Sci‐AM1 Sat ‐ 05: Fractal and motion modeling of PET/CT tumours
Bibliographic record
Abstract
Objective: To generate realistic computer‐simulated PET/CT tumour images with characteristics typical of the tumour (shape, pixel intensity and movement over time) and of the imaging system (acquisition parameters and resolution). Methods: An initial tumour volume was segmented from the CT scan of a lung cancer patient via region growing. To improve the axial resolution and mimic the contour curvature and pixel variations in the transaxial plane, fractal analysis and interpolation was used. Breathing motion with a period and extents typical of lung cancer patients was then applied to the fractal tumour. To produce PET and CT images, the moving tumour was sampled according to acquisition characteristics typical of the two modalities. For CT, a slice thickness of 3.0mm, acquisition time of 0.7s and resolution of 2mm was assumed. For PET, it was assumed that tumour motion was captured within one bed position over an acquisition time of 180s, and the imager resolution was 6mm. Results: Following fractal interpolation, the tumour exhibited similar curvature and gray‐level gradation in all three imaging planes. Application of motion and blurring produced CT images different in appearance from the stationary fractal tumour volume and the total volume traced by the moving object. Motion and blurring produced PET images similar to the volume traced by the moving object, graded by the time spent in each location within the volume and blurred due to the degraded resolution of the scanner. Conclusions: Realistic computer‐simulated PET/CT tumour images can be generated using fractal analysis and motion modeling.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".