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Record W2066541383 · doi:10.1118/1.1387273

On few‐view tomographic reconstruction with megavoltage photon beams

2001· article· en· W2066541383 on OpenAlexaff
Stephen Loose, Konrad Leszczyński

Bibliographic record

VenueMedical Physics · 2001
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsNortheast Cancer Centre
Fundersnot available
KeywordsIterative reconstructionProjection (relational algebra)Tomographic reconstructionMedical imagingLinear particle acceleratorTomographyComputer scienceImage-guided radiation therapyRadiation therapyComputer visionMedical physicsArtificial intelligenceNuclear medicineBeam (structure)PhysicsOpticsRadiologyMedicineAlgorithm

Abstract

fetched live from OpenAlex

Currently portal imaging devices are used to obtain information on patient localization during radiation therapy treatments. Such obtained information is two dimensional in nature, limited to the plane of the captured image. It has been proposed that megavoltage computed tomography images be reconstructed to overcome this limitation. This study explores the feasibility of reconstructing tomographic images from fan-beam projection data acquired with a commercial portal imaging device on a standard radiotherapy linear accelerator. Several CT reconstruction algorithms are examined as to their performance and suitability for applications in radiation therapy verification. The results show that it is possible, using some of the iterative reconstruction techniques, to obtain an image useful for patient localization from only several (< or =10) projection views.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.287
Teacher spread0.270 · 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 designOther design
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

Citations17
Published2001
Admission routes1
Has abstractyes

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