{"type":"ScholarlyArticle","title":"Accurate MRI Reconstruction via Multi-Domain Recurrent Networks","authors":[{"name":"Jinbao Wei","slug":"jinbao-wei","given":"Jinbao","family":"Wei","affiliation":"University of Science and Technology of China; Zepp Health"},{"name":"Zhijie Wang","slug":"zhijie-wang","given":"Zhijie","family":"Wang","affiliation":"University of Science and Technology of China; Zepp Health"},{"name":"Kongqiao Wang","slug":"kongqiao-wang","given":"Kongqiao","family":"Wang","affiliation":"Zepp Health"},{"name":"Li Guo","slug":"li-guo","given":"Li","family":"Guo","affiliation":"Zepp Health"},{"name":"Xueyang Fu","slug":"xueyang-fu","given":"Xueyang","family":"Fu","affiliation":"University of Science and Technology of China"},{"name":"Ji Liu","slug":"ji-liu","given":"Ji","family":"Liu","affiliation":"University of Science and Technology of China"},{"name":"Xun Chen","slug":"xun-chen","given":"Xun","family":"Chen","affiliation":"University of Science and Technology of China"}],"abstract":"In recent years, deep convolutional neural networks (CNNs) have become dominant in MRI reconstruction from undersampled k-space. However, most existing CNNs methods reconstruct the undersampled images either in the spatial domain or in the frequency domain, and neglecting the correlation between these two domains. This hinders the further reconstruction performance improvement. To tackle this issue, in this work, we propose a new multi-domain recurrent network (MDR-Net) with multi-domain learning (MDL) blocks as its basic units to reconstruct the undersampled MR image progressively. Specifically, the MDL block interactively processes the local spatial features and the global frequency information to facilitate complementary learning, leading to fine-grained features generation. Furthermore, we introduce an effective frequency-based loss to narrow the frequency spectrum gap, compensating for over-smoothness caused by the widely used spatial reconstruction loss. Extensive experiments on public fastMRI datasets demonstrate that our MDR-Net consistently outperforms other competitive methods and is able to provide more details.","doi":"10.24963/ijcai.2023/169","url":"https://ijcai.org/proceedings/2023/169","pdf":"https://ijcai.org/proceedings/2023/0169.pdf","markdown":"https://ijcai.org/proceedings/2023/169.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/169","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":169,"pages":{"first":1524,"last":1532},"keywords":["Computer Vision: CV: Biomedical image analysis","Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal et al., 2018] Hemant K Aggarwal, Merry P Mani, and Mathews Jacob. 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