On the direct acquisition of beam’s-eye-view images in MRI for integration with external beam radiotherapy
Bibliographic record
Abstract
The recent interest in the integration of external beam radiotherapy with a magnetic resonance (MR) imaging unit offers the potential for real-time adaptive tumour tracking during radiation treatment. The tracking of large tumours which follow a rapid trajectory may best be served by the generation of a projection image from the perspective of the beam source, or 'beam's eye view' (BEV). This type of image projection represents the path of the radiation beam, thus enabling rapid compensations for target translations, rotations and deformations, as well time-dependent critical structure avoidance. MR units have been traditionally incapable of this type of imaging except through lengthy 3D acquisitions and ray tracing procedures. This work investigates some changes to the traditional MR scanner architecture that would permit the direct acquisition of a BEV image suitable for integration with external beam radiotherapy. Based on the theory presented in this work, a phantom was imaged with nonlinear encoding-gradient field patterns to demonstrate the technique. The phantom was constructed with agarose gel tubes spaced two cm apart at their base and oriented to converge towards an imaginary beam source 100 cm away. A corresponding virtual phantom was also created and subjected to the same encoding technique as in the physical demonstration, allowing the method to be tested without hardware limitations. The experimentally acquired and simulated images indicate the feasibility of the technique, showing a substantial amount of blur reduction in a diverging phantom compared to the conventional imaging geometry, particularly with the nonlinear gradients ideally implemented. The theory is developed to demonstrate that the method can be adapted in a number of different configurations to accommodate all proposed integration schemes for MR units and radiotherapy sources. Depending on the configuration, the implementation of this technique will require between two and four additional nonlinear encoding coils.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".