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Record W1972475251 · doi:10.1118/1.4757582

2D lag and signal nonlinearity correction in an amorphous silicon EPID and their impact on pretreatment dosimetric verification

2012· article· en· W1972475251 on OpenAlexaff
Brad Warkentin, S Rathee, S Steciw

Bibliographic record

VenueMedical Physics · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of AlbertaOntario Institute for Cancer Research
Fundersnot available
KeywordsMultileaf collimatorSIGNAL (programming language)Image-guided radiation therapyLinear particle acceleratorNonlinear systemDetectorDosimetrySignal processingOpticsNuclear medicineMedical imagingComputer sciencePhysicsBeam (structure)Artificial intelligenceDigital signal processingMedicine

Abstract

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PURPOSE: This investigation provides measurements of signal lag and nonlinearity separately for the Varian aS500 electronic portal imaging device (EPID), and an algorithm to correct for these effects in 2D; their potential impact on intensity modulated radiation therapy (IMRT) verification is also investigated. The authors quantify lag, as a function of both delivered monitor units (MU) and time, by using a range of MUs delivered at a clinically used rate of 400 MU∕min. Explicit cumulative lag curves are thus determined for a range of MUs and times between the end of irradiation and the end of image acquisition. Signal nonlinearity is also investigated as a function of total MUs delivered. The family of cumulative lag curves and signal nonlinearity are then used to determine their effects on dynamic multileaf collimator (MLC) (IMRT) deliveries, and to correct for theses effects in 2D. METHODS: Images acquired with an aS500 EPID and Varis Portal-Vision software were used to quantify detector lag and signal-nonlinearity. For the signal lag investigation, Portal-Vision's service monitor was used to acquire EPID images at a rate of 8 frames/s. The images were acquired during irradiation and 66 s thereafter, by inhibiting the M-holdoff-In signal of the Linac for a range of 4.5-198.5 MUs. Relative cumulative lag was calculated by integrating the EPID signal for a time after beam-off, and normalizing this to the integrated EPID signal accumulated during radiation. Signal nonlinearity was studied by acquiring 10 × 10 cm(2) open-field EPID images in "integrated image" mode for a range of 2-500 MUs, and normalized to the 100 MU case. All data were incorporated into in-house written software to create a 2D correction map for these effects, using the field's MLC file and a field-specific calculated 2D "time-map," which keeps track of the time elapsed from the last fluence delivered at each given point in the image to the end of the beam delivery. RESULTS: Relative cumulative lag curves reveal that the lag alone can deviate the EPID's perceived dose by as large as 6% (1 MU delivery, 60 s postirradiation). For signal nonlinearity relative to 100 MU, EPID signals per MU of 0.84 and 1.01 were observed for 2 and 500 MUs, respectively. Correction maps were applied to a 1 cm sweeping-window 14 × 14 cm(2) field and clinical head-and-neck IMRT field. A mean correction of 1.028 was implemented in the head-and-neck field, which significantly reduced lag-related asymmetries in the EPID images, and restored linearity to the EPID imager's dose response. Corrections made to the sweeping-field showed good agreement with the treatment planning system-predicted field, yielding an average percent difference of 0.05% ± 0.91%, compared to the -1.32% ± 1.02% before corrections, or 1.75% ± 1.04% when only a signal nonlinearity correction is made. CONCLUSIONS: Lag and signal-nonlinearity have been quantified for an aS500 EPID imager, and an effective 2D correction method has been developed which effectively removes nonlinearity and lag effects. Both of these effects were shown to negatively impact IMRT verifications. Especially fields that involve prolonged irradiation and small overall MUs should be corrected for in 2D.

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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)0.006
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.302
Teacher spread0.289 · 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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Citations7
Published2012
Admission routes1
Has abstractyes

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