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Record W1966775525 · doi:10.1088/0031-9155/58/7/2305

Finding an improved amorphous-silicon x-ray flat-panel detector configuration for the in-line geometry

2013· article· en· W1966775525 on OpenAlexaff
Martin F. Fast, A. Teymurazyan, Geordi Pang, Uwe Oelfke, J. A. Rowlands

Bibliographic record

VenuePhysics in Medicine and Biology · 2013
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreThunder Bay Regional Research InstituteUniversity of Toronto
FundersDeutscher Akademischer AustauschdienstCancer Research UK
KeywordsFlat panel detectorDetectorMaterials scienceFlat panelMonte Carlo methodOpticsAttenuationSIGNAL (programming language)Flat panel displaySiliconAmorphous siliconBeam (structure)Crystalline siliconOptoelectronicsPhysicsComputer science

Abstract

fetched live from OpenAlex

We have previously investigated the use of a conventional amorphous-silicon flat-panel detector (FPD) for intrafractional image guidance in the in-line geometry. In this configuration, the FPD is mounted between the patient and the treatment head, with the front of the FPD facing towards the patient. By geometrically separating signals from the diagnostic (kV) and treatment (MV) beams, it is possible to monitor the patient and treatment beam at the same time. In this study, we propose an FPD design based on existing technology with a 70% reduced up-stream areal density that is more suited to this new application. We have investigated our FPD model by means of a validated Monte Carlo simulation. Experimentally, simple rectangular fields were used to irradiate through the detector and observe the impact of removing detector components such as the support structure or the phosphor screen on the measured signal. The proposed FPD performs better than the conventional FPD: (i) attenuation of the MV beam is decreased by 60%; (ii) the MV signal is reduced by 20% for the primary MV field region which can avoid saturation of the FPD; and (iii) long range scatter from the MV into the kV region of the detector is greatly reduced.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.325

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 head0.147
GPT teacher head0.359
Teacher spread0.212 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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