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Record W2473458111 · doi:10.1118/1.4957308

Optical CT imaging of solid radiochromic dosimeters in mismatched refractive index solutions using a scanning laser and large area detector

2016· article· en· W2473458111 on OpenAlexaff
Kurtis H Dekker, Jerry Battista, Kevin Jordan

Bibliographic record

VenueMedical Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsDosimeterOpticsMaterials scienceRefractive indexFiducial markerDetectorCollimatorLaserSchlierenRotation (mathematics)PhysicsRadiationComputer science

Abstract

fetched live from OpenAlex

PURPOSE: The practical use of the PRESAGE® solid plastic dosimeter is limited by the inconvenience of immersing it in high-viscosity oils to achieve refractive index matching for optical computed tomography (CT) scanning. The oils are slow to mix and difficult to clean from surfaces, and the dosimeter rotation can generate dynamic Schlieren inhomogeneity patterns in the reference liquid, limiting the rotational and overall scan speed. Therefore, it would be beneficial if lower-viscosity, water-based solutions with slightly unmatched refractive index could be used instead. The purpose of this work is to demonstrate the feasibility of allowing mismatched conditions when using a scanning laser system with a large acceptance angle detector. A fiducial-based ray path measurement technique is combined with an iterative CT reconstruction algorithm to reconstruct images. METHODS: A water based surrounding liquid with a low viscosity was selected for imaging PRESAGE® solid dosimeters. Liquid selection was optimized to achieve as high a refractive index as possible while avoiding rotation-induced Schlieren effects. This led to a refractive index mismatch of 6% between liquid and dosimeters. Optical CT scans were performed with a fan-beam scanning-laser optical CT system with a large area detector to capture most of the refracted rays. A fiducial marker placed on the wall of a cylindrical sample occludes a given light ray twice. With knowledge of the rotation angle and the radius of the cylindrical object, the actual internal path of each ray through the dosimeter can be calculated. Scans were performed with 1024 projections of 512 data samples each, and rays were rebinned to form 512 parallel-beam projections. Reconstructions were performed on a 512 × 512 grid using 100 iterations of the SIRT iterative CT algorithm. Proof of concept was demonstrated with a uniformly attenuating solution phantom. PRESAGE® dosimeters (11 cm diameter) were irradiated with Cobalt-60 irradiator to achieve either a uniform dose or a 2-level "step-dose" pattern. RESULTS: With 6% refractive index mismatching, a circular field of view of 85% of the diameter of a cylindrical sample can be reconstructed accurately. Reconstructed images of the test solution phantom were uniform (within 3%) inside this radius. However, the dose responses of the PRESAGE® samples were not spatially uniform, with variations of at least 5% in sensitivity. The variation appears as a "cupping" artifact with less sensitivity in the middle than at the periphery of the PRESAGE® cylinder. Polarization effects were also detected for these samples. CONCLUSIONS: The fiducial-based ray path measurement scheme, coupled with an iterative reconstruction algorithm, enabled optical CT scanning of PRESAGE® dosimeters immersed in mismatched refractive index solutions. However, improvements to PRESAGE® dose response uniformity are required.

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)0.001
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.294
Teacher spread0.281 · 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".

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Citations14
Published2016
Admission routes1
Has abstractyes

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