Deep Learning Aided Motion Correction for Low-Dose Brain PET
Bibliographic record
Abstract
We have previously developed a deep learning (DL) aided PET motion correction method which successfully detected and corrected for head motion in the situation of a rapidly changing tracer distribution such as the initial tracer uptake period – immediately after injection. Here we extend this data driven method to a general low-dose scenario where tracer distribution is relatively constant but available counts are very low, such as for low-dose imaging. Data from four tracers were used ([11C]-RAC, [18F]-FDG, [11C]-PBR, and [11C]-DTBZ) to form a representative dataset for training and testing of a model capable of mapping low-count, high temporal resolution frames (acquired during a low-count period of the scan), to higher-count frames containing better structural features than the original low-count frames. The ability to recover a known motion trace that was artificially injected into low-count subject data was tested. The use of the DL model reduced the standard deviation of the motion estimation error from 0.5 mm and 0.8 degrees (when registration was performed without the aid of DL) to 0.3 mm and 0.5 degrees. These results were then validated with real human motion using the estimated motion from simultaneous fMRI acquisition as the ground truth.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".