[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"_public_publisher_byId_92de6964-45a4-46b3-8e6e-486d6f3c8648":3,"_public_publication_all{\"sortAscending\":false,\"sortField\":\"updateTime\",\"page\":0,\"size\":10,\"facet\":true,\"searchKey\":\"publisherId:92de6964-45a4-46b3-8e6e-486d6f3c8648,\"}":19},{"code":4,"data":5,"meta":9},"SUCCESS",{"id":6,"createTime":7,"updateTime":7,"relativeEntities":8,"slug":9,"properties":10,"entityType":13,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15,"subjectFields":16,"manageAffiliations":17,"indexDatabases":18,"url":9,"thumbnailPath":9,"statistic":9,"gsStatistic":9,"type":9,"analyzePriority":9},"92de6964-45a4-46b3-8e6e-486d6f3c8648","2023-12-23T00:05:40.446+00:00",[],null,{"title":11},{"EN":12},"Proceedings IEEE International Symposium on Biomedical Imaging","PUBLISHER","PENDING",0,[],[],[],{"meta":20,"data":22},{"total":21},"270",[23,102,163,194,270,379,490,646,692,793],{"id":24,"createTime":25,"updateTime":26,"relativeEntities":27,"slug":28,"properties":29,"entityType":40,"verifyStatus":41,"verifyTime":26,"verifyNote":42,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15,"primaryUrl":43,"fullTextUrl":44,"authors":45,"publicationType":89,"publisherRelationship":90,"citationCount":9,"citationInfo":9,"publishDate":9,"publishYear":9,"citationAnalyzeStatus":14,"lastCitationAnalyze":9,"indexDatabases":9,"openAccess":9,"references":9,"isForceReanalyzing":101},"e0f75928-1ebb-4db0-9597-96ff3f383ee6","2023-12-23T00:48:30.967+00:00","2025-01-04T23:59:12.309+00:00",[],"Fast-iterative-field-corrected-image-reconstruction-for-MRI",{"references":30,"keywords":32,"abstract":34,"title":36,"doi":38},{"VOID":31},"fessler, 2001, Proc IEEE Intl Conf on Image Processing, 1, 706\nkannengießer, 2001, Proc ISMRM 9th Scientific Meeting, 1800\nsutton, 2001, Proc ISMRM 9th Scientific Meeting, 763\nfessler, 2001, Submitted to IEEE Trans Sig Proc\n10.1109\u002F42.712126\n10.1002\u002Fmrm.1910380416\n10.1002\u002F(SICI)1522-2594(200001)43:1\u003C151::AID-MRM19>3.0.CO;2-K\n10.1109\u002F42.781014\n10.1109\u002F42.764889\n10.1002\u002Fmrm.1910370523\n10.1002\u002Fmrm.1910250210\n10.1109\u002F42.108599\n10.1137\u002F0914081",{"EN":33},"Image reconstruction,Magnetic resonance imaging,Iterative methods,Spirals,Reconstruction algorithms,Equations,Fast Fourier transforms,Phase estimation,Image segmentation,Iterative algorithms",{"EN":35},"Magnetic field inhomogeneities cause distortions in the reconstructed images for non-cartesian k-space MRI (using spirals, for example). Several noniterative methods are currently used to compensate for the off-resonance during the reconstruction, but these methods rely on the assumption of a smoothly varying field map. Recently, iterative methods have been proposed that do not rely on this assumption and have the potential to estimate undistorted field maps, but suffer from prohibitively long computation times. In this abstract we present a min-max derived, time-segmented approximation to the signal equation for MRI that, when combined with the nonuniform fast Fourier transform, provides a fast, accurate field-corrected image reconstruction.",{"EN":37},"Fast, iterative, field-corrected image reconstruction for MRI",{"VOID":39},"10.1109\u002FISBI.2002.1029301","PUBLICATION","VERIFIED","Auto Verify","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1029301\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1029301",[46,64,77],{"id":47,"sortIndex":48,"researcher":9,"roles":49,"affiliations":51,"properties":61},"bb844f1a-4baf-4b04-8f11-72a9a95ed10a",2,[50],"AUTHOR",[52],{"id":9,"sortIndex":15,"affiliation":53,"properties":9},{"id":54,"createTime":55,"updateTime":55,"relativeEntities":56,"slug":9,"properties":57,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},"c0562795-9801-4776-99e5-803b60603623","2023-12-23T00:48:30.987+00:00",[],{"title":58},{"VI":59},"Department of Biomedical Engineering, University of