Accurate MRI Reconstruction via Multi-Domain Recurrent Networks
Authors: Jinbao Wei, Zhijie Wang, Kongqiao Wang, Li Guo, Xueyang Fu, Ji Liu, Xun Chen
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public fast MRI datasets demonstrate that our MDR-Net consistently outperforms other competitive methods and is able to provide more details. |
| Researcher Affiliation | Collaboration | 1University of Science and Technology of China 2Zepp Health |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that its code is released. |
| Open Datasets | Yes | fast MRI dataset [Zbontar et al., 2018]. CC359 dataset. The CC-359 Brain dataset[Souza et al., 2018] |
| Dataset Splits | Yes | The training, validation, and testing sets consisted of 973, 199 and 108 volumes, respectively. ... with 25 designated for training, 10 for validation, and 10 for testing. |
| Hardware Specification | Yes | All experiments are implemented using the Pytorch platform on two NVIDIA Ge Force GTX 3090 with 24GB GPU memory. |
| Software Dependencies | No | The paper mentions 'Pytorch platform' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | The hyper-parameter setting of the network is as follows: the entry channel number MDR-Net is c = 32, and the number of recurrent blocks is N = 4. All experiments are implemented using the Pytorch platform on two NVIDIA Ge Force GTX 3090 with 24GB GPU memory. Our network is trained with an RMSProp optimizer. The initial learning rate is 10 3 and reduce to 10 4 after 40 epochs. The batch size is set as 1, and the network is trained for 50 epochs to ensure convergence. |