Feasibility study of a synchronized‐moving‐grid (SMOG) system to improve image quality in cone‐beam computed tomography (CBCT)
Bibliographic record
Abstract
PURPOSE: To evaluate the feasibility of a synchronized moving grid (SMOG) system to remove scatter artifacts, improve the contrast-to-noise ratio (CNR), and reduce image lag artifacts in cone-beam CT (CBCT). METHODS: The SMOG system proposed here uses a rapidly oscillating, synchronized moving grid attached to the kV source. Multiple partial projections are taken at different grid positions to form a complete projection in each gantry position, before the gantry moves to the next position during a scan. The grid has a low transmission factor, and it is used for both scatter reduction and scatter measurement for postscan scatter correction. Experimental studies using a static grid and an enlarged CATphan phantom were performed to evaluate the potential CNR enhancement for different SMOG exposure numbers (1, 2, and 4). Simulation studies were performed to evaluate the image lag correction for different exposure numbers (2, 3, and 4) and grid interspace widths in SMOG using the data from an anthropomorphic pelvis phantom scan. Imaging dose of SMOG was also estimated by measuring the imaging dose in a CIRS CT dose phantom using a static grid. RESULTS: SMOG can enhance the CNR by 16% and 13% when increasing exposure number from 1 to 2 and from 2 to 4, respectively. This enhancement was more dramatic for larger phantoms and smaller initial exposure numbers. Simulation results indicated that SMOG could reduce the lag to less than 4.3% for 2-exposure mode and to less than 0.8% for 3-exposure mode when the grid interspace width was 1.4 cm. Increasing the number of exposures in SMOG dramatically reduced the residual lag in the image. Reducing the grid interspace width somewhat reduced the residual lag. Skin line artifacts were removed entirely in SMOG. Point dose measurement showed that imaging dose of SMOG at isocenter was similar as that of a conventional CBCT. CONCLUSIONS: Compared to our previously developed static-grid dual-rotation method, the proposed SMOG technique has the advantages of enhancing the CNR, correcting the image lag, and reducing the delivery time. Once implemented, SMOG has the potential to remove scatter and image lag artifacts, and significantly enhance CNR for CBCT using the same scanning time as conventional CBCT.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".