Breaking Information Isolation: Accelerating MRI via Inter-sequence Mapping and Progressive Masking
Authors: Jianwei Zheng, Xiaomin Yao, Guojiang Shen, Wei Li, Jiawei Jiang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Massive experiments are conducted under various sampling patterns and acceleration rates, whose results demonstrate that, without any sophisticated architectures, our IMA outperforms the current cutting-edge methods both visually and numerically. Codes are available as an attachment and will be publicly released. |
| Researcher Affiliation | Academia | Jianwei Zheng, Xiaomin Yao, Guojiang Shen, Wei Li, and Jiawei Jiang* Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model optimization and network design using mathematical equations and descriptive text, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Codes are available as an attachment and will be publicly released. |
| Open Datasets | Yes | We evaluate our proposal on two publicly available datasets: IXI (578 registered T2 and PD image pairs, 256 256) and knee fast MRI (240 PD image pairs, 320 320). |
| Dataset Splits | Yes | The data is split into training, validation, and testing sets (7:1:2). |
| Hardware Specification | Yes | All experiments are conducted using Py Torch on an NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify a version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | For our IMA, we use l1-norm loss, the Adam optimizer (0.9, 0.999), 100 epochs, an initial learning rate of 10 4, batch size of 1, ratio=0.9, γ = 0.5, δ = 3, and T=12. |