verify the dosimetric impact of high‐dose rate (HDR) source positional uncertainty in brachytherapy, and to introduce a model for three‐dimensional (3D) position tracking of the HDR source based on a two‐dimensional (2D) measurement. This model has been utilized for the development of a comprehensive source quality assurance (QA) method using radiochromic film (RCF) dosimetry including assessment of different digitization uncertainties. Methods An algorithm was developed and verified to generate 2D dose maps of the mHDR‐V2 192Ir source (Elekta, Veenendaal, Netherlands) based on the AAPM TG‐43 formalism. The limits of the dosimetric error associated with source (0.9 mm diameter) positional uncertainty were evaluated and experimentally verified with EBT3 film measurements for 6F (2.0 mm diameter) and 4F (1.3 mm diameter) size catheters at the surface (4F, 6F) and 10 mm further (4F only). To quantify this uncertainty, a source tracking model was developed to incorporate the unique geometric features of all isodose lines (IDLs) within any given 2D dose map away from the source. The tracking model normalized the dose map to its maximum, then quantified the IDLs using blob analysis based on features such as area, perimeter, weighted centroid, elliptic orientation, and circularity. The Pearson correlation coefficients (PCCs) between these features and source coordinates (x, y, z, θy, θz) were calculated. To experimentally verify the accuracy of the tracking model, EBT3 film pieces were positioned within a Solid Water® (SW) phantom above and below the source and they were exposed simultaneously. Results The maximum measured dosimetric variations on the 6F and 4F catheter surfaces were 39.8% and 36.1%, respectively. At 10 mm further, the variation reduced to 2.6% for the 4F catheter which is in agreement with the calculations. The source center (x, y) was strongly correlated with the low IDL‐weighted centroid (PCC = 0.99), while the distance to source (z) was correlated with the IDL areas (PCC = 0.96) and perimeters (PCC = 0.99). The source orientation θy was correlated with the difference between high and low IDL‐weighted centroids (PCC = 0.98), while θz was correlated with the elliptic orientation of the 60–90% IDLs (PCC = 0.97) for a maximum distance of z = 5 mm. Beyond 5 mm, IDL circularity was significant, therefore limiting the determination of θz (PCC ≤ 0.48). The measured positional errors from the film sets above and below the source indicated a source position at the bottom of the catheter (−0.24 ± 0.07 mm). Conclusions Isodose line features of a 2D dose map away from the HDR source can reveal its spatial coordinates. RCF was shown to be a suitable dosimeter for source tracking and dosimetry. This technique offers a novel source QA method and has the potential to be used for QA of commercial and customized applicators.

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)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.320
Teacher spread0.297 · 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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Citations6
Published2020
Admission routes2
Has abstractyes

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