Selective and super-selective C-arm based cone beam CT angiography (CBCTA) with DynaCT for CyberKnife radiosurgery planning of intracranial arteriovenous malformations (AVMs).
Bibliographic record
Abstract
BACKGROUND: Successful radiosurgery for intracranial arteriovenous malformations (AVMs) requires accurate delineation of the nidus in 3D. Exact targeting and precise equipment is needed to achieve obliteration of the nidus while minimizing toxicity to the surrounding brain. In some micro-AVMs and poorly visible AVMs we have used cone beam CT angiography (CBCTA) with selective and super-selective angiography where a micro-catheter is advanced into the feeding arteries- to assist with nidus definition for CyberKnife radiosurgery planning. METHODS: Four patients who had AVMs inadequately visualized with MRI, MRA, CT, CTA, and dynamic CT angiography (dCTA) were identified for selective angiography (2 had super-selective angiography) for CyberKnife radiosurgery. The mean age at the time of treatment was 45 years (range: 22 - 71 years). All patients had suffered prior hemorrhage and were deemed inoperable. Super-selective angiography was done under general anesthesia to minimize motion artefact and the risk of arterial dissection. Angiography was performed using the biplane angiographic suite (ArtisQ; Siemens). Cone beam reconstructions were performed using DynaCT software. For each scan, volumetric data was acquired over 20 seconds in a single rotation of the C-arm mounted flat-panel detector cone-beam CT system. The data set was imported into the CyberKnife TPS and co-registered with the treatment planning CT, T2 MRI and Toshiba dCTA. Delineation of the AVM nidus was performed by the multi-disciplinary AVM team. RESULTS: There were no adverse events related to the angiography or radiosurgery treatment. CBCTA data sets created using DynaCT were accurately co-registered with the treatment planning scans in the CyberKnife treatment planning system (Multiplan). For all 4 patients, feeding arteries, draining veins and nidi were clearly visualized and used to develop radiosurgery plans. Mean nidus size was 0.45cc (range: 0.07 - 1.00cc). CONCLUSIONS: For intracranial micro-AVMs and AVMs otherwise poorly visualized using DSA, MRA, CTA or dCTA, selective and super-selective CBCTA images (created using DynaCT) can be successfully imported into the CyberKnife TPS to assist in nidus delineation. Advancement of a micro-catheter into the feeding arteries to allow continuous contrast injection during volumetric scanning constitutes super-selective CBCTA. This technique provides superior visualization of micro-AVMs and should be utilized for radiosurgery planning of poorly visualized AVMs.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".