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Record W1972611177 · doi:10.1118/1.2965935

Poster - Thurs Eve-16: Just-in-time tomography (JiTT)

2008· article· en· W1972611177 on OpenAlexaff
Geordi Pang, J. A. Rowlands

Bibliographic record

VenueMedical Physics · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Optical Sensing Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCone beam computed tomographyImage-guided radiation therapyRadiation therapyMedical physicsMedical imagingNuclear medicineComputer scienceMedicineComputer visionComputed tomographyArtificial intelligenceRadiology

Abstract

fetched live from OpenAlex

Soft-tissue target motion is one of the main concerns in high-precision radiation therapy. Cone beam computed tomography (CBCT) has been developed recently to image soft-tissue targets in the treatment room for image-guided radiation therapy. However, due to its relatively long image acquisition time the CBCT approach cannot provide images of the target at the instant of the treatment and thus is not adequate for imaging targets with intrafraction motion. In this work, a new concept for image-guided radiation therapy- just-in-time tomography (JiTT) - is introduced. Differing from CBCT, JiTT takes much less time to generate the needed tomographical, beam's-eye-view images of the treatment target at the right moment to guide the radiation therapy treatment. A system to achieve JiTT is proposed and its feasibility is investigated. Research supported by Siemens.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.705

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 integrity0.0000.001
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 head0.251
Teacher spread0.237 · 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 designTheoretical or conceptual
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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