MétaCan
Menu
Back to cohort
Record W2037480429 · doi:10.1118/1.2370503

Thick, segmented ‐photodiode detector for cone beam megavoltage CT: A Monte Carlo study of system design parameters

2006· article· en· W2037480429 on OpenAlexafffund
T. T. Monajemi, B. G. Fallone, S Rathee

Bibliographic record

VenueMedical Physics · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsOpticsDetective quantum efficiencyPhotodiodeDetectorPhysicsMonte Carlo methodOptical transfer functionPhotonBeam (structure)Beam divergenceMaterials scienceBeam diameterImage qualityLaser

Abstract

fetched live from OpenAlex

Megavoltage (MV) imaging detectors have been the focus of research by many groups in recent years. We have been working with segmented CdWO4 crystals in contact with photodiodes in our lab. The present study uses both x-ray and optical photon transport Monte Carlo simulations to analyze the effects of scintillation crystal height, septa material, beam divergence, and beam spectrum on the modulation transfer function, MTF(f) and zero frequency detective quantum efficiency, DQE(0), of a theoretical area detector. The theoretical detector is comprised of tall, segmented CdWO4 crystals and two dimensional photodiode arrays with a pitch of 1 mm and a fill factor of 72%. Increasing the crystal height above 10 mm does not result in an improvement in the DQE(0) if the reflection coefficient of the septa is less than 0.8. For a reflection coefficient of 0.975 for the septa, there is a continual gain in the DQE(0) up to 30 mm tall crystals. Similar calculations show that employing a 3.5 MV beam without a flattening filter increases the DQE(0) for 20 mm tall crystals by 9% compared to a typical 6 MV beam with a flattening filter. The severe degradations due to beam divergence on MTF(f) are quantified and suggest the use of focused detectors in MV imaging. It is found that when the effect of optical photons is considered, the presence of divergence can appear as a shift in the location of the input signal as well as loss of spatial resolution.

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: none
Teacher disagreement score0.540
Threshold uncertainty score0.786

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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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

Citations20
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueMedical PhysicsSame topicAdvanced X-ray and CT ImagingFrench-language works237,207