Learned k-space Partitioning for Optimized Self-Supervised MRI Reconstruction
Bibliographic record
Abstract
Self-supervised magnetic resonance imaging (MRI) reconstruction methods train deep learning networks without the need for fully sampled reference data. One such approach, self-supervised via data under-sampling (SSDU), partitions under-sampled k-space into two disjoint sets, with a neural network mapping between them. However, SSDU and its variants rely on heuristic k-space partitioning, which may lead to suboptimal performance and necessitates new partitioning schemes when initial under-sampling patterns change. In this work, we propose a novel approach to learn optimal k-space partitioning by modeling a probability distribution which we use for partitioning. Specifically, we employ the LOUPE framework to learn an optimal partitioning probability distribution. Furthermore, we introduce a weighted dual-domain self-supervised loss function that incorporates both k-space and image-space loss terms. Evaluations on the fastMRI dataset demonstrate that our dual-domain learned partitioning method outperforms existing partitioning strategies and adapts to new sampling patterns without requiring hand-picked partitioning methods.Clinical Relevance- Typical clinical MRI protocols are under-sampled and reconstructed using parallel imaging. Self-supervised reconstruction can train directly on under-sampled clinical data, eliminating the need for separately acquired fully sampled datasets.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".