Accuracy of respiratory motion compensated image reconstruction using 4DPET-derived deformation fields
Bibliographic record
Abstract
PET quantitation in the thorax and upper abdomen is confounded by artifacts introduced by breathing motion. This has led to the emergence of a variety of techniques for motion compensated image reconstruction, many of which rely on motion information computed from a series of respiratory-gated anatomical images synchronized with the PET gates. A simpler alternative to this approach is to derive deformation fields directly from non-attenuation-corrected gated 4DPET images. The goal of this paper is to assess the accuracy of motion compensated image reconstruction based on PET-derived motion information using the ground truth and anatomically derived motion information as references. We used the Monte Carlo simulation software GATE to generate realistic PET images of the XCAT phantom with pulmonary lesions. Deformation fields were derived from 4DPET images in two passes. In the first pass, the fields were estimated from 4D non-attenuation-corrected PET images. In the second pass, 4D attenuation maps were generated using the first-pass motion estimates and then used to reconstruct 4D attenuation-corrected PET images. A second set of deformation fields were then computed from the 4D attenuation-corrected PET images. The PET-derived deformation fields were compared with the true XCAT deformations. For more realistic validation, gated pseudo-MR images were generated from the XCAT phantom. We then compared MR-derived deformation fields with the true deformations. Finally, motion compensated PET images were reconstructed using the different motion estimates, and error estimates were computed for individual organs and lesions.
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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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Open science | 0.001 | 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".