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Record W1966037103 · doi:10.1259/dmfr/21679313

A novel alignment device for cone beam computed tomography: principle and application

2010· article· en· W1966037103 on OpenAlexaff
Andrew Dawood, Veronique Sauret-Jackson, Shanon Patel, Alastair Darwood

Bibliographic record

VenueDentomaxillofacial Radiology · 2010
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsCentre de Santé et de Services Sociaux Cavendish
Fundersnot available
KeywordsCone beam computed tomographyScannerField of viewComputer scienceComputer visionRotation (mathematics)Artificial intelligenceNuclear medicineComputed tomographyMedicineRadiology

Abstract

fetched live from OpenAlex

OBJECTIVES: the aim of this investigation was to optimize the positioning and size of the scanned field of view (FOV) in cone beam CT (CBCT) scanners using a practical external alignment device fitted in the patient's mouth in order to train radiographers and reduce radiation dose to the patient. This is particularly challenging when using small FOVs to cover small volumes of interest. METHODS: test objects were positioned and scanned using the aligner to show that the design and geometry were correct and help the radiographer to superimpose the scanner and the volume of interest axis of rotation. An in vivo study was then undertaken comparing the accuracy of patient positioning when using the aligner, instead of scouts, to position the patient for small FOV (cylinders of 4-8 cm height and 4-8 cm diameter) dental scans. The scanners used were the Accuitomo F170 CBCT scanner (Morita, Kyoto, Japan) and the iCAT Next Gen CBCT scanner (Imaging Sciences, Hatfield, PA). RESULTS: there was no significant difference in positioning the patient when using the aligner compared with the scout images. CONCLUSIONS: it is possible to rely on the aligner for patient positioning for a small volume scan and therefore spare the radiation dose associated with scout 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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score1.000

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 head0.013
GPT teacher head0.284
Teacher spread0.271 · 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.

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

Citations3
Published2010
Admission routes1
Has abstractyes

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