WE‐DE‐BRA‐08: A Linear Accelerator Target Allowing Rapid Switching Between Treatment and High‐Contrast Imaging Modes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".