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Record W4244441078 · doi:10.32920/ryerson.14646576.v1

A Novel High Quantum Efficiency MV X-Ray Detector For Image-Guided Radiotherapy

2021· preprint· en· W4244441078 on OpenAlexafffund
Jian Liu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsToronto Metropolitan UniversityHealth Sciences CentreSunnybrook Health Science CentreWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDetectorImage resolutionOpticsQuantum efficiencyDetective quantum efficiencyPhysicsSIGNAL (programming language)OptoelectronicsMaterials scienceImage qualityComputer scienceImage (mathematics)Computer vision

Abstract

fetched live from OpenAlex

To develop a new MV x-ray detector with a high quantum efficiency and an adequate spatial resolution for image-guided radiotherapy, scintillating fibers with a diameter of 1 mm were embedded in lead to form a honeycomb pattern with a thickness of 2 cm. The properties of the detector were measured using a 6 MV beam on a Linac machine. The prototype detector has a quantum efficient of 35%, about an order of magnitude higher than that of current detectors used in the clinic. The spatial resolution of the prototype is comparable to that of video-based electronic portal imaging systems. The prototype detector can also suppress scattered signals which will help to improve the signal to noise ratio of the image. This work indicates that using scintillating fibers to generate and guide imaging signals, it is possible to increase the quantum efficiency and maintain an adequate spatial resolution for MV x-ray imaging.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.309
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2021
Admission routes2
Has abstractyes

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