{"id":2986,"date":"2018-09-18T13:07:19","date_gmt":"2018-09-18T17:07:19","guid":{"rendered":"https:\/\/www.med.unc.edu\/bigs2\/?page_id=2986"},"modified":"2020-10-13T18:18:29","modified_gmt":"2020-10-13T22:18:29","slug":"ghmm","status":"publish","type":"page","link":"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/","title":{"rendered":"GHMM: Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data"},"content":{"rendered":"<div id=\"content1\">\n<div id=\"main\">\n<div id=\"right\">\n<div id=\"right_text\">\n<div class=\"box\">\n<p>Magnetic resonance imaging (MRI) has become an important imaging technique for quantifying the spatial location and magnitude\/direction of longitudinal cartilage morphology changes in patients with osteoarthritis (OA). Although several analytical methods, such as subregion-based analysis, have been developed to refine and improve quantitative cartilage analyses, they can be suboptimal due to two major issues: the lack of spatial correspondence across subjects and time and the spatial heterogeneity of cartilage progression across subjects. The aim of this paper is to present a statistical method for longitudinal cartilage quantification in OA patients, while addressing these two issues. The 3D knee image data is preprocessed to establish spatial correspondence across subjects and\/or time. Then, a Gaussian hidden Markov model (GHMM) is proposed to deal with the spatial heterogeneity of cartilage progression across both time and OA subjects. To estimate unknown parameters in GHMM, we employ a pseudo-likelihood function and optimize it by using an expectation-maximization (EM) algorithm. The proposed model can effectively detect diseased regions in each OA subject and present a localized analysis of longitudinal cartilage thickness within each latent subpopulation. Our GHMM integrates the strengths of two standard statistical methods including the local subregion-based analysis and the ordered value approach. We use simulation studies and the Pfizer longitudinal knee MRI dataset to evaluate the finite sample performance of GHMM in the quantification of longitudinal cartilage morphology changes. Our results indicate that GHMM significantly outperforms several standard analytical methods.<\/p>\n<p><a href=\"https:\/\/www.nitrc.org\/projects\/ghmm\/\"><strong>Software Download<\/strong> <\/a><\/p>\n<h4><\/h4>\n<p>Citation:<\/p>\n<p>1. Huang, C., Shan, L., Charles, H., Niethammer, M., and Zhu, H. T..\u00a0Diseased Region Detection of Longitudinal Knee MRI Data.\u00a0<em>Information Processing in Medical Imaging<\/em>, 7917, 632-643, 2013.<\/p>\n<p>2. Huang, C., Shan, L., Charles, H., Wirth, W., Niethammer, M., and Zhu, H. T..\u00a0Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data.<em>\u00a0IEEE Transactions on Medical Imaging<\/em>, 34, 1914-1927, 2015.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div id=\"footer\">\n<p>&nbsp;<\/p>\n<\/div>\n<\/div>\n<p><!-- footer ends--><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Magnetic resonance imaging (MRI) has become an important imaging technique for quantifying the spatial location and magnitude\/direction of longitudinal cartilage morphology changes in patients with osteoarthritis (OA). Although several analytical methods, such as subregion-based analysis, have been developed to refine and improve quantitative cartilage analyses, they can be suboptimal due to two major issues: the &hellip; <a href=\"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/\" aria-label=\"Read more about GHMM: Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data\">Read more<\/a><\/p>\n","protected":false},"author":1503,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-2986","page","type-page","status-publish","hentry","odd"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>GHMM: Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data - BIG-S2<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GHMM: Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data - BIG-S2\" \/>\n<meta property=\"og:description\" content=\"Magnetic resonance imaging (MRI) has become an important imaging technique for quantifying the spatial location and magnitude\/direction of longitudinal cartilage morphology changes in patients with osteoarthritis (OA). Although several analytical methods, such as subregion-based analysis, have been developed to refine and improve quantitative cartilage analyses, they can be suboptimal due to two major issues: the &hellip; Read more\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/\" \/>\n<meta property=\"og:site_name\" content=\"BIG-S2\" \/>\n<meta property=\"article:modified_time\" content=\"2020-10-13T22:18:29+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/\",\"url\":\"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/\",\"name\":\"GHMM: Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data - BIG-S2\",\"isPartOf\":{\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/#website\"},\"datePublished\":\"2018-09-18T17:07:19+00:00\",\"dateModified\":\"2020-10-13T22:18:29+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.med.unc.edu\/bigs2\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"GHMM: Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.med.unc.edu\/bigs2\/#website\",\"url\":\"https:\/\/www.med.unc.edu\/bigs2\/\",\"name\":\"BIG-S2\",\"description\":\"Biostatistics and Imaging Genomics analysis lab - Statistics &amp; Signal\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.med.unc.edu\/bigs2\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"GHMM: Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data - BIG-S2","robots":{"index":"noindex","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"og_locale":"en_US","og_type":"article","og_title":"GHMM: Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data - BIG-S2","og_description":"Magnetic resonance imaging (MRI) has become an important imaging technique for quantifying the spatial location and magnitude\/direction of