Michigan, USA","AFFILIATION",{"title":62},{"VI":63},"J.A. Fessler",{"id":65,"sortIndex":66,"researcher":9,"roles":67,"affiliations":68,"properties":74},"2814fedb-bdc2-4410-8e9d-bcf8b8d9f792",1,[50],[69],{"id":9,"sortIndex":15,"affiliation":70,"properties":9},{"id":54,"createTime":55,"updateTime":55,"relativeEntities":71,"slug":9,"properties":72,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},[],{"title":73},{"VI":59},{"title":75},{"VI":76},"D.C. Noll",{"id":78,"sortIndex":15,"researcher":9,"roles":79,"affiliations":80,"properties":86},"cb92835f-d511-4d80-a045-903010e9e6f0",[50],[81],{"id":9,"sortIndex":15,"affiliation":82,"properties":9},{"id":54,"createTime":55,"updateTime":55,"relativeEntities":83,"slug":9,"properties":84,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},[],{"title":85},{"VI":59},{"title":87},{"VI":88},"B.P. Sutton","ARTICLE",{"url":43,"publisher":91,"properties":98},{"id":6,"createTime":7,"updateTime":7,"relativeEntities":92,"slug":9,"properties":93,"entityType":13,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15,"subjectFields":95,"manageAffiliations":96,"indexDatabases":97,"url":9,"thumbnailPath":9,"statistic":9,"gsStatistic":9,"type":9,"analyzePriority":9},[],{"title":94},{"EN":12},[],[],[],{"pages":99},{"VOID":100},"489-492",false,{"id":103,"createTime":104,"updateTime":105,"relativeEntities":106,"slug":107,"properties":108,"entityType":40,"verifyStatus":41,"verifyTime":105,"verifyNote":42,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15,"primaryUrl":119,"fullTextUrl":120,"authors":121,"publicationType":89,"publisherRelationship":152,"citationCount":9,"citationInfo":9,"publishDate":9,"publishYear":9,"citationAnalyzeStatus":14,"lastCitationAnalyze":9,"indexDatabases":9,"openAccess":9,"references":9,"isForceReanalyzing":101},"ef3fa7fc-bac2-46c0-a9b5-58a6c6a5ce5d","2023-12-23T03:43:55.898+00:00","2025-02-07T23:52:58.635+00:00",[],"Generalized-series-dynamic-imaging-with-deformable-references-MRI-application-",{"references":109,"keywords":111,"abstract":113,"title":115,"doi":117},{"VOID":110},"10.1002\u002Fmrm.1910240209\nliang, 2000, Motion-compensated Keyhole\u002FRIGR imaging, Proc ISMRM 8th Scientific Meeting and Exhibition, 1697\n10.1002\u002F(SICI)1098-1098(1999)10:3\u003C258::AID-IMA6>3.0.CO;2-7\nmedic, 1998, Im-proved keyhole approach with motion-correction technique in contrast-enhanced dynamic MRI, Proc ISMRM 6th Scientific Meeting Exhibition, 2064\nliang, 2001, Generalized series imaging with multiple references, Workshop on Minimum MR Data Acquisiion Methods Making More with Less\n10.1109\u002F34.24792\ncollignon, 1997, Multimodality image registration by maximization of mutual information, IEEE Trans Med Imaging, 16, 187, 10.1109\u002F42.563664\n10.1109\u002FTASSP.1978.1163055\n10.1002\u002Fmrm.1910380414\n10.1002\u002F(SICI)1522-2594(199911)42:5\u003C952::AID-MRM16>3.0.CO;2-S\n10.1002\u002Fjmri.1880030419\n10.1002\u002Fmrm.1910290618\n10.1002\u002Fmrm.1910300306\n10.1109\u002F42.363100\n10.1109\u002FTMI.1986.4307732\n10.1088\u002F0022-3719\u002F10\u002F3\u002F004\n10.1002\u002Fmrm.1910320219",{"EN":112},"High-resolution imaging,Spatial resolution,Encoding,Image reconstruction,Image coding,Spline,Application software,Image resolution,Deformable models,Solid modeling",{"EN":114},"Many imaging applications require the acquisition of a time series of images. Conventional full-scan methods acquire each of these images independently, resulting in a tradeoff between spatial resolution and temporal resolution. To address this problem, a generalized series-based reduced-encoding method has been proposed, which collects one or a few high-resolution references and a sequence of reduced data sets. This paper extends this imaging scheme to allow deformable references to be used to compensate for possible geometric variations between the references and the reduced data sets. The deformable references are constructed using a thin-plate spline model and a mutual information-based registration algorithm. Simulation results demonstrate that the proposed method can handle large geometric changes such as those encountered in cardiac imaging.",{"EN":116},"Generalized series dynamic imaging with deformable references [MRI application]",{"VOID":118},"10.1109\u002FISBI.2002.1029376","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1029376\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1029376",[122,137],{"id":123,"sortIndex":66,"researcher":9,"roles":124,"affiliations":125,"properties":134},"f7b93d7a-db23-4c84-be0a-4b57ba03adfc",[50],[126],{"id":9,"sortIndex":15,"affiliation":127,"properties":9},{"id":128,"createTime":129,"updateTime":129,"relativeEntities":130,"slug":9,"properties":131,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},"72b34c3c-017d-4e0f-94e3-095aa9b981f4","2024-02-07T13:28:51.265+00:00",[],{"title":132},{"VI":133},"Department