Data-Driven Motion Correction Strategy for Dynamic Brain PET using Ultra-fast List-mode Reconstruction
Bibliographic record
Abstract
We describe a data-driven rigid motion correction strategy for dynamic brain PET based on an ultra-fast list-mode reconstruction. Short snap non-attenuation corrected (NAC) PET images were reconstructed using the ultra-fast list-mode reconstruction, and motion estimates were obtained using image-based rigid registration. To minimize the impact due to change in the PET tracer distribution on the registration accuracy, we use our study specific framing protocol to define the reference images for the intra-frame motion estimation. We further optimize the intra-frame motion estimation by updating the reference images with the intra-frame motion corrected NAC PET (i.e. iterative motion estimate). Then we estimate the inter-frame motion using the intra-frame motion corrected NAC PET frames. We validate our proposed strategy using [11C]RAC on GE SIGNA PET/MR. The count limit to obtain reliable motion estimate for [11C]RAC was determined to be 1.5 x 105trues+scattered events. The iterative motion estimation produced more consistent motion trace with higher detection sensitivity than the non-iterative approach due to the less motion blurred reference images. The improvement in time-activity-curve with both inter- and intra-frame motion corrections outperformed that with inter-frame motion correction only as expected. All in all, this work demonstrated promising results using the ultra-fast list-mode reconstruction to estimate and correct subject motion with the proposed strategy for dynamic brain PET without requiring external motion tracking devices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".