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Record W1983051351 · doi:10.1118/1.2839146

Correction of megavoltage cone‐beam CT images for dose calculation in the head and neck region

2008· article· en· W1983051351 on OpenAlexafffund
Jean‐François Aubry, Jean Pouliot, Luc Beaulieu

Bibliographic record

VenueMedical Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsHôtel-Dieu de QuébecCentre hospitalier universitaire de Québec
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNuclear medicineMedical imagingCone beam ctHead and neckCone beam computed tomographyImage registrationComputed tomographyMedicineComputer scienceImage (mathematics)RadiologyArtificial intelligence

Abstract

fetched live from OpenAlex

Megavoltage cone-beam computed tomography (MVCBCT) imaging systems are now available for image-guided radiation therapy delivery and verification. In order to use the three-dimensional anatomical information for dose calculation, the MVCBCT image must provide accurate electron density. This work proposes a new method that has been developed to correct for the cupping and missing data artifacts seen on MVCBCT images of the head and neck region. It uses a conventional kilovoltage CT (kVCT) image as a reference for electron density and rigid registration with a MVCBCT image to obtain correction factors. Dose calculations performed on MVCBCT images corrected with the proposed method agree with calculations done on kVCT images within +/- 1% on phantoms. With patients images the agreement is within +/- 13% above the shoulders and +/- 5% below the shoulder line. This level of dose calculation accuracy allows the use of MVCBCT images for dose verification purposes.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score0.233

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 communication0.0000.000
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 head0.297
Teacher spread0.281 · 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 designOther design
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

Citations24
Published2008
Admission routes2
Has abstractyes

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