of Electrical and Computer Engineering, University of Illinois at, Urbana-Champaign",{"title":135},{"VI":136},"Zhi-Pei Liang",{"id":138,"sortIndex":15,"researcher":9,"roles":139,"affiliations":140,"properties":149},"6fbbedfe-236e-4ef5-903b-a85a00a51e25",[50],[141],{"id":9,"sortIndex":15,"affiliation":142,"properties":9},{"id":143,"createTime":144,"updateTime":144,"relativeEntities":145,"slug":9,"properties":146,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},"afa897c2-c9d7-4809-b933-ecb3c11cc68f","2024-01-05T13:46:36.220+00:00",[],{"title":147},{"VI":148},"Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana-Champaign, USA",{"title":150},{"VI":151},"Xiuquan Ji",{"url":119,"publisher":153,"properties":160},{"id":6,"createTime":7,"updateTime":7,"relativeEntities":154,"slug":9,"properties":155,"entityType":13,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15,"subjectFields":157,"manageAffiliations":158,"indexDatabases":159,"url":9,"thumbnailPath":9,"statistic":9,"gsStatistic":9,"type":9,"analyzePriority":9},[],{"title":156},{"EN":12},[],[],[],{"pages":161},{"VOID":162},"789-792",{"id":164,"createTime":165,"updateTime":166,"relativeEntities":167,"slug":168,"properties":169,"entityType":40,"verifyStatus":14,"verifyTime":176,"verifyNote":177,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15,"primaryUrl":178,"fullTextUrl":179,"authors":180,"publicationType":89,"publisherRelationship":181,"citationCount":9,"citationInfo":9,"publishDate":192,"publishYear":193,"citationAnalyzeStatus":14,"lastCitationAnalyze":9,"indexDatabases":9,"openAccess":9,"references":9,"isForceReanalyzing":101},"952d9062-3b6e-4bba-9f23-a148d6c277e9","2023-12-22T21:44:03.321+00:00","2025-01-20T23:49:46.125+00:00",[],"2002-IEEE-International-Symposium-On-Biomedical-Imaging-front-matter-",{"abstract":170,"title":172,"doi":174},{"EN":171},"Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original conference proceedings.",{"EN":173},"2002 IEEE International Symposium On Biomedical Imaging [front matter]",{"VOID":175},"10.1109\u002FISBI.2002.1029178","2025-01-20T23:49:46.122+00:00","Author title is blank","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1029178\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1029178",[],{"url":178,"publisher":182,"properties":189},{"id":6,"createTime":7,"updateTime":7,"relativeEntities":183,"slug":9,"properties":184,"entityType":13,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15,"subjectFields":186,"manageAffiliations":187,"indexDatabases":188,"url":9,"thumbnailPath":9,"statistic":9,"gsStatistic":9,"type":9,"analyzePriority":9},[],{"title":185},{"EN":12},[],[],[],{"pages":190},{"VOID":191},"i-xxxi","2002-01-01",2002,{"id":195,"createTime":196,"updateTime":197,"relativeEntities":198,"slug":199,"properties":200,"entityType":40,"verifyStatus":41,"verifyTime":214,"verifyNote":42,"syncStatus":14,"languages":9,"translateLanguages":215,"viewCount":15,"primaryUrl":217,"fullTextUrl":218,"authors":219,"publicationType":89,"publisherRelationship":259,"citationCount":9,"citationInfo":9,"publishDate":9,"publishYear":9,"citationAnalyzeStatus":14,"lastCitationAnalyze":9,"indexDatabases":9,"openAccess":9,"references":9,"isForceReanalyzing":101},"a59e9561-3f06-4f65-a870-83977af2d341","2023-12-23T03:56:04.977+00:00","2025-02-11T23:42:18.458+00:00",[],"A-fully-automatic-calibration-procedure-for-freehand-3D-ultrasound",{"references":201,"keywords":203,"abstract":206,"title":209,"doi":212},{"VOID":202},"10.1088\u002F0031-9155\u002F46\u002F5\u002F201\n10.1016\u002FS0301-5629(98)00044-1\n10.1016\u002FS0301-5629(99)00130-1\nhenry, 1997, Outils pour La modélisation des structures et la simulation d'examens échographiques\n10.1016\u002FS0031-3203(98)00091-0\nblackall, 2000, An image registration approach to automated calibration for freehand 3d ultrasound, Proc of Medical Image Computing and Computer-Assisted Intervention\ncarr, 1996, Surface reconstruction in 3D medical imaging\n10.1016\u002F0301-5629(94)90052-3",{"EN":204,"VI":205},"Calibration,Ultrasonic