Deep-Learning-Aided Intraframe Motion Correction for Low-Count Dynamic Brain PET
Bibliographic record
Abstract
Data-driven intraframe motion correction of a dynamic brain PET scan (with each frame duration on the order of minutes) is often achieved through the co-registration of high-temporal-resolution (e.g., 1-s duration) subframes to estimate subject head motion. However, this conventional method of subframe co-registration may perform poorly during periods of low counts and/or drastic changes in the spatial tracer distribution over time. Here, we propose a deep learning (DL), U-Net-based convolutional neural network model which aids in the PET motion estimation to overcome these limitations. Unlike DL models for PET denoising, a nonstandard 2.5-D DL model was used which transforms the high-temporal-resolution subframes into nonquantitative DL subframes which allow for improved differentiation between noise and structural/functional landmarks and estimate a constant tracer distribution across time. When estimating motion during periods of drastic change in spatial distribution (within the first minute of the scan, ~1-s temporal resolution), the proposed DL method was found to reduce the expected magnitude of error (+/−) in the estimation for an artificially injected motion trace from 16 mm and 7° (conventional method) to 0.7 mm and 0.6° (DL method). During periods of low counts but a relatively constant spatial tracer distribution (60th min of the scan, ~1-s temporal resolution), an expected error was reduced from 0.5 mm and 0.7° (conventional method) to 0.3 mm and 0.4° (DL method). The use of the DL method was found to significantly improve the accuracy of an image-derived input function calculation when motion was present during the first minute of the scan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".