KDI-Net: dual-domain mapping network for undersampled magnetic resonance imaging reconstruction
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is an imaging technique that provides comprehensive anatomical and functional information on the human body, but prolonged acquisition of fully sampled MRI images causes patient discomfort and motion artifacts. In recent years, deep learning (DL) has made significant progress in accelerating MRI image reconstruction. At present, MRI image reconstruction is concentrated on single domain, but hybrid domain reconstruction has more advantages. Here, we propose a hybrid domain reconstruction method based on AUTOMAP—KDI-Net, which divided into three parts: K-Block, D-Block and I-Block. We evaluated the performance of our KDI-Net model for MRI image reconstruction using PSNR, RMSE, and SSIM metrics and three reduction factors (2, 3, 4) on two publicly available MRI datasets (the AUTOMAP brain dataset and the Calgary Campinas single-coil brain dataset). The results show that KDI-Net performs better than AUTOMAP and Complex AUTOMAP. Compared to AUTOMAP, KDI-Net basically improved each metric by more than 6%. Compared to Complex AUTOMAP, KDI-Net basically improved by more than 8% on each metric. For reduction factor 2, the reconstructed MRI image is very close to ground true. Our specially designed KDI-Net can extract the sparsity from single channel MRI K-space, which achieve 2 folder acceleration.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".