imaging,Probes,Image reconstruction,Imaging phantoms,Mechanical systems,Phased arrays,Surface reconstruction,Iterative algorithms,Biomedical imaging","Hiệu chuẩn,Hình ảnh siêu âm,Đầu dò,Tái cấu trúc hình ảnh,Mô phỏng hình ảnh,Hệ thống cơ học,Ma trận pha,Tái cấu trúc bề mặt Thuật toán lặp,Hình ảnh y sinh",{"EN":207,"VI":208},"Describes a novel method for calibration of freehand three-dimensional (3D) ultrasound. A position sensor is mounted on a conventional ultrasound probe, thus the set of B-scans can be localized in 3D, and can be compounded into a volume. The calibration process aims at determining the transformation (translations, rotations, scaling) between the coordinates system of images and the coordinate system of the localization system. In our study, the phantom used to calibrate the 3D ultrasound system is a plane. It provides in each image a strong, straight line. The calibration process is based on the set of lines in 2D images forming a plane in 3D. Points of interest are extracted from the ultrasound sequence. The eight parameters of the transformation are determined with an iterative algorithm which is based on the principle that correct registration between the plane and the points of interest provides correct calibration. Validation of this method has been performed on synthetic sequences. This calibration method is shown to be easy to perform, completely automatic and fast enough for clinical use.","Bài báo này mô tả một phương pháp mới cho việc hiệu chuẩn siêu âm ba chiều (3D) bằng tay. Một cảm biến vị trí được gắn trên một đầu dò siêu âm thông thường, do đó tập hợp các B-scan có thể được định vị trong không gian 3D và có thể được kết hợp thành một thể tích. Quy trình hiệu chuẩn nhằm xác định phép biến đổi (dịch chuyển, xoay, và tỷ lệ) giữa hệ tọa độ của hình ảnh và hệ tọa độ của hệ thống định vị. Trong nghiên cứu của chúng tôi, mô hình được sử dụng để hiệu chuẩn hệ thống siêu âm 3D là một mặt phẳng. Nó cung cấp trong mỗi hình ảnh một đường thẳng rõ nét, mạnh mẽ. Quy trình hiệu chuẩn dựa trên tập hợp các đường trong hình ảnh 2D tạo thành một mặt phẳng trong không gian 3D. Các điểm quan tâm được trích xuất từ chuỗi siêu âm. Tám tham số của phép biến đổi được xác định bằng một thuật toán lặp, dựa trên nguyên tắc rằng việc đăng ký chính xác giữa mặt phẳng và các điểm quan tâm sẽ cung cấp hiệu chuẩn chính xác. Phương pháp này đã được xác nhận qua các chuỗi hình ảnh tổng hợp. Phương pháp hiệu chuẩn này được chứng minh là dễ thực hiện, hoàn toàn tự động và đủ nhanh cho việc sử dụng lâm sàng.",{"EN":210,"VI":211},"A fully automatic calibration procedure for freehand 3D ultrasound","Một quy trình hiệu chuẩn hoàn toàn tự động cho siêu âm 3D bằng tay",{"VOID":213},"10.1109\u002FISBI.2002.1029428","2025-02-05T18:34:21.758+00:00",[216],"VI","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1029428\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1029428",[220,235,247],{"id":221,"sortIndex":66,"researcher":9,"roles":222,"affiliations":223,"properties":232},"a064551a-7f5b-4a71-a305-601f694accf4",[50],[224],{"id":9,"sortIndex":15,"affiliation":225,"properties":9},{"id":226,"createTime":227,"updateTime":227,"relativeEntities":228,"slug":9,"properties":229,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},"80201d70-9b93-4424-841b-9fd2f3c9fead","2023-12-23T03:56:04.999+00:00",[],{"title":230},{"VI":231},"IRISA, Université Rennes 1-INRIA-CNRS, Rennes, France",{"title":233},{"VI":234},"P. Hellier",{"id":236,"sortIndex":48,"researcher":9,"roles":237,"affiliations":238,"properties":244},"637cb5c5-7946-4f14-b7bc-43a4cc088f3e",[50],[239],{"id":9,"sortIndex":15,"affiliation":240,"properties":9},{"id":226,"createTime":227,"updateTime":227,"relativeEntities":241,"slug":9,"properties":242,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},[],{"title":243},{"VI":231},{"title":245},{"VI":246},"C. Barillot",{"id":248,"sortIndex":15,"researcher":9,"roles":249,"affiliations":250,"properties":256},"e6399584-8d1e-454e-9e71-60c05e37500c",[50],[251],{"id":9,"sortIndex":15,"affiliation":252,"properties":9},{"id":226,"createTime":227,"updateTime":227,"relativeEntities":253,"slug":9,"properties":254,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},[],{"title":255},{"VI":231},{