TH‐C‐BRA‐02: A Novel Technology for Volume‐Of‐Interest KV Cone Beam CT
Bibliographic record
Abstract
Purpose: In this work we describe the development of a new technology allowing volume‐of‐interest (VOI) kV CBCT. A device for dynamic kV beam collimation is described and initial imaging and dosimetric results are given. Methods: We have manufactured a prototype of an iris aperture capable of tracking a chosen VOI during kV CBCT imaging. The aperture and housing is compact and occupies an area comparable to the bow‐tie filter when mounted to the source of an on‐board imaging system. The system allows two‐dimensional translation of the aperture as a function of gantry angle and dynamic adjustment of iris diameter. Iris leaves are non‐overlapping and therefore can be made sufficiently thick to attenuate higher energy kV beams used. The device was tested for functionality on a Varian OBI system. Dose reduction in the VOI was measured and compared to full‐field imaging, for planar and CBCT imaging protocols, as a function of iris dimension. Initial projection and CBCT images of a head phantom were acquired. Results: The system provides a versatile tool for dynamic collimation of the kV beam during CBCT. While dose reduction in VOI CBCT is achieved primarily by blocking of non‐VOI anatomy, the dose inside the imaged VOI is also reduced substantially, by 5% to 60% for 20 cm and 2 cm iris diameters, respectively. The magnitude of dose reduction increases slightly with increased tube potential, between 65 and 125 kVp. Projection images demonstrate that the iris produces effective collimation with minimal leakage. Reconstructed CBCT images are affected by truncation artefact, but this may be remedied by data filling techniques or other (e.g. pi‐line) reconstruction algorithms. Conclusions: The technology presented is a novel approach to kV CBCT, localizing imaging dose to a chosen VOI while, compared to full‐field CBCT, reducing dose both inside and outside of the VOI. No conflict of interest exists.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".