Motion artifact detection in four-dimensional computed tomography images
Bibliographic record
Abstract
Motion artifacts appear in four-dimensional computed tomography (4DCT) images because of suboptimal acquisition parameters or patient breathing irregularities. Frequency of motion artifacts is high and they may introduce errors in radiation therapy treatment planning. Motion artifact detection can be useful for image quality assessment and 4D reconstruction improvement but manual detection in many images is a tedious process. We propose a novel method to evaluate the quality of 4DCT images by automatic detection of motion artifacts. The method was used to evaluate the impact of the optimization of acquisition parameters on image quality at our institute. 4DCT images of 114 lung cancer patients were analyzed. Acquisitions were performed with a rotation period of 0.5 seconds and a pitch of 0.1 (74 patients) or 0.081 (40 patients). A sensitivity of 0.70 and a specificity of 0.97 were observed. End-exhale phases were less prone to motion artifacts. In phases where motion speed is high, the number of detected artifacts was systematically reduced with a pitch of 0.081 instead of 0.1 and the mean reduction was 0.79. The increase of the number of patients with no artifact detected was statistically significant for the 10%, 70% and 80% respiratory phases, indicating a substantial image quality improvement.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 0.001 |
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".