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Record W2040287532 · doi:10.1118/1.4764563

Fast transit portal dosimetry using density‐scaled layer modeling of aSi‐based electronic portal imaging device and Monte Carlo method

2012· article· en· W2040287532 on OpenAlexaff
Jae Won Jung, Jong Oh Kim, I Yeo, Young‐Bin Cho, Sun Mo Kim, Steven J. DiBiase

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer Centre
FundersVarian Medical Systems
KeywordsImaging phantomDosimetryMonte Carlo methodImage-guided radiation therapyNuclear medicineCalibrationMedical imagingRadiation treatment planningDetectorOpticsMaterials sciencePhysicsRadiation therapyMedicineMathematicsRadiology

Abstract

fetched live from OpenAlex

PURPOSE: Fast and accurate transit portal dosimetry was investigated by developing a density-scaled layer model of electronic portal imaging device (EPID) and applying it to a clinical environment. METHODS: The model was developed for fast Monte Carlo dose calculation. The model was validated through comparison with measurements of dose on EPID using first open beams of varying field sizes under a 20-cm-thick flat phantom. After this basic validation, the model was further tested by applying it to transit dosimetry and dose reconstruction that employed our predetermined dose-response-based algorithm developed earlier. The application employed clinical intensity-modulated beams irradiated on a Rando phantom. The clinical beams were obtained through planning on pelvic regions of the Rando phantom simulating prostate and large pelvis intensity modulated radiation therapy. To enhance agreement between calculations and measurements of dose near penumbral regions, convolution conversion of acquired EPID images was alternatively used. In addition, thickness-dependent image-to-dose calibration factors were generated through measurements of image and calculations of dose in EPID through flat phantoms of various thicknesses. The factors were used to convert acquired images in EPID into dose. RESULTS: For open beam measurements, the model showed agreement with measurements in dose difference better than 2% across open fields. For tests with a Rando phantom, the transit dosimetry measurements were compared with forwardly calculated doses in EPID showing gamma pass rates between 90.8% and 98.8% given 4.5 mm distance-to-agreement (DTA) and 3% dose difference (DD) for all individual beams tried in this study. The reconstructed dose in the phantom was compared with forwardly calculated doses showing pass rates between 93.3% and 100% in isocentric perpendicular planes to the beam direction given 3 mm DTA and 3% DD for all beams. On isocentric axial planes, the pass rates varied between 95.8% and 99.9% for all individual beams and they were 98.2% and 99.9% for the composite beams of the small and large pelvis cases, respectively. Three-dimensional gamma pass rates were 99.0% and 96.4% for the small and large pelvis cases, respectively. CONCLUSIONS: The layer model of EPID built for Monte Carlo calculations offered fast (less than 1 min) and accurate calculation for transit dosimety and dose reconstruction.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.015
GPT teacher head0.314
Teacher spread0.299 · 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

Citations12
Published2012
Admission routes1
Has abstractyes

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