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Record W2730130938 · doi:10.1002/mp.12464

A Monte Carlo study of the effect of an ultrasound transducer on surface dose during intrafraction motion imaging for external beam radiation therapy

2017· article· en· W2730130938 on OpenAlexfundno aff
Michael Martyn, Tuathan O’Shea, Emma Harris, Jeffrey C. Bamber, Stephen Gilroy, Mark Foley

Bibliographic record

VenueMedical Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsnot available
FundersInstitute of Cancer ResearchIrish Centre for High-End ComputingCancer Research UK
KeywordsImaging phantomMonte Carlo methodBeam (structure)IsocenterVoxelNuclear medicineCalibrationDosimetryRadiation therapyOpticsUltrasoundMaterials sciencePhysicsMedicineRadiologyMathematics

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study was to estimate changes in surface dose due to the presence of the Clarity Autoscan™ ultrasound (US) probe during prostate radiotherapy using Monte Carlo (MC) methods. METHODS: MC models of the Autoscan US probe were developed using the BEAMnrc/DOSXYZnrc code based on kV and MV CT images. CT datasets were converted to voxelized mass density phantoms using a CT number-to-mass density calibration. The dosimetric effect of the probe, in the contact region (an 8 mm × 12 mm single layer of voxels), was investigated using a phantom set-up mimicking two scenarios (a) a transperineal imaging configuration (radiation beam perpendicular to the central US axial direction), and (b) a transabdominal imaging configuration (radiation beam parallel to the central US axial direction). For scenario (a), the dosimetric effect was evaluated as a function of the probe to inferior radiation field edge distance. Clinically applicable distances from 5 mm separation to 2 mm overlap were determined from the radiotherapy plans of 27 patients receiving Clarity imaging. Overlaps of 3 to 14 (1 to 3 SD) mm were also considered to include the effect of interfraction motion correction. The influence of voxel size on surface dose estimation was investigated. Approved clinical plans from two prostate patients were used to simulate worst-case dosimetric impact of the probe when large couch translations were applied to correct for interfraction prostate motion. RESULTS: The dosimetric impact of both the MV and kV probe models agreed within ±2% for both beam configurations. For scenario (a) and 1 mm voxel model, the probe gave mean dose increases of 1.2% to 4.6% (of the dose at isocenter) for 5 mm separation to 0 mm overlap in the probe-phantom contact region, respectively. This increased to 27.5% for the largest interfraction motion correction considered (14 mm overlap). For separations of ≥ 2 mm dose differences were < 2%. Simulated dose perturbations were found to be superficial; for the 14 mm overlap the dose increase reduced to < 3% at 5.0 mm within the phantom. For scenario (b), dose increases due to the probe were < 5% in all cases. The dose increase was underestimated by up to ~13% when the voxel size was increased from 1 mm to 3 mm. MC simulated dose to the PTV and OARs for the two clinical plans considered showed good agreement with commercial treatment planning system results (within 2%). Mean dose increases due to the presence of the probe, after the maximum interfraction motion correction, were ~16.3% and ~8.0%, in the contact region, for plan 1 and plan 2, respectively. CONCLUSIONS: The presence of the probe results in superficial dose perturbations for patients with an overlap between the probe and the radiation field present in either the original treatment plan or due to translation of the radiation field to simulate correction of interfraction internal prostate motion.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.303
Teacher spread0.295 · 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 designSimulation or modeling
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

Citations6
Published2017
Admission routes1
Has abstractyes

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