Gamma Knife irradiation method based on dosimetric controls to target small areas in rat brains
Bibliographic record
Abstract
PURPOSE: Targeted and whole-brain irradiation in humans can result in significant side effects causing decreased patient quality of life. To adequately investigate structural and functional alterations after stereotactic radiosurgery, preclinical studies are needed. The purpose of this work is to establish a robust standardized method of targeted irradiation on small regions of the rat brain. METHODS: Euthanized male Fischer rats were imaged in a stereotactic bed, by computed tomography (CT), to estimate positioning variations relative to the bregma skull reference point. Using a rat brain atlas and the stereotactic bregma coordinates obtained from CT images, different regions of the brain were delimited and a treatment plan was generated. A single isocenter treatment plan delivering ≥ 100 Gy in 100% of the target volume was produced by Leksell GammaPlan using the 4 mm diameter collimator of sectors 4, 5, 7, and 8 of the Gamma Knife unit. Impact of positioning deviations of the rat brain on dose deposition was simulated by GammaPlan and validated with dosimetric measurements. RESULTS: The authors' results showed that 90% of the target volume received 100 ± 8 Gy and the maximum of deposited dose was 125 ± 0.7 Gy, which corresponds to an excellent relative standard deviation of 0.6%. This dose deposition calculated with GammaPlan was validated with dosimetric films resulting in a dose-profile agreement within 5%, both in X- and Z-axes. CONCLUSIONS: The authors' results demonstrate the feasibility of standardizing the irradiation procedure of a small volume in the rat brain using a Gamma Knife.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".