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In recent years, many noise removal algorithms with impressive performances have been proposed. In this work, inspired by the idea of deep learning, we propose a denoising method named 3D\u2010Parallel\u2010RicianNet, which will combine global and local information to remove noise in MR images. Specifically, we introduce a powerful dilated convolution residual (DCR) module to expand the receptive field of the network and to avoid the loss of global features. Then, to extract more local information and reduce the computational complexity, we design the depthwise separable convolution residual (DSCR) module to learn the channel and position information in the image, which not only reduces parameters dramatically but also improves the local denoising performance. In addition, a parallel network is constructed by fusing the features extracted from each DCR module and DSCR module to improve the efficiency and reduce the complexity for training a denoising model. Finally, a reconstruction (REC) module aims to construct the clean image through the obtained noise deviation and the given noisy image. Due to the lack of ground\u2010truth images in the real MR dataset, the performance of the proposed model was tested qualitatively and quantitatively on one simulated T1\u2010weighted MR image dataset and then expanded to four real datasets. The experimental results show that the proposed 3D\u2010Parallel\u2010RicianNet network achieves performance superior to that of several state\u2010of\u2010the\u2010art methods in terms of the peak signal\u2010to\u2010noise ratio, structural similarity index, and entropy metric. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation.<\/jats:p>","DOI":"10.1155\/2021\/5577956","type":"journal-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T20:13:14Z","timestamp":1620159194000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5988-4021","authenticated-orcid":false,"given":"Liang","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1442-0976","authenticated-orcid":false,"given":"Shunbo","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3424-7151","authenticated-orcid":false,"given":"Changchun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,5,4]]},"reference":[{"key":"e_1_2_8_1_2","unstructured":"ZhangZ. 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