SU‐DD‐A3‐04: Quantitative Evaluation of Cone Beam Digital Tomosynthesis (CBDT) for Image‐Guided Radiation Therapy
Bibliographic record
Abstract
Purpose: Cone beam digital tomosynthesis (CBDT) is a new imaging technique proposed by us recently as a rapid approach for creating cross sectional images of a patient in the radiotherapy treatment room. Similar to the cone beam computed tomography (CBCT) approach, the CBDT uses an X‐ray source and an X‐ray detector on a Linac to acquire projection data by rotation around the patient. Unlike CBCT, CBDT utilizes partial scans, typically in the range of 20–60 degrees of gantry arc. The purposes of this work are (1) to evaluate quantitatively the image quality of CBDT in terms of signal‐to‐noise ratio and spatial resolution; (2) to demonstrate that we can use CBDT to image soft tissue targets, e.g., the prostate. Method and Materials: An experimental CBDT system has been built on a Linac with a recently developed flat‐panel detector. A number of phantoms including a spatial resolution phantom, two contrast‐detail phantoms and a custom‐made anthropomorphic pelvic phantom (CIRS Inc.) were used in the evaluation. CBDT phantom images have been generated and analyzed for different degrees of gantry arc and compared to those from CBCT. Results: Quantitative results on signal‐to‐noise ratio and spatial resolution have been obtained for different degrees of gantry arc. Compared to CBCT, CBDT has a worse spatial resolution in the direction perpendicular to the planes of reconstruction but a better resolution in the parallel direction. The image quality of CBDT is acceptable in the planes most relevant to the treatment. It has been shown that the prostate is visible on CBDT reconstructed images of the pelvic phantom. Conclusion: This work indicates that the CBDT approach can be a viable and rapid tomographical imaging technique for treatment verification in radiotherapy. Conflict of Interest: This work was partially supported by Siemens Medical Solutions, Inc.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".