Megavoltage cone beam digital tomosynthesis (MV-CBDT) for image-guided radiotherapy: a clinical investigational system
Bibliographic record
Abstract
Cone beam digital tomosynthesis (CBDT) is a new imaging technique proposed recently as a rapid approach for creating tomographic images of a patient in the radiotherapy treatment room. The purpose of this work is to investigate the feasibility of performing megavoltage (MV) CBDT clinically. A clinical investigational MV-CBDT system was installed on an existing LINAC. After the installation, the treatment machine can be operated in two distinct modes: (1) normal clinical treatment mode; (2) CBDT mode, in which tomographic images of the patient can be obtained using MV-CBDT. Various calibration and phantom measurements were performed on the system, followed by a patient study. Our phantom measurements have shown that: (1) for the same imaging dose, MV-CBDT has the same signal-difference-to-noise ratio as megavoltage cone beam computed tomography (MV-CBCT); (2) MV-CBDT has a better spatial resolution than MV-CBCT in the planes of reconstruction but a worse spatial resolution in the direction perpendicular to the planes of reconstruction. MV-CBDT patient images were also obtained and compared to that of MV-CBCT. We have demonstrated that it is clinically feasible to perform MV-CBDT in the treatment room for image-guided radiotherapy.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 |
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".