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Record W2095161483 · doi:10.1118/1.3181908

SU‐FF‐T‐426: Fast 4D Monte Carlo Dose Calculations in Deforming Anatomies

2009· article· en· W2095161483 on OpenAlexaff
Emily Heath, I. Kawrakow

Bibliographic record

VenueMedical Physics · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMonte Carlo methodImaging phantomVoxelComputationImage warpingGeometryMathematicsPhysicsComputer scienceAlgorithmOpticsStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose: The voxel warping approach to 4D dose calculation calculates the dose deposition on a deformed dose grid in order to determine the contribution of dose deposited at one anatomical state to a reference state. The current implementation of this method in the defDOSXYZnrc Monte Carlo code resulted in a significant increase in computation times. The purpose of this work was an efficient implementation of the voxel warping method in the VMC++ Monte Carlo. Three alternative deformed voxel geometry definitions were investigated to see how they influence the remapped dose calculation efficiency and accuracy. Method and Materials: A set of new deformable geometry classes was created for VMC++ which were compiled as dynamic shared libraries that could be loaded at run time. We implemented three different deformable geometries: a deformed dodecahedron geometry and two geometries based on tetrahedral elements with alternate divisions of the voxel faces. Dose calculations using the new deformable VMC++ geometry were validated by comparison with defDOSXYZnrc by calculating remapped dose in a deforming water phantom and a lung patient deformed from Inhale to Exhale. Dose distributions were compared using the gamma index as well as the Kawrakow‐Fippel test to distinguish systematic discrepancies from dose differences due to random statistical uncertainties. Results: Efficiency gains of the order of 100 were obtained relative to defDOSXYZnrc. A further factor of 1.2 gain in efficiency was realized with the tetrahedral geometries. VMC++ and defDOSXYZnrc calculations were found to agree within 1% in the deforming phantom. Although it could be demonstrated that different folding of the voxel faces influences the dose calculation no differences were found in the patient dose distributions calculated using the different geometries. Conclusion: We have implemented a new deformed geometry class in the VMC++ Monte Carlo code which makes possible efficient and accurate 4D dose calculations.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.003

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 head0.009
GPT teacher head0.292
Teacher spread0.283 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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