Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction
Authors: Pengcheng Lei, Faming Fang, Guixu Zhang, Ming Xu
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our MC-CDic model on multicontrast MRI SR and reconstruction tasks. Experimental results demonstrate the superior performance of the proposed MC-CDic model against existing SOTA methods. Code is available at https://github.com/lpcccc-cv/MC-CDic. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, East China Normal University 2Software Engineering Institute, East China Normal University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/lpcccc-cv/MC-CDic. |
| Open Datasets | Yes | Following [Lyu et al., 2020; Fang et al., 2022], we employ two publicly available multi-modal MR image datasets, IXI and Bra TS2018 [Menze et al., 2015] to validate the effectiveness of our model. |
| Dataset Splits | Yes | We splitted the two datasets patient-wisely into a ratio of 7:1:2 for training/validation/testing. |
| Hardware Specification | Yes | Our proposed MC-CDic model is implemented in PyTorch with an NVIDIA RTX3090 GPU. |
| Software Dependencies | No | The paper mentions "PyTorch" but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | We adopt the Adam optimizer with a batch size of 6. The learning rate is set to 1e-4 and the models are trained 50 epochs. |