"title":257},{"VI":258},"F. Rousseau",{"url":217,"publisher":260,"properties":267},{"id":6,"createTime":7,"updateTime":7,"relativeEntities":261,"slug":9,"properties":262,"entityType":13,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15,"subjectFields":264,"manageAffiliations":265,"indexDatabases":266,"url":9,"thumbnailPath":9,"statistic":9,"gsStatistic":9,"type":9,"analyzePriority":9},[],{"title":263},{"EN":12},[],[],[],{"pages":268},{"VOID":269},"985-988",{"id":271,"createTime":272,"updateTime":273,"relativeEntities":274,"slug":275,"properties":276,"entityType":40,"verifyStatus":41,"verifyTime":273,"verifyNote":42,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15,"primaryUrl":287,"fullTextUrl":288,"authors":289,"publicationType":89,"publisherRelationship":368,"citationCount":9,"citationInfo":9,"publishDate":9,"publishYear":9,"citationAnalyzeStatus":14,"lastCitationAnalyze":9,"indexDatabases":9,"openAccess":9,"references":9,"isForceReanalyzing":101},"f21ab1be-95a0-40e3-9fc4-99e23366bc04","2023-12-23T00:08:33.166+00:00","2025-01-21T23:27:24.471+00:00",[],"Classifying-convex-sets-for-vessel-detection-in-retinal-images",{"references":277,"keywords":279,"abstract":281,"title":283,"doi":285},{"VOID":278},"kochner, 1998, Course tracking and contour extraction of retinal vessels from color fundus photographs: most efficient use of steerable filters for model-based image analysis, SPIE Proc Medical Imaging, 755, 10.1117\u002F12.310955\n10.1109\u002F42.700738\nbishop, 1996, Neural Networks for Pattern Recognition\nflorack, 1997, Image Structure, 10.1007\u002F978-94-015-8845-4\n10.1109\u002F34.922704\nduda, 1973, Pattern Classification and Scene Analysis\n10.1145\u002F293347.293348\nmartínez-pérez, 1999, Retinal blood vessel segmentation by means of scale-space analysis and region growing, Proc of the 2nd Int Conf on Med lmage Comp and Comp Ass Inierv, 1679, 90\n10.1017\u002FCBO9780511801389\n10.1109\u002F42.845178",{"EN":280},"Retina,Eigenvalues and eigenfunctions,Image edge detection,Pixel,Reflection,Diabetes,Retinopathy,Performance evaluation,Kernel",{"EN":282},"We present a method to detect vessels in images of the retina. Instead of relying on pixel classification, as many detection algorithms do, we propose a more natural representation for elongated structures, such as vessels. This new representation consists of primitives called affine convex sets. On these convex sets we apply the classification step. The reason for choosing this approach is two-fold: (1) By using a dedicated representation of image structures, one can exploit prior knowledge. (2) A method based on pixel classification is often computationally unattractive. The method can also be applied to other image structures, if an appropriate representation for the structures is chosen. The method was tested on fundus reflection images. We obtained an accuracy of 0.897, a sensitivity of 0.700 and a specificity of 0.923.",{"EN":284},"Classifying convex sets for vessel detection in retinal images",{"VOID":286},"10.1109\u002FISBI.2002.1029245","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1029245\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1029245",[290,306,318,330,343,356],{"id":291,"sortIndex":292,"researcher":9,"roles":293,"affiliations":294,"properties":303},"83e9962f-9b63-47f3-b40e-22ec647900b3",5,[50],[295],{"id":9,"sortIndex":15,"affiliation":296,"properties":9},{"id":297,"createTime":298,"updateTime":298,"relativeEntities":299,"slug":9,"properties":300,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},"fc3f168b-2e28-41fd-8f76-54f0c0f83961","2023-12-23T00:08:33.251+00:00",[],{"title":301},{"VI":302},"Image Sciences Institute Heidelberglaan, Utrecht, CX, The Netherlands",{"title":304},{"VI":305},"M.A. Viergever",{"id":307,"sortIndex":66,"researcher":9,"roles":308,"affiliations":309,"properties":315},"754bb835-edfa-4b6b-81a0-064d5e5b14c6",[50],[310],{"id":9,"sortIndex":15,"affiliation":311,"properties":9},{"id":297,"createTime":298,"updateTime":298,"relativeEntities":312,"slug":9,"properties":313,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},[],{"title":314},{"VI":302},{"title":316},{"VI":317},"S.N. 