MO‐D‐304A‐04: Cone‐Beam CT Lag Correction Models: Effect of Optimized Parameter Selection
Bibliographic record
Abstract
Purpose: Image lag degrades image quality in cone‐beam CT (CBCT). This work investigates the magnitude of lag artifacts and develops an optimized lag coefficient model to correct lag artifacts in CBCT images to improve guidance of radiotherapy(Elekta Synergy XVI). Method and Materials: Image lag and its relationship with various parameters including signal strength and frame number was investigated for a PerkinElmer (RID1640) flat‐panel imager. A new lag correction model referred to as the “average‐optimized lag coefficients model” (ALCM) is developed to correct CBCT images. The optimization of lag coefficients was purely based on quantitative improvement in lag corrected CBCT images. Each projection was corrected for lag effects by subtracting previous projections weighted by the magnitude of image lag. The quantification/measurement of lag coefficients for four different techniques including RESF(Rising‐Edge‐Step‐Response‐Function), IRF(Impulse‐Response‐Function), FESF(Rising‐Edge‐Step‐Response‐Function) and ALCM(Average‐Optimized‐Lag‐Coefficient‐Model) for the same detector. These models are applied/tested in correcting CBCT images of two customs made phantoms referred to as Ellipse_Lucite (MTF and skinline) and Irregular_Lucite (CNR). Results: Experimental results illustrate that the nth frame lag of the imager for all four model shows different behavior with frame number. The RCTN at 5 mm depth after lag correction was measured in CT♯ as 4.38±1.01, 10.51±1.35, 8.35±1.31 and 2.121±0.81 for RESF, IRF, FESF and ALCM, respectively. Similarly, the spatial frequency/cm for MTF(50%) before and after lag correction for RESF, IRF, FESF and ALCM was measured as 6.3±0.24, 5.7±0.23, 5.8±0.22 and 6.5±0.24, respectively. CNR for ALCMwas almost two times higher than nominal. Conclusion: Lag artifacts can be reduced by correction of the projection images using the ALCM model. Lag correction is most important for high contrast and irregularly shaped objects. The performance metrics suggest a significant improvement for RESF and ALCM and strongly support their use for lag correction in cone‐beam CT. Research sponsored by Elekta.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".