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Record W2473056131 · doi:10.1118/1.4957837

WE‐DE‐BRA‐08: A Linear Accelerator Target Allowing Rapid Switching Between Treatment and High‐Contrast Imaging Modes

2016· article· en· W2473056131 on OpenAlexaff
Mammo Yewondwossen, James L. Robar, David Parsons

Bibliographic record

VenueMedical Physics · 2016
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLinear particle acceleratorFiducial markerMedical imagingImaging phantomImage-guided radiation therapyRadiation therapyDosimetryComputer scienceNuclear medicinePhysicsOpticsMedicineArtificial intelligenceRadiologyBeam (structure)

Abstract

fetched live from OpenAlex

Purpose: During radiotherapy treatment, lung tumors can display substantial respiratory motion. This motion usually necessitates enlarged treatment margins to provide full tumour coverage. Unfortunately, these margins limit the dose that can be prescribed for tumour control and cause complications to normal tissue. Options for real‐time methods of direct detection of tumour position, and particularly those that obviate the need for inserted fiducial markers, are limited. We propose a method of tumor tracking without implanted fiducial markers using a novel fast switching‐target that toggles between a FFF copper/tungsten therapy mode and a FFF low‐Z target mode for imaging. In this work we demonstrate proof‐of‐concept of this new technology. Methods: The prototype includes two targets: i) a FFF copper/tungsten target equivalent to that in the Varian 2100 EX 6 MV, and ii) a low‐Z (carbon) target with a thickness of 110% of continuous slowing down approximation range (CSDA) at 7 MeV. The two targets can be exchanged with a custom made linear slide and motor‐driven actuator. The usefulness of the switching‐target concept is demonstrated through experimental BEV Planar images acquired with continual treatment and imaging at a user‐defined period. Results: The prototype switching‐target demonstrates that two recent advances in linac technology (FFF target for therapy and low‐Z target) can be combined with synergy. The switching‐target approach offers the capacity for rapid switching between treatment and high‐contrast imaging modes, allowing intrafractional tracking, as demonstrated in this work with dynamic breathing phantom. By using a single beam‐line, the design is streamlined and may obviate the need for an auxiliary imaging system (e.g., kV OBI.) Conclusion: This switching‐target approach is a feasible combination of two current advances in linac technology (FFF target for therapy and a FFF low‐Z target) allowing new options in on‐line IGRT.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.019
GPT teacher head0.284
Teacher spread0.265 · 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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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