Adrian V Dalca, Andreea Bobu, Natalia S Rost, Polina Golland
{"title":"基于patch的临床脑图像离散配准。","authors":"Adrian V Dalca, Andreea Bobu, Natalia S Rost, Polina Golland","doi":"10.1007/978-3-319-47118-1_8","DOIUrl":null,"url":null,"abstract":"<p><p>We introduce a method for registration of brain images acquired in clinical settings. The algorithm relies on three-dimensional patches in a discrete registration framework to estimate correspondences. Clinical images present significant challenges for computational analysis. Fast acquisition often results in images with sparse slices, severe artifacts, and variable fields of view. Yet, large clinical datasets hold a wealth of clinically relevant information. Despite significant progress in image registration, most algorithms make strong assumptions about the continuity of image data, failing when presented with clinical images that violate these assumptions. In this paper, we demonstrate a non-rigid registration method for aligning such images. The method explicitly models the sparsely available image information to achieve robust registration. We demonstrate the algorithm on clinical images of stroke patients. The proposed method outperforms state of the art registration algorithms and avoids catastrophic failures often caused by these images. We provide a freely available open source implementation of the algorithm.</p>","PeriodicalId":91784,"journal":{"name":"Patch-based techniques in medical imaging : Second International Workshop, Patch-MI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016 : proceedings. Patch-MI (Workshop) (2nd : 2016 : Athens, Greece)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-47118-1_8","citationCount":"45","resultStr":"{\"title\":\"Patch-Based Discrete Registration of Clinical Brain Images.\",\"authors\":\"Adrian V Dalca, Andreea Bobu, Natalia S Rost, Polina Golland\",\"doi\":\"10.1007/978-3-319-47118-1_8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We introduce a method for registration of brain images acquired in clinical settings. The algorithm relies on three-dimensional patches in a discrete registration framework to estimate correspondences. Clinical images present significant challenges for computational analysis. Fast acquisition often results in images with sparse slices, severe artifacts, and variable fields of view. Yet, large clinical datasets hold a wealth of clinically relevant information. Despite significant progress in image registration, most algorithms make strong assumptions about the continuity of image data, failing when presented with clinical images that violate these assumptions. In this paper, we demonstrate a non-rigid registration method for aligning such images. The method explicitly models the sparsely available image information to achieve robust registration. We demonstrate the algorithm on clinical images of stroke patients. The proposed method outperforms state of the art registration algorithms and avoids catastrophic failures often caused by these images. We provide a freely available open source implementation of the algorithm.</p>\",\"PeriodicalId\":91784,\"journal\":{\"name\":\"Patch-based techniques in medical imaging : Second International Workshop, Patch-MI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016 : proceedings. Patch-MI (Workshop) (2nd : 2016 : Athens, Greece)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1007/978-3-319-47118-1_8\",\"citationCount\":\"45\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Patch-based techniques in medical imaging : Second International Workshop, Patch-MI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016 : proceedings. Patch-MI (Workshop) (2nd : 2016 : Athens, Greece)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/978-3-319-47118-1_8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2016/9/22 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Patch-based techniques in medical imaging : Second International Workshop, Patch-MI 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016 : proceedings. Patch-MI (Workshop) (2nd : 2016 : Athens, Greece)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-319-47118-1_8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2016/9/22 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Patch-Based Discrete Registration of Clinical Brain Images.
We introduce a method for registration of brain images acquired in clinical settings. The algorithm relies on three-dimensional patches in a discrete registration framework to estimate correspondences. Clinical images present significant challenges for computational analysis. Fast acquisition often results in images with sparse slices, severe artifacts, and variable fields of view. Yet, large clinical datasets hold a wealth of clinically relevant information. Despite significant progress in image registration, most algorithms make strong assumptions about the continuity of image data, failing when presented with clinical images that violate these assumptions. In this paper, we demonstrate a non-rigid registration method for aligning such images. The method explicitly models the sparsely available image information to achieve robust registration. We demonstrate the algorithm on clinical images of stroke patients. The proposed method outperforms state of the art registration algorithms and avoids catastrophic failures often caused by these images. We provide a freely available open source implementation of the algorithm.