Evaluation of 4D cone-beam CT reconstruction methods for lung images acquired using rapid cone-beam CT acquisition: a phantom study
Bibliographic record
Abstract
Abstract Objective. Cone-beam CT (CBCT) technological advances for linear accelerators (Linacs) have led to CBCT imaging in <20 s, which can reduce radiation therapy treatment times. However, these rapid CBCT scans only allow for 3DCBCT images. In this paper we evaluate 4DCBCT reconstruction methods for rapid acquisition 3DCBCT protocol scans using an anthropomorphic breathing phantom. Approach. We evaluate two previously developed motion-compensated Feldkamp-Davis-Kress (MCFDK) methods, using a prior-motion model (MCFDK-Prior) and a data-driven MCFDK method (MCFDK-DD), on CBCT images of the phantom using an Ethos linac. The deformable phantom lungs contained three synthetic tumours and a commercial phantom motion platform with a sinusoidal breathing pattern. The phantom was imaged in free-breathing with rapid (16.6 s) and standard (30.8 s) thorax 3DCBCT acquisition protocols, then reimaged while stationary at inhale and exhale, which were the ground truth reconstructions. MCFDK reconstructions were compared with conventional 3D-FDK and 4D-FDK reconstructions. Image quality was compared between all reconstructions using mean square error (MSE), structural similarity index measurement (SSIM), peak signal-to-noise (PSNR), edge response width (ERW) for the diaphragm-lung border for the right lung, tumour centroid accuracy, tumour dice similarity coefficient and sphericity. Main results. For all metrics the MCFDK-Prior reconstructions performed better than the 3D-FDK reconstructions. Similarly for all tumour-related metrics as well as ERW the MCFDK-DD reconstructions performed better than then 3D-FDK reconstructions, but the overall MSE, SSIM and PSNR were similar for the MCFDK-DD and 3D-FDK reconstructions. For all metrics except for tumour centroid error the MCFDK-Prior method produced better quality reconstructions than the MCFDK-DD method. 4D-FDK reconstructions produced poor quality volumes. Significance. We demonstrated that 4DCBCT reconstruction for rapid CBCT acquisition protocols is possible and leads to reduced motion artefacts and more accurate reconstructions when compared to 3DCBCT reconstructions. The 4DCBCT methods demonstrated in this paper will allow for fast, accurate 4DCBCT acquisition for new linacs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".