Brain atlas fusion from high-thickness diagnostic magnetic resonance images by learning-based super-resolution

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• 作者：Jinpeng Zhang^a ; ¹Author Vitae ; Lichi Zhang^a ; ^c ; ¹Author Vitae ; Lei Xiang^aAuthor Vitae ; Yeqin Shao^bAuthor Vitae ; Guorong Wu^cAuthor Vitae ; Xiaodong Zhou^dAuthor Vitae ; Dinggang Shen^c ; ^e ; ^{dgshen@med.unc.edu}Author Vitae ; Qian Wang^a ; ^{wang.qian@sjtu.edu.cn}Author Vitae
• 关键词：Brain atlas ; Super-resolution ; Image enhancement ; Sparsity learning ; Random forest regression ; Groupwise registration
• 刊名：Pattern Recognition
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：63
• 期：Complete
• 页码：531-541
• 全文大小：1304 K
• 卷排序：63

文摘

We fuse the brain atlas from real diagnostic MR images with high inter-slice thickness. All images are processed through the two-stage learning-based super-resolution. Groupwise registration is applied for unbiased atlas fusion.