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Alignment of multi\u2010contrast MR images is performed in an unsupervised manner by a CNN work, yielding transformation parameters to align input images in order to minimize the normalized cross\u2010correlation loss among multi\u2010contrast images. Then, fine\u2010tuning for image alignment is performed by maximizing the normalized mutual information. The motion correction network corrects motion artifacts in the aligned multi\u2010contrast images. The correction network is trained to minimize the structural similarity loss and the VGG loss in a supervised manner. All datasets of motion\u2010corrupted images are generated using motion simulation based on MR physics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A motion\u2010correction network for multi\u2010contrast brain MRI successfully corrected artifacts of simulated motion for 4 test subjects, showing 0.96%, 7.63%, and 5.03% increases in the average structural simularity and 5.19%, 10.2%, and 7.48% increases in the average normalized mutual information for T\n                      <jats:sub>1<\/jats:sub>\n                      \u2010weighted, T\n                      <jats:sub>2<\/jats:sub>\n                      \u2010weighted, and T\n                      <jats:sub>2<\/jats:sub>\n                      \u2010weighted fluid\u2010attenuated inversion recovery images, respectively. The experimental setting with image alignment and artifact\u2010free input images for other contrasts shows better performances in correction of simulated motion artifacts. Furthermore, the proposed method quantitatively outperforms recent deep learning motion correction and synthesis methods. Real motion experiments from 5 healthy subjects demonstrate the potential of the proposed method for use in a clinical environment.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A deep learning\u2010based motion correction method for multi\u2010contrast MRI was successfully developed, and experimental results demonstrate the validity of the proposed method.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.28719","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T10:02:02Z","timestamp":1615802522000},"page":"1077-1092","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["MC\n                    <sup>2<\/sup>\n                    \u2010Net: motion correction network for multi\u2010contrast brain MRI"],"prefix":"10.1002","volume":"86","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7961-1899","authenticated-orcid":false,"given":"Jongyeon","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9320-1391","authenticated-orcid":false,"given":"Byungjai","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4757-1922","authenticated-orcid":false,"given":"HyunWook","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,3,15]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2015.03.007"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.24850"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/0730-725X(88)90135-X"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/61\/5\/R32"},{"key":"e_1_2_7_6_1","volume-title":"Handbook of MRI Pulse Sequences","author":"Bernstein MA","year":"2004"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.173.1.2781017"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910310505"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1081\/JCMR-100107467"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.039"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.23230"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<963::AID-MRM17>3.0.CO;2-L"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2013.2268898"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.650886"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2016.2557069"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-017-9983-4"},{"key":"e_1_2_7_17_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010net: convolutional networks for biomedical image segmentation. 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