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Record W3178209406 · doi:10.1002/acm2.13285

Investigating the impacts of intrafraction motion on dosimetric outcomes when treating small targets with virtual cones

2021· article· en· W3178209406 on OpenAlexaff
Cody Church, David Parsons, Alasdair Syme

Bibliographic record

VenueJournal of Applied Clinical Medical Physics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCollimatorImaging phantomRotation (mathematics)RadiosurgeryMonte Carlo methodOpticsArc (geometry)Aperture (computer memory)VoxelPhysicsNuclear medicineSuperposition principleMathematicsGeometryRadiation therapyMedicineComputer scienceComputer visionAcousticsStatistics

Abstract

fetched live from OpenAlex

Abstract Purpose Intrafraction patient motion is a well‐documented phenomenon in radiation therapy. In stereotactic radiosurgery applications in which target sizes can be very small and dose gradients very steep, patient motion can significantly impact the magnitude and positional accuracy of the delivered dose. This work investigates the impact of intrafraction motion on dose metrics for small targets when treated with a virtual cone. Materials and Methods Monte Carlo simulations were performed to calculate dose kernels for treatment apertures ranging from 1 × 2.5 mm 2 to 10 × 10 mm 2 . The phantom was an 8.2‐cm diameter sphere and isotropic voxels had lengths of 0.25 mm. Simulated treatments consisted of 3 arcs: 1 axial arc (360° gantry rotation, couch angle 0°) and 2 oblique arcs (180° gantry rotation, couch angle ±45°). Dose distributions were calculated via superposition of the rotated kernels. Two different collimator orientations were considered to create a virtual cone: (a) each treatment arc was delivered twice, once each with a static collimator angle of ±45°, and (b) each treatment arc was delivered once, with dynamic collimator rotation throughout the arc. Two different intrafraction motion patterns were considered: (a) constant linear motion and (b) sudden, persistent motion. The impact of motion on dose distributions for target sizes ranging from 1 to 10 mm diameter spheres was quantified as a function of the aperture size used to treat the lesions. Results The impact of motion on both the target and the surrounding tissue was a function of both aperture shape and target size. When a 0.5‐mm linear drift along each dimension occurred during treatment, targets ≥5 mm saw less than a 10% decrease in coverage by the prescription dose. Smaller apertures accrued larger penalties with respect to dosimetric hotspots seen in the tissues surrounding the target volume during intrafraction motion. For example, treating a 4‐mm‐sized target that undergoes 2.60 mm (3D vector) of continuous linear motion, the D 5 in the concentric shells that extend 1, 2, and 3 mm from the surface of the target was 39%, 24%, and 14% smaller, respectively when comparing the delivery of a larger aperture (6 × 10 mm 2 ) to a smaller aperture (2 × 5 mm 2 ). Using a static collimator for shaping a virtual cone during treatment minimized the dosimetric impact of motion in the majority of cases. For example, the volume that is covered by 70% or more of the prescription dose is smaller in 60.4% of cases when using the static collimator. The volume covered by 50, and 30% or more of the prescription dose is also smaller when treating with a static collimator, but the clinical significance of this finding is unknown. Conclusions In this work, the dosimetric trade‐offs between aperture size and target size when irradiating with virtual cones has been demonstrated. These findings provide information about the tradeoffs between target coverage and normal tissue sparing that may help inform clinical decision making when treating smaller targets with virtual cones.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.336
Teacher spread0.309 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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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Citations3
Published2021
Admission routes1
Has abstractyes

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