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Spatial and Dosimetric accuracy of 3D polymer gel with CBCT readout - Varian HyperArc® SRS implementation

2023· article· en· W4388698807 on OpenAlexaff
Tenzin Kunkyab, Michelle Hilts, Andrew Jirasek, Derek Hyde

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsKelowna General HospitalUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsDosimetryCone beam computed tomographyRadiosurgeryMaterials scienceNuclear medicineRadiation treatment planningMedical physicsBiomedical engineeringRadiation therapyComputed tomographyMedicineRadiology

Abstract

fetched live from OpenAlex

Abstract 3D polymer gel dosimetry is a promising means to verify complex radiation treatments such as stereotactic radiosurgery (SRS), as it provides both 3D dosimetric and spatial information. The purpose of the study is to use a polymer gel read-out with cone-beam computed tomography (CBCT) to commission Varian’s HyperArc ® -treatment planning and delivery. Three targets (3 cm, 2 cm, 1 cm diameter respectively) were defined on a treatment plan with a maximum dose of 25 Gy, resembling a single isocentre, multiple-lesion SRS plan. Pre- and post-irradiation CBCT images of the gel were obtained for dosimetry analysis. One slice containing two large targets was used to self-calibrate the entire gel volume for dose comparison. We were able to achieve sub-millimeter spatial accuracy and all evaluated gamma criterion (5% 1mm, 3% 1mm, 2% 1 mm were > 95%). In summary, in this study we have demonstrated that CBCT polymer gel dosimetry can be a highly valuable tool for commissioning complex radiation treatment techniques such as SRS.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.301
Teacher spread0.284 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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