Image fusion for radiosurgery treatments of arteriovenous malformations
Bibliographic record
Abstract
An interactive 3D target localisation and delineation tool has been developed for radiosurgery planning of arteriovenous malformations (AVMs). With this system, magnetic resonance (MR), MR angiography (MRA) and computed tomography (CT) volumes can be fused in stereotactic space. Stereotactic angiography (SA) images can be linked to the MRA volume by recovering the SA acquisition geometry. The MRA and SA images can be correlated (1) by ray-tracing through the MRA volume with the recovered SA acquisition geometry and overlaying the images onto the SA images and (2) by localising the AVM onto a volume rendered representation of the MRA with a 3D cursor and projecting its position onto the SA images. Target contours can then be drawn on the MRA/MR/CT images and simultaneously projected onto the SA images. The plans of patients who had previously undergone radiosurgery at our institution employing SA images for localisation and MR images for delineation were investigated. MRA datasets were also acquired at the time of MR scanning employing the 3D TOF technique. Some ray-traced MRA images correlated well visually with the SA images, others presented inconsistencies which suggest that MRA should be used only as complement to SA images. The role of the different modalities (M-RA, MR and SA) in the definition of target volumes is investigated by defining the target contours with different combinations of modalities within the interactive system. The target volumes drawn with different modalities were compared to a reference volume, drawn using MRA, MR and SA images, and presented underestimation and overestimation of target volumes ranging from 20% to 92% and from 3% to 40%. The dosimetric implications of image fusion for target delineation are investigated by retrospective evaluation of the dose coverage of the reference target volume by the original treatment plan. Target coverage inferior to 60% of the reference target volumes by the original treatment plans was obtained for the patients. Treatment plan optimisation was performed to evaluate the possible dosimetric improvements resulting from image fusion for AVM target delineation. The plans were improved by employing three, eight and four isocentres for the 3 patients, and resulted in target coverage equal or superior to 98% for all three patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".