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Record W2802753733 · doi:10.1002/mp.12910

Quality control methods for linear accelerator radiation and mechanical axes alignment

2018· article· en· W2802753733 on OpenAlexaff
D. Létourneau, H. Keller, Nathan Becker, Md Nurul Amin, Bernhard D. Norrlinger, David A. Jaffray

Bibliographic record

VenueMedical Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsLinear particle acceleratorCollimatorRotation (mathematics)OpticsBeam (structure)PhysicsRadiationComputer scienceComputer vision

Abstract

fetched live from OpenAlex

PURPOSE: The delivery accuracy of highly conformal dose distributions generated using intensity modulation and collimator, gantry, and couch degrees of freedom is directly affected by the quality of the alignment between the radiation beam and the mechanical axes of a linear accelerator. For this purpose, quality control (QC) guidelines recommend a tolerance of ±1 mm for the coincidence of the radiation and mechanical isocenters. Traditional QC methods for assessment of radiation and mechanical axes alignment (based on pointer alignment) are time consuming and complex tasks that provide limited accuracy. In this work, an automated test suite based on an analytical model of the linear accelerator motions was developed to streamline the QC of radiation and mechanical axes alignment. METHODS: The proposed method used the automated analysis of megavoltage images of two simple task-specific phantoms acquired at different linear accelerator settings to determine the coincidence of the radiation and mechanical isocenters. The sensitivity and accuracy of the test suite were validated by introducing actual misalignments on a linear accelerator between the radiation axis and the mechanical axes using both beam steering and mechanical adjustments of the gantry and couch. RESULTS: The validation demonstrated that the new QC method can detect sub-millimeter misalignment between the radiation axis and the three mechanical axes of rotation. A displacement of the radiation source of 0.2 mm using beam steering parameters was easily detectable with the proposed collimator rotation axis test. Mechanical misalignments of the gantry and couch rotation axes of the same magnitude (0.2 mm) were also detectable using the new gantry and couch rotation axis tests. For the couch rotation axis, the phantom and test design allow detection of both translational and tilt misalignments with the radiation beam axis. For the collimator rotation axis, the test can isolate the misalignment between the beam radiation axis and the mechanical collimator rotation axis from the impact of field size asymmetry. The test suite can be performed in a reasonable time (30-35 min) due to simple phantom setup, prescription-based beam delivery, and automated image analysis. As well, it provides a clear description of the relationship between axes. After testing the sensitivity of the test suite to beam steering and mechanical errors, the results of the test suite were used to reduce the misalignment errors of the linac to less than 0.7-mm radius for all axes. CONCLUSIONS: The proposed test suite offers sub-millimeter assessment of the coincidence of the radiation and mechanical isocenters and the test automation reduces complexity with improved efficiency. The test suite results can be used to optimize the linear accelerator's radiation to mechanical isocenter alignment by beam steering and mechanical adjustment of gantry and couch.

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)0.009
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.414
Teacher spread0.385 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations16
Published2018
Admission routes1
Has abstractyes

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