A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic\n Resonance Image Reconstruction
Bibliographic record
Abstract
Decreasing magnetic resonance (MR) image acquisition times can potentially\nreduce procedural cost and make MR examinations more accessible. Compressed\nsensing (CS)-based image reconstruction methods, for example, decrease MR\nacquisition time by reconstructing high-quality images from data that were\noriginally sampled at rates inferior to the Nyquist-Shannon sampling theorem.\nIn this work we propose a hybrid architecture that works both in the k-space\n(or frequency-domain) and the image (or spatial) domains. Our network is\ncomposed of a complex-valued residual U-net in the k-space domain, an inverse\nFast Fourier Transform (iFFT) operation, and a real-valued U-net in the image\ndomain. Our experiments demonstrated, using MR raw k-space data, that the\nproposed hybrid approach can potentially improve CS reconstruction compared to\ndeep-learning networks that operate only in the image domain. In this study we\ncompare our method with four previously published deep neural networks and\nexamine their ability to reconstruct images that are subsequently used to\ngenerate regional volume estimates. We evaluated undersampling ratios of 75%\nand 80%. Our technique was ranked second in the quantitative analysis, but\nqualitative analysis indicated that our reconstruction performed the best in\nhard to reconstruct regions, such as the cerebellum. All images reconstructed\nwith our method were successfully post-processed, and showed good volumetry\nagreement compared with the fully sampled reconstruction measures.\n
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".