Bibliographic record
Abstract
Supervised deep learning MRI reconstruction recovers images from accelerated MRI scans, but requires fully-sampled k-space for training. Self-supervised learning for MRI reconstruction bypasses the need for fully sampled k-space by partitioning under-sampled k-space into two disjoint sets, and training a neural network to predict one set from the other. Remarkably, self-supervised learning achieves performance similar to supervised learning without requiring fully sampled data. In this thesis, I enhance self-supervised learning methods through two key approaches: (1) learning an optimal k-space partitioning strategy, and (2) incorporating multi-contrast information into the reconstruction process. In the first approach, I trained a network to learn an optimal k-space partitioning probability distribution for self-supervised learning, outperforming previous heuristic-based methods. In the second approach, I demonstrate that integrating multiple MRI contrasts improves self-supervised reconstruction performance by leveraging correlated information across contrasts. I further improve the second approach by extending multi-contrast self-supervised learning to jointly learn an optimal k-space partitioning for each contrast. These proposed enhancements improve self-supervised reconstruction fidelity compared to previous single-contrast self-supervised learning methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".