Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313443445> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W4313443445 endingPage "106513" @default.
- W4313443445 startingPage "106513" @default.
- W4313443445 abstract "Magnetic resonance imaging (MRI) is one of the most important modalities for clinical diagnosis. However, the main disadvantages of MRI are the long scanning time and the moving artifact caused by patient movement during prolonged imaging. It can also lead to patient anxiety and discomfort, so accelerated imaging is indispensable for MRI. Convolutional neural network (CNN) based methods have become the fact standard for medical image reconstruction, and generative adversarial network (GAN) have also been widely used. Nevertheless, due to the limited ability of CNN to capture long-distance information, it may lead to defects in the structure of the reconstructed images such as blurry contour. In this paper, we propose a novel Swin Transformer-based dual-domain generative adversarial network (SwinGAN) for accelerated MRI reconstruction. The SwinGAN consists of two generators: a frequency-domain generator and an image-domain generator. Both the generators utilize Swin Transformer as backbone for effectively capturing the long-distance dependencies. A contextual image relative position encoder (ciRPE) is designed to enhance the ability to capture local information. We extensively evaluate the method on the IXI brain dataset, MICCAI 2013 dataset and MRNet knee dataset. Compared with KIGAN, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are improved by 6.1% and 1.49% to 37.64 dB and 0.98 on IXI dataset respectively, which demonstrates that our model can sufficiently utilize the local and global information of image. The model shows promising performance and robustness under different undersampling masks, different acceleration rates and different datasets. But it needs high hardware requirements with the increasing of the network parameters. The code is available at: https://github.com/learnerzx/SwinGAN." @default.
- W4313443445 created "2023-01-06" @default.
- W4313443445 creator A5006651636 @default.
- W4313443445 creator A5013415135 @default.
- W4313443445 creator A5033000067 @default.
- W4313443445 creator A5048581170 @default.
- W4313443445 date "2023-02-01" @default.
- W4313443445 modified "2023-10-10" @default.
- W4313443445 title "SwinGAN: A dual-domain Swin Transformer-based generative adversarial network for MRI reconstruction" @default.
- W4313443445 cites W2119667497 @default.
- W4313443445 cites W2574952845 @default.
- W4313443445 cites W2594014149 @default.
- W4313443445 cites W2611467245 @default.
- W4313443445 cites W2778924750 @default.
- W4313443445 cites W2791621240 @default.
- W4313443445 cites W2883105305 @default.
- W4313443445 cites W2902719825 @default.
- W4313443445 cites W2902874468 @default.
- W4313443445 cites W2905566673 @default.
- W4313443445 cites W2942173509 @default.
- W4313443445 cites W3001319253 @default.
- W4313443445 cites W3034514764 @default.
- W4313443445 cites W3101674918 @default.
- W4313443445 cites W3162980553 @default.
- W4313443445 cites W3177540801 @default.
- W4313443445 cites W3203764003 @default.
- W4313443445 cites W3204069956 @default.
- W4313443445 cites W4223589960 @default.
- W4313443445 cites W4226133625 @default.
- W4313443445 doi "https://doi.org/10.1016/j.compbiomed.2022.106513" @default.
- W4313443445 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36603439" @default.
- W4313443445 hasPublicationYear "2023" @default.
- W4313443445 type Work @default.
- W4313443445 citedByCount "6" @default.
- W4313443445 countsByYear W43134434452023 @default.
- W4313443445 crossrefType "journal-article" @default.
- W4313443445 hasAuthorship W4313443445A5006651636 @default.
- W4313443445 hasAuthorship W4313443445A5013415135 @default.
- W4313443445 hasAuthorship W4313443445A5033000067 @default.
- W4313443445 hasAuthorship W4313443445A5048581170 @default.
- W4313443445 hasConcept C104317684 @default.
- W4313443445 hasConcept C108583219 @default.
- W4313443445 hasConcept C111919701 @default.
- W4313443445 hasConcept C118505674 @default.
- W4313443445 hasConcept C136536468 @default.
- W4313443445 hasConcept C153180895 @default.
- W4313443445 hasConcept C154945302 @default.
- W4313443445 hasConcept C185592680 @default.
- W4313443445 hasConcept C2988773926 @default.
- W4313443445 hasConcept C31972630 @default.
- W4313443445 hasConcept C41008148 @default.
- W4313443445 hasConcept C55493867 @default.
- W4313443445 hasConcept C63479239 @default.
- W4313443445 hasConcept C81363708 @default.
- W4313443445 hasConceptScore W4313443445C104317684 @default.
- W4313443445 hasConceptScore W4313443445C108583219 @default.
- W4313443445 hasConceptScore W4313443445C111919701 @default.
- W4313443445 hasConceptScore W4313443445C118505674 @default.
- W4313443445 hasConceptScore W4313443445C136536468 @default.
- W4313443445 hasConceptScore W4313443445C153180895 @default.
- W4313443445 hasConceptScore W4313443445C154945302 @default.
- W4313443445 hasConceptScore W4313443445C185592680 @default.
- W4313443445 hasConceptScore W4313443445C2988773926 @default.
- W4313443445 hasConceptScore W4313443445C31972630 @default.
- W4313443445 hasConceptScore W4313443445C41008148 @default.
- W4313443445 hasConceptScore W4313443445C55493867 @default.
- W4313443445 hasConceptScore W4313443445C63479239 @default.
- W4313443445 hasConceptScore W4313443445C81363708 @default.
- W4313443445 hasLocation W43134434451 @default.
- W4313443445 hasLocation W43134434452 @default.
- W4313443445 hasOpenAccess W4313443445 @default.
- W4313443445 hasPrimaryLocation W43134434451 @default.
- W4313443445 hasRelatedWork W2731899572 @default.
- W4313443445 hasRelatedWork W2732542196 @default.
- W4313443445 hasRelatedWork W2738221750 @default.
- W4313443445 hasRelatedWork W3116150086 @default.
- W4313443445 hasRelatedWork W3133861977 @default.
- W4313443445 hasRelatedWork W4200173597 @default.
- W4313443445 hasRelatedWork W4214561993 @default.
- W4313443445 hasRelatedWork W4312417841 @default.
- W4313443445 hasRelatedWork W4321369474 @default.
- W4313443445 hasRelatedWork W564581980 @default.
- W4313443445 hasVolume "153" @default.
- W4313443445 isParatext "false" @default.
- W4313443445 isRetracted "false" @default.
- W4313443445 workType "article" @default.