A network‐assisted joint image and motion estimation approach for robust <scp>3D MRI</scp> motion correction across severity levels
Bibliographic record
Abstract
Abstract Purpose The purpose of this work was to develop and evaluate a novel method that leverages neural networks and physical modeling for 3D motion correction at different levels of corruption. Methods The novel method (“UNet+JE”) combines an existing neural network (“UNet mag ”) with a physics‐informed algorithm for jointly estimating motion parameters and the motion‐compensated image (“JE”). UNet mag and UNet+JE were trained on two training datasets separately with different distributions of motion corruption severity and compared to JE as a benchmark. All five resulting methods were tested on T 1 w 3D MPRAGE scans of healthy participants with simulated ( n = 40) and in vivo ( n = 10) motion corruption ranging from mild to severe motion. Results UNet+JE provided better motion correction than UNet mag ( for all metrics for both simulated and in vivo data), under both training datasets. UNet mag exhibited residual image artifacts and blurring, as well as greater susceptibility to data distribution shifts than UNet+JE. UNet+JE and JE did not significantly differ in image correction quality ( for all metrics), even under strong distribution shifts for UNet+JE. However, UNet+JE reduced runtimes by a median reduction factor of between 2.00 to 3.80 as well as 4.05 for the simulation and in vivo studies, respectively. Conclusions UNet+JE benefitted from the robustness of joint estimation and the fast image improvement provided by the neural network, enabling the method to provide high quality 3D image correction under a wide range of motion corruption within shorter runtimes.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".