The impact of tumor motion upon CT image integrity and target delineation
Bibliographic record
Abstract
Accurate planning target volume delineation is vital to the success of conformal radiation techniques such as standard three-dimensional conformal radiotherapy and intensity modulated radiation therapy. With the exception of breath-hold schemes, all current approaches acquire images while the tumor is nonstationary and, as such, are subject to the presence of motion artifacts. In lung cancer sites where tumor mobility can be significant, the detrimental effect of these motion-induced distortions on image quality and subsequently target volume delineation cannot be ignored in the pursuit of improved treatment outcomes. To investigate the fundamental nature and functional dependence of computed tomography (CT) artifacts associated with lung tumor motion, and the implications for tumor delineation, a filtered backprojection algorithm was developed in MATLAB to generate transverse CT simulation images. In addition, a three-dimensional phantom capable of mimicking the essential motions of lung tumors was constructed for experimental verification. Results show that the spatial extent of a mobile object is distorted from its true shape and location and does not accurately reflect the volume occupied during the extent of motion captured. The presence of motion also negatively impacts image intensity (density) integrity rendering accurate volume delineation highly problematic and calling into question the use of such data in CT-based heterogeneity correction algorithms for dosimetric calculation.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".