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These systems often exhibit performance characteristics, e.g. spatial resolution, substantially better than contemporary human PET scanners and are often the first systems to demonstrate new technologies, e.g. avalanche photodiode-based detector modules. Despite these advances, spatial resolution, sensitivity, resolution uniformity and other performance parameters must continue to be improved if accurate general purpose imaging is to be carried out in the most popular research subject, the mouse. Moreover, as these improvements occur, methods must also be devised to minimize the resolution-degrading effects of positron range, the distance a positron travels from the decaying nucleus before encountering and mutually annihilating an electron. Range effects are particularly important for compounds labeled with \"non-traditional\" positron-emitters such as I-124 or Tc-94m. In order to illustrate the complex interplay of issues that must be addressed when contemplating such improvements, we describe how we have approached high performance PET imaging in the design and construction of ATLAS (Advanced Technology Laboratory Animal Scanner), a small animal PET scanner now entering service at the National Institutes of Health (NIH) in Bethesda, Md.",{"EN":393},"Towards high performance small animal positron emission tomography",{"VOID":395},"10.1109\u002FISBI.2002.1029270","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1029270\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1029270",[399,416,431,443,455,467],{"id":400,"sortIndex":345,"researcher":9,"roles":401,"affiliations":402,"properties":413},"a523e79f-0cb5-4b80-9de7-e2181d74a31e",[50],[403],{"id":9,"sortIndex":15,"affiliation":404,"properties":9},{"id":405,"createTime":406,"updateTime":407,"relativeEntities":408,"slug":409,"properties":410,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},"12b1a08d-3f0f-4513-8296-c05eb498f86e","2023-12-23T11:42:52.406+00:00","2024-11-30T23:45:02.663+00:00",[],"Hospital-Universitario-Gregorio-Mara%C3%B1on-Madrid-Spain",{"title":411},{"VI":412},"Hospital Universitario Gregorio Marañon, Madrid, Spain",{"title":414},{"VI":415},"J. 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The basic idea is to select a reference frame (e.g., the first image of a cycle) and to map each image in the sequence to it using elastic deformation. What makes our method specific is the use of a semi-local parametric model of the deformation (spatio-temporal spline), and the reformulation of the registration task as a global spatio-temporal optimization problem. The scale of the spline model controls the smoothness of the displacement field. Our algorithm uses a multiresolution optimization strategy for higher speed and robustness. We validated the accuracy of our algorithm by applying it to a synthetic sequence; this one heart-cycle test sequence was generated by deforming a reference frame according to a realistic motion model and by adding random noise to it. Finally, we present results on real data from normal and pathological subjects to illustrate the clinical applicability of our method.",{"EN":504},"Cardiac ultrasound motion detection by elastic registration exploiting temporal coherence",{"VOID":506},"10.1109\u002FISBI.2002.1029325","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1029325\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1029325",[510,527,542,557,580,592,621],{"id":511,"sortIndex":48,"researcher":9,"roles":512,"affiliations":513,"properties":524},"aaf87160-bf8e-4734-a57f-df49f4d64ca7",[50],[514],{"id":9,"sortIndex":15,"affiliation":515,"properties":9},{"id":516,"createTime":517,"updateTime":518,"relativeEntities":519,"slug":520,"properties":521,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},"132aca9b-c88d-49ee-8f9e-f820212d9fb5","2023-12-23T03:01:08.919+00:00","2024-09-21T05:45:25.781+00:00",[],"Biomedical-Imaging-Group-Swiss-Federal-Institute-of-Technology-Lausanne-Switzerland",{"title":522},{"VI":523},"Biomedical Imaging Group, Swiss Federal Institute of Technology, Lausanne, Switzerland",{"title":525},{"VI":526},"M. 