Dynamic MR-based respiratory motion compensation for hybrid PET/MR system
Bibliographic record
Abstract
This paper describes a novel respiratory motion compensation (MC) technique for hybrid PET/MR system. Existing PET/MR respiratory MC techniques mainly rely on respiratory gating to reconstruct 3D MR images for different respiratory phases, and use image registration techniques to estimate the deformation between different respiratory phases. A well known limitation of respiratory gating is its assumption that breathing motion is perfectly periodic, therefore MC techniques based on gated MR are sensitive to irregular breathing patterns. To address this limitation, the proposed technique uses dynamic 2D MRIs for PET/MR respiratory motion estimation. A static 3D MRI and a series of dynamic 2D MRIs are acquired before and during the PET data acquisition, respectively. The dynamic 2D MRIs are registered against the static 3D MRI using a novel deformable 2D+t to 3D image registration method to derive a 3D+t deformation field, which is then used for motion corrected PET reconstruction. The proposed method is validated on synthetic PET/MR data and real MR data, demonstrating its efficacy to compensate for irregular respiratory motion and to deblur the PET image.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".