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Record W2056391277 · doi:10.1118/1.2244630

Po‐Thur Eve General‐03: The Use of Megavoltage Computed Tomography (MVCT) in Treatment Planning

2006· article· en· W2056391277 on OpenAlexaff
David Sasaki, G Field, M. Mackenzie, S Rathee, B. G. Fallone

Bibliographic record

VenueMedical Physics · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsImaging phantomRadiation treatment planningNuclear medicineComputed tomographyMedicineCalibrationMedical imagingTomographyMaterials scienceRadiation therapyRadiologyPhysics

Abstract

fetched live from OpenAlex

In modern radiotherapy treatment planning, the information in diagnostic CT images is used for two purposes: to delineate tumour and surrounding critical structures and to provide an electron density map of the patient that is used to calculate the dose distribution resulting from exposure to a certain beam arrangement. In pelvic cancer patients with hip prostheses, the metal implants produce artifacts in the diagnostic CT images such that both the location of the tumour and accurate electron densities are either difficult or impossible to obtain. We have used megavoltage CT (MVCT) images for treatment planning in an attempt to quantify the impact of metal artifacts and overcome the problems they introduce. This has been done in three different sets of experiments. The first was a calibration of the megavoltage CT number‐to‐electron density curve using a CIRS phantom. This also allowed for measurements of the impact of metal artifacts on apparent relative electron density in both kVCT and MVCT images. The second was the comparison of treatment plans generated for patients with metal implants using both diagnostic and megavoltage CT studies. This allowed for quantitative measurements of the calculated dosimetric effect of metal artifacts. The final set of experiments compared MVCT and kVCT treatment plans of a water tank containing a stainless steel 316L rod. Dose measurements were taken at various points and compared to the doses calculated using both MVCT and kVCT studies.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.435

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 head0.019
GPT teacher head0.237
Teacher spread0.218 · 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 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

Citations0
Published2006
Admission routes1
Has abstractyes

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