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Record W3027405226 · doi:10.1002/mp.14213

Tomographic‐based 3D scintillation dosimetry using a three‐view plenoptic imaging system

2020· article· en· W3027405226 on OpenAlexafffund
Madison Rilling, Guillaume Allain, Simon Thibault, Louis Archambault

Bibliographic record

VenueMedical Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversité LavalHôtel-Dieu de Québec
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDosimetryOpticsScintillationScintillatorVoxelImage resolutionPhysicsIterative reconstructionPixelNuclear medicineComputer scienceComputer visionDetector

Abstract

fetched live from OpenAlex

PURPOSE: To demonstrate the feasibility of a three-plenoptic camera projection, scintillation-based dosimetry system for measuring three-dimensional (3D) dose distributions of static photon radiation fields. METHODS: Static x-ray photon beams were delivered to a cubic plastic scintillator volume embedded within acrylic blocks. For each beam, three orthogonal projections of the scintillating light emission were recorded using a multifocus plenoptic camera. Experimental 3D reconstructions of the light distribution were obtained using an iterative maximum likelihood-expectation maximization (ML-EM) algorithm. For this purpose, the elements of the system matrix representing the contribution of the scintillator volume voxels to the camera sensor pixels were calculated using optical design software. A reconstruction-specific correction was applied to light reconstructions to account for scintillating light imaged by the camera but not directly resulting from dose deposition. Cross beam profiles (CBPs) and percentage depth dose (PDD) curves were compared to treatment planning system data for square fields. Three-dimensional and 3D gamma analyses were performed for concave-shaped dose distributions and the Pearson correlation coefficient and reconstruction error were employed to assess the quality of the measured relative 3D dose distributions. RESULTS: compared with treatment planning system reference distributions, the maximum root-mean-square error of the CBPs evaluated at two different depths was of 3.2%, 1.2%, and 1.1%, respectively; as for the corresponding linearly fitted PDDs of the square fields, the slopes of the reconstructed dose distributions overestimated those of the reference distributions by at most 0.2%/ cm. The 2D gamma passing rate with a criterion of 2%/2 mm for the concave-shaped photon field was of 61.6%, 66.1%, and 76.4% using one, two, and three plenoptic projections; the respective success rates become 77.1%, 87.5%, and 94.9% using a criterion of 3%/3 mm. The 3D correlation coefficient for the corresponding reconstructions was of 0.688, 0.905, and 0.976, respectively. CONCLUSIONS: Three-dimensional light distributions emitted from within a plastic scintillator volume were successfully recovered using optical design software to establish a complete tomographic model of a plenoptic camera-based prototype. The tomographic model can equivalently extend to dynamic dose delivery measurements, providing temporal resolution limited by the camera's exposure time. This feasibility study enables a simplified design-to-implementation process for volumetric scintillation dosimetry prototypes toward fully meeting the clinical needs of 3D dose measurements for static and dynamic delivery techniques.

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)0.002
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.281
Teacher spread0.264 · 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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Citations16
Published2020
Admission routes2
Has abstractyes

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