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Record W2030645385 · doi:10.1118/1.2965953

Poster - Thurs Eve-34: Extended CT-range in RT planning of pelvic cancer treatment in presence of hip replacements

2008· article· en· W2030645385 on OpenAlexaff
Maja Popović, Orest Ostapiak, Tom Chow

Bibliographic record

VenueMedical Physics · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsJuravinski Cancer Centre
Fundersnot available
KeywordsImaging phantomNuclear medicineImplantRange (aeronautics)Computed tomographyProsthesisMaterials scienceMedicineBiomedical engineeringRadiologySurgery

Abstract

fetched live from OpenAlex

Extended CT range in conventional CT scanners has a potential to allow for a more conformal treatment of patients with hip prosthesis. Its use may limit inaccuracies in electron density maps that are observed due to severe artifacts in CT data. In this study, we investigate the use of CT images with extended CT numbers in dose calculations and compare the results of calculations with standard CT data and measured doses. A phantom containing a hip prosthesis was scanned and treatment was planned with extended and standard CT numbers. The density override function was used to eliminate the effect of artifacts in the region outside of the metallic implant, while raw CT numbers were used to indicate density within the implant. Dose measurements were performed with two types of ion chambers at 6, 10 and 18MV energies. Our results indicate that data with extended CT range result in a better agreement between measured and calculated dose at the central position of the body phantom, as should be expected. However, CT artifacts within the implant region also result in discrepancies between the measured and calculated dose. The discrepancy is greater at lower cross-sectional thickness where bright, high density surface artifacts are high relative to the artificially low density inner region of the implant. Potential ways of resolving the discrepancies are outlined and a possibility of their application to clinical routine will be discussed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.451

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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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 designObservational
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

Citations0
Published2008
Admission routes1
Has abstractyes

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