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Mitigating artifacts arising during image reconstruction and accelerating cardiac cine MRI acquisition to obtain high\u2010quality images is important. A novel end\u2010to\u2010end deep learning network is developed to improve cardiac cine MRI reconstruction. First, a U\u2010Net is adopted to obtain the initial reconstructed images in k\u2010space. Further to remove the motion artifacts, the motion\u2010guided deformable alignment (MGDA) module with second\u2010order bidirectional propagation is introduced to align the adjacent cine MRI frames by maximizing spatial\u2013temporal information to alleviate motion artifacts. Finally, the multi\u2010resolution fusion (MRF) module is designed to correct the blur and artifacts generated from alignment operation and obtain the last high\u2010quality reconstructed cardiac images. At an 8\u00d7 acceleration rate, the numerical measurements on the ACDC dataset are structural similarity index (SSIM) of 78.40%\u2009\u00b1\u20094.57%, peak signal\u2010to\u2010noise ratio (PSNR) of 30.46\u2009\u00b1\u20091.22\u2009dB, and normalized mean squared error (NMSE) of 0.0468\u2009\u00b1\u20090.0075. On the ACMRI dataset, the results are SSIM of 87.65%\u2009\u00b1\u20094.20%, PSNR of 30.04\u2009\u00b1\u20091.18\u2009dB, and NMSE of 0.0473\u2009\u00b1\u20090.0072. The proposed method exhibits high\u2010quality results with richer details and fewer artifacts for cardiac cine MRI reconstruction on different accelerations.<\/jats:p>","DOI":"10.1002\/ima.23131","type":"journal-article","created":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T04:50:16Z","timestamp":1719031816000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Reconstruction of Cardiac Cine <scp>MRI<\/scp> Using Motion\u2010Guided Deformable Alignment and Multi\u2010Resolution Fusion"],"prefix":"10.1002","volume":"34","author":[{"given":"Xiaoxiang","family":"Han","sequence":"first","affiliation":[{"name":"School of Health Science and Engineering University of Shanghai for Science and Technology  Shanghai China"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"Algorithm Team ToolSensing Technologies Co., Ltd  Chengdu China"}]},{"given":"Qiaohong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Medical Instruments Shanghai University of Medicine and Health Sciences  Shanghai China"}]},{"given":"Yiman","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pediatric Cardiology, Shanghai Children's Medical Center, School of Medicine Shanghai Jiao Tong University  Shanghai China"},{"name":"Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication &amp; Electronic Engineering East China Normal University  Shanghai China"}]},{"given":"Keyan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Health Science and Engineering University of Shanghai for Science and Technology  Shanghai China"}]},{"given":"Yuanjie","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Health Science and Engineering University of Shanghai for Science and Technology  Shanghai China"}]},{"given":"Weikun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Health Science and Engineering University of Shanghai for Science and Technology  Shanghai China"}]}],"member":"311","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"issue":"7","key":"e_1_2_10_2_1","first-page":"1381","article-title":"Gradient\u2010and Spin\u2010Echo MR Imaging of the Brain","volume":"20","author":"Patel M. 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