Static MRI Reconstruction Based on K-space and Image-domain Information
Bibliographic record
Abstract
In order to accelerate the imaging process of MRI (Magnetic Resonance Imaging), CS-MRI (Compressed Sensing Magnetic Resonance Imaging) utilizes the prior information of MR images and reconstructs under-sampled MR images by solving an optimization problem through an iterative algorithm, and it is time consuming. With the emergence of deep learning technology, more and more optimization problems can be solved through well-designed networks and large amounts of training data. Therefore, DL-Based (Deep Learning-Based) methods have attracted much attention. However, most of the current work simply treats MRI reconstruction as an image-to-image task, so there is still a lot of room for improvement in how to better integrate MRI information. This paper proposes a new DL-based method, which reconstructs the MR images in both k-space and image domain using several sub-networks and fuses information among different sub-networks. We also used down-sampling and up-sampling pairs to reduce the computational complexity. Our proposed architecture obtained promising experimental results on Calgary-Campinas Brain MRI 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 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".