A Denoising UNet Model with ConvNeXt Block for MRI Reconstruction
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) is an important auxiliary tool in clinical medicine, but its long imaging time remains a significant issue, making the reconstruction of high-quality images from fewer k-space data a key research direction. Recently, deep learning has achieved promising results in the field of MRI reconstruction. However, most current deep learning methods are based on traditional ConvNet, which are limited by their receptive field size and unable to capture global features of the image. Thus, this paper proposes a novel model named MRI ConvNeXt Blocks Network (MICXN). The model consists of two main components: (1) a V-Net-like architecture with ConvNeXt blocks (UCA), serving as the main part of the denoising network. This uses ConvNeXt blocks to provide larger convolutional kernels and receptive fields, better-preserving image details, and incorporates skip connections between the encoder and decoder to merge specific hierarchical features; (2) a Data Consistency (DC) layer to ensure the integrity of the original k-space data. We conducted both quantitative evaluation and qualitative analysis of our method on the Calgary-Campinas (CC) dataset, comparing it with other deep learning approaches. The experimental results indicate that our method achieves superior reconstruction performance even with low-sampled k-space data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".