Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Null Space Matters: Range-Null Decomposition for Consistent Multi-Contrast MRI Reconstruction
Authors: Jiacheng Chen, Jiawei Jiang, Fei Wu, Jianwei Zheng
AAAI 2024 | Venue PDF | 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 EMAIL |
| 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. |