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Gregorio Marañón, Madrid, Spain",{"title":634},{"VI":442},{"url":507,"publisher":636,"properties":643},{"id":6,"createTime":7,"updateTime":7,"relativeEntities":637,"slug":9,"properties":638,"entityType":13,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15,"subjectFields":640,"manageAffiliations":641,"indexDatabases":642,"url":9,"thumbnailPath":9,"statistic":9,"gsStatistic":9,"type":9,"analyzePriority":9},[],{"title":639},{"EN":12},[],[],[],{"pages":644},{"VOID":645},"585-588",{"id":647,"createTime":648,"updateTime":649,"relativeEntities":650,"slug":651,"properties":652,"entityType":40,"verifyStatus":41,"verifyTime":649,"verifyNote":42,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15,"primaryUrl":663,"fullTextUrl":664,"authors":665,"publicationType":89,"publisherRelationship":681,"citationCount":9,"citationInfo":9,"publishDate":9,"publishYear":9,"citationAnalyzeStatus":14,"lastCitationAnalyze":9,"indexDatabases":9,"openAccess":9,"references":9,"isForceReanalyzing":101},"ba233180-0eb8-4cb7-b237-f208177568af","2023-12-23T00:39:24.441+00:00","2025-02-21T23:20:31.849+00:00",[],"In-vivo-micro-CT-for-small-animals-imaging",{"references":653,"keywords":655,"abstract":657,"title":659,"doi":661},{"VOID":654},"10.1038\u002F384335a0\n10.1046\u002Fj.1365-2818.1998.00367.x\n10.1364\u002FJOSAA.1.000612\nford, 0, Fundamental Limits to Precision in Small-Animal Computed Tomography, HiRes-2001, 242\n10.1002\u002F(SICI)1097-0029(19990515\u002F01)45:4\u002F5\u003C303::AID-JEMT14>3.0.CO;2-8\n10.1109\u002F42.241876\n10.1038\u002Fsj.neo.7900069\nhildebrand, 0, Quantification of Bone Microarchitecture with the SMI, CMBBE, 1, 15\n10.1117\u002F12.452844",{"EN":656},"Animals,Image reconstruction,X-ray imaging,Geometry,High-resolution imaging,Instruments,Throughput,X-ray scattering,Cameras,X-ray detection",{"EN":658},"Recent developments in in-vivo small animal imaging are based on the demand from the biological and pharmaceutical researchers for the high-resolution and high throughput micro-CT instruments. Two in-vivo micro-CT scanners has been developed: a research grade high-resolution scanner with isotropic 8 \u002Fspl mu\u002Fm voxel size in any location of 80 mm \u002Fspl times\u002F 200 mm animal's body and an ultra-fast full-body scanner with acquisition + reconstruction cycle for 512\u002Fsup 3\u002F pixels of 100-180 seconds. The research grade system uses small-angle scattering for resolution improvement without extra dose and incorporates unique reconstruction software for cross-section size up to 8K\u002Fspl times\u002F8K pixels. The fast micro-CT scanner is designed on the basis of dual-beam X-ray geometry and multicomputer reconstruction through local network. Both systems include animal's physiological monitoring and possibility for scanning synchronization with breathing rate. Software provides analysis from the reconstructed dataset and shows results as a realistic 3D-image with possibilities of virtual manipulations.",{"EN":660},"In-vivo micro-CT for small animals imaging",{"VOID":662},"10.1109\u002FISBI.2002.1029272","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1029272\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1029272",[666],{"id":667,"sortIndex":15,"researcher":9,"roles":668,"affiliations":669,"properties":678},"cec06b13-8785-4b70-84f4-c4af913f7f14",[50],[670],{"id":9,"sortIndex":15,"affiliation":671,"properties":9},{"id":672,"createTime":673,"updateTime":673,"relativeEntities":674,"slug":9,"properties":675,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},"23bcf0ee-8455-428a-adc9-4592dc0dd81a","2023-12-23T00:39:24.458+00:00",[],{"title":676},{"VI":677},"Skyscan, Belgium",{"title":679},{"VI":680},"A. 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An automated method was developed to detect and quantify the volume of PN on STIR MR images. The automated algorithm implements heuristics derived from human-based recognition of lesions (pixel intensity contrast and edge detection\u002Ffollowing). A connected component analysis distinguishes multiple non-contiguous lesions and removes lesions considered too small, and an edge following algorithm defines the border of the lesion. This method was validated by two observers, who performed automated volume calculations and manual tracings to estimate tumor volume. The method was reproducible (C.V., 0.6% to 5.6%), and the inter-observer difference in the average tumor volume ranged from 6% \u002Fspl plusmn\u002F 3.8 to -5.2% \u002Fspl plusmn\u002F 5.4. The automated and manual methods of volume determination yielded similar results (R = 0.999). The automated method will likely improve the reproducibility and sensitivity of response assessment in clinical trials for patients with PN.",{"EN":704},"Automatic lesion detection and volume measurement in MR imaging of plexiform neurofibromas",{"VOID":706},"10.1109\u002FISBI.2002.1029235","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1029235\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1029235",[710,725,740,755,770],{"id":711,"sortIndex":345,"researcher":9,"roles":712,"affiliations":713,"properties":722},"c7701883-48d9-4c71-b956-9eb7b9ed81bc",[50],[714],{"id":9,"sortIndex":15,"affiliation":715,"properties":9},{"id":716,"createTime":717,"updateTime":717,"relativeEntities":718,"slug":9,"properties":719,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},"d3882af1-0d6a-4494-939f-6ec4f0071819","2023-12-22T23:17:27.449+00:00",[],{"title":720},{"VI":721},"Diagnostic Radiology Department, CC\u002FNIH, USA",{"title":723},{"VI":724},"N. 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Region Growing, and BayeS\u002F{MDL} for Multiband Image Segmentation, PAMI, 18, 884, 10.1109\u002F34.537343\n10.1109\u002F34.954599\n10.1109\u002F34.954609\n10.1109\u002FICPR.1990.118200\n10.1117\u002F12.57073\n10.1117\u002F12.143652\n10.1109\u002FCVPR.2000.854953\n10.1109\u002F34.368194\n10.1007\u002FBF00133570\n10.1109\u002FICCV.1995.466850\n10.1016\u002F1049-9660(92)90003-L\n10.1109\u002FCVPR.1999.784720\n10.1145\u002F218380.218442\n10.1109\u002F34.841758\nbeucher, 1979, Use of Watersheds in Contour Detection, Proc Int Workshop on Image Processing Real-Time Edge and Motion Detection\u002FEstimation\n10.1109\u002F34.87344\n10.1109\u002F34.546254\n10.1007\u002FBF01386390\npress, 1992, Numerical Recipes in C, 681",{"EN":801},"Image segmentation,Paints,Active contours,Competitive intelligence,Mice,Robustness,Feedback,Lifting equipment,Labeling,Data mining",{"EN":803},"Intelligent Scissors and Intelligent Paint are complementary interactive image segmentation tools that allow a user to quickly and accurately select objects of interest using simple gesture motions with a mouse. With Intelligent Scissors, when the cursor position comes in proximity to an object edge, a live-wire boundary \"snaps\" to, and wraps around the object of interest. The Intelligent Paint tool uses the cursor position to sample the image data interior to the object and grows the current region, in discrete, snapping increments, to include similar neighboring regions. Both techniques make use of a watershed algorithm called toboganning. With Intelligent Scissors, image segmentation is formulated as a piece-wise globally optimal graph searching problem, while Intelligent Paint approaches it as an adaptive, cost-ordered connected component labelling scheme. Using these tools, objects or regions can be selected in a few seconds, with better accuracy and reproducibility than can be obtained using manual selection tools. In particular interobserver reproducibility using intelligent segmentation tools is far better than intraobserver reproducibility using manual segmentation methods.",{"EN":805},"Intelligent segmentation tools",{"VOID":807},"10.1109\u002FISBI.2002.1029232","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1029232\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1029232",[811,832,844],{"id":812,"sortIndex":15,"researcher":9,"roles":813,"affiliations":814,"properties":829},"ea75b21c-5854-473b-8e13-cf5bc6d776ce",[50],[815],{"id":816,"sortIndex":15,"affiliation":817,"properties":826},"f25aafc3-f57b-4a48-a1df-31cf809efb47",{"id":818,"createTime":819,"updateTime":820,"relativeEntities":821,"slug":822,"properties":823,"entityType":60,"verifyStatus":14,"verifyTime":9,"verifyNote":9,"syncStatus":14,"languages":9,"translateLanguages":9,"viewCount":15},"e8becba4-382d-4967-83d1-02d3871808bf","2024-01-29T04:58:13.817+00:00","2024-09-13T06:19:58.151+00:00",[],"Brigham-Young-University-USA",{"title":824},{"VI":825},"Brigham Young University USA",{"title":827},{"VI":828},"Brigham Young University, USA",{"title":830},{"VI":831},"W.A. 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