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 [1].
Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model
Authors: Jiahua Xu, Dawei Zhou, Lei Hu, Jianfeng Guo, Feng Yang, Zaiyi Liu, Nannan Wang, Xinbo Gao
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative experiments are performed on datasets from different tissues and show that our method achieves superior performance on several metrics. Qualitative evaluations with radiologists also show that our method provides better clinical feedback. |
| Researcher Affiliation | Academia | 1State Key Laboratory of Integrated Services Networks, Xidian University, Xi an, China 2Department of Radiology, Guangdong Provincial People s Hospital, Southern Medical University, Guangzhou, China 3Department of Radiology, Xiangyang No. 1 People s Hospital, Hubei University of Medicine, Xiangyang, China 4Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China |
| Pseudocode | Yes | Algorithm 1: PFAD for motion artifact removal Input: xori, x T N (0, I), Φh, Φl, a, Dθ, { αi}T 1 , {mj}T 1 . Output: The motion-free image ex0. 1: for i = T to 1 do xi 1 Dθ(xi) 3: ωi 1 αi 4: Mi ωi mi 5: Φl(fx i 1) Φl(fxori) 6: Φh(fx i 1) Φh(fxori) Mi + Φh(fxi 1) (1 Mi) 7: x i 1 |F 1(Φh(fx i 1) + Φl(fx i 1))| Frequency domain 8: ϵ N(0, I) 9: xfor i 1 αi xori + 1 αi ϵ 10: x i 1 xfor i 1 Mi + xi 1 (1 Mi) Pixel domain 11: γi a e i T + 1 12: exi 1 γi x i 1 + (1 γi) x i 1 Dual domain balance 13: xi 1 exi 1 14: end for 15: return ex0 |
| Open Source Code | Yes | Code https://github.com/medcx/PFAD |
| Open Datasets | Yes | We utilize two public datasets and one private dataset for the evaluation and validation of PFAD. The first dataset is the Human Connectome Project (HCP) data (Van Essen et al. 2012), which is a dataset containing MRI data of the human brain, and the second dataset is the knee MRI data from fast MRI (Zbontar et al. 2018). |
| Dataset Splits | No | The paper mentions using public datasets (HCP, fast MRI) and a private one, and refers to "test dataset" in table captions, but it does not specify the exact percentages, sample counts, or methodology for training, validation, and test splits within the main text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiments, we choose the value of a for the best case of the total metric, where a is equal to 0.7. |