Null Space Matters: Range-Null Decomposition for Consistent Multi-Contrast MRI Reconstruction
Authors: Jiacheng Chen, Jiawei Jiang, Fei Wu, Jianwei Zheng
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The quantitative and qualitative results show that our proposal outperforms most cuttingedge methods by a large margin. Codes will be released on https://github.com/chenjiachengzzz/RNU. Experiments Datasets and Implementation Details Results and Analysis Ablation Experiments |
| Researcher Affiliation | Academia | College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China zjw@zjut.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes will be released on https://github.com/chenjiachengzzz/RNU. |
| Open Datasets | Yes | Datasets: The classical IXI dataset and the currently largest fast MRI (Zbontar et al. 2018) dataset are employed for performance evaluation. |
| Dataset Splits | No | The paper mentions 'training data' and uses 'validation' as a term for evaluating performance, but it does not explicitly provide specific dataset split information (percentages, sample counts, or detailed splitting methodology) for training, validation, and test sets. |
| Hardware Specification | Yes | The proposed RNU is implemented using Py Torch and evaluated with NVIDIA 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' as implementation software, but it does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | The Adam optimizer is utilized for model training, with an initial learning rate lr = 10 4 that is gradually decayed to 10 6 over 50 epochs. The batch size is set as 4. To facilitate a better generalization, the training data are randomly augmented by flipping horizontally or vertically and rotating at different angles. L1 loss is used to optimize the network. To ensure a fair comparison, all competing approaches are trained using the finely tuned parameter settings. Unless specified otherwise, the stage number K is 8. |