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Record W4207044841 · doi:10.1002/mp.15487

Dose calculations for preclinical radiobiology experiments conducted with single‐field cabinet irradiators

2022· article· en· W4207044841 on OpenAlexaff
Courage Mahuvava, Nolan Esplen, Yannick Poirier, Stephen F. Kry, Magdalena Bazalova‐Carter

Bibliographic record

VenueMedical Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDosimetryMonte Carlo methodCollimated lightField sizeMaterials scienceImaging phantomNuclear medicineOpticsBiomedical engineeringPhysicsMathematicsMedicine

Abstract

fetched live from OpenAlex

PURPOSE: To provide percentage depth dose (PDD) data along the central axis for dosimetry calculations in small-animal radiation biology experiments performed in cabinet irradiators. The PDDs are provided as a function of source-to-surface distance (SSD), field size, and animal size. METHODS: The X-ray tube designs for four biological cabinet irradiators, the RS2000, RT250, MultiRad350, and XRAD320, were simulated using the BEAMnrc Monte Carlo code to generate 160, 200, 250, and 320 kVp photon beams, respectively. The 320 kVp beam was simulated with two filtrations: a soft F1 aluminium filter and a hard F2 thoraeus filter made of aluminium, tin, and copper. Beams were collimated into circular fields with diameters of 0.5-10 cm at SSDs of 10-60 cm. Monte Carlo dose calculations in 1-5-cm diameter homogeneous (soft tissue) small-animal phantoms as well as in heterogeneous phantoms with 3-mm diameter cylindrical lung and bone inserts (rib and cortical bone) were performed using DOSXYZnrc. The calculated depth doses in three test-cases were estimated by applying SSD, field size, and animal size correction factors to a reference case (40-cm SSD, 1-cm field, and 5-cm animal size), and these results were compared with the specifically simulated (i.e., expected) doses to assess the accuracy of this method. Dosimetry for two test-case scenarios of 160 and 250 kVp beams (representative of end-user beam qualities) was also performed, whereby the simulated PDDs at two different depths were compared with the results based on the interpolation from reference data. RESULTS: The depth doses for three test-cases calculated at 200, 320 kVp F1, and 320 kVp F2 with half value layers (HVLs) ranging from ∼0.6 to 3.6 mm Cu, agreed well with the expected doses, yielding dose differences of 1.2%, 0.1%, and 1.0%, respectively. The two end-user test-cases for 160 and 250 kVp beams with respective HVLs of ∼0.8 and 1.8 mm Cu yielded dose differences of 1.4% and 3.2% between the simulated and the interpolated PDDs. The dose increase at the bone-tissue proximal interface ranged from 1.2 to 2.5 times the dose in soft tissue for rib and 1.3 to 3.7 times for cortical bone. The dose drop-off at 1-cm depth beyond the bone ranged from 1.3% to 6.0% for rib and 3.2% to 11.7% for cortical bone. No drastic dose perturbations occurred in the presence of lung, with lung-tissue interface dose of >99% of soft tissue dose and <3% dose increase at 1-cm depth beyond lung. CONCLUSIONS: The developed dose estimation method can be used to translate the measured dose at a point to dose at any depth in small-animal phantoms, making it feasible for preclinical calculation of dose distributions in animals irradiated with cabinet-style irradiators. The dosimetric impact of bone must be accurately quantified as dramatic dose perturbations at and beyond the bone interfaces can occur due to the relative importance of the photoelectric effect at kilovoltage energies. These results will help improve dosimetric accuracy in preclinical experiments.

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)0.002
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.034
GPT teacher head0.350
Teacher spread0.316 · 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".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractyes

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