Show simple item record

dc.contributor.authorHuang, Jiahao
dc.contributor.authorDing, Weiping
dc.contributor.authorLv, Jun
dc.contributor.authorYang, Jingwen
dc.contributor.authorDong, Hao
dc.contributor.authorDel Ser, Javier
dc.contributor.authorXia, Jun
dc.contributor.authorRen, Tiaojuan
dc.contributor.authorWong, Stephen T.
dc.contributor.authorYang, Guang
dc.date.accessioned2022-03-20T21:44:35Z
dc.date.available2022-03-20T21:44:35Z
dc.date.issued2022-01-28
dc.identifier.citationHuang, Jiahao, Weiping Ding, Jun Lv, Jingwen Yang, Hao Dong, Javier Del Ser, Jun Xia, Tiaojuan Ren, Stephen T. Wong, and Guang Yang. “Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-View Information.” Applied Intelligence (January 28, 2022). doi:10.1007/s10489-021-03092-w.en
dc.identifier.issn0924-669Xen
dc.identifier.urihttp://hdl.handle.net/11556/1290
dc.description.abstractIn clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing.en
dc.description.sponsorshipThis work was supported in part by the Zhejiang Shuren University Basic Scientifc Research Special Funds, in part by the European Research Council Innovative Medicines Initiative (DRAGON, H2020-JTI-IMI2 101005122), in part by the AI for Health Imaging Award (CHAIMELEON, H2020-SC1-FA-DTS-2019-1 952172), in part by the UK Research and Innovation Future Leaders Fellowship (MR/V023799/1), in part by the British Heart Foundation (Project Number: TG/18/5/34111, PG/16/78/32402), in part by the Foundation of Peking University School and Hospital of Stomatology [KUSSNT-19B11], in part by the Peking University Health Science Center Youth Science and Technology Innovation Cultivation Fund [BMU2021PYB017], in part by the National Natural Science Foundation of China [61976120], in part by the Natural Science Foundation of Jiangsu Province [BK20191445], in part by the Qing Lan Project of Jiangsu Province, in part by National Natural Science Foundation of China [61902338], in part by the Project of Shenzhen International Cooperation Foundation [GJHZ20180926165402083], in part by the Basque Government through the ELKARTEK funding program [KK-2020/00049], and in part by the consolidated research group MATHMODE [IT1294-19].en
dc.language.isoengen
dc.publisherSpringeren
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleEdge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view informationen
dc.typearticleen
dc.identifier.doi10.1007/s10489-021-03092-wen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/952172/EU/Accelerating the lab to market transition of AI tools for cancer management/CHAIMELEONen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/101005122/EU/The RapiD and SecuRe AI enhAnced DiaGnosis, Precision Medicine and Patient EmpOwerment Centered Decision Support System for Coronavirus PaNdemics/DRAGONen
dc.rights.accessRightsopenAccessen
dc.subject.keywordsFast MRIen
dc.subject.keywordsParallel imagingen
dc.subject.keywordsMulti-view learningen
dc.subject.keywordsGenerative adversarial networksen
dc.subject.keywordsEdge enhancementen
dc.identifier.essn1573-7497en
dc.journal.titleApplied Intelligenceen


Files in this item

Thumbnail

    Show simple item record

    Attribution 4.0 InternationalExcept where otherwise noted, this item's license is described as Attribution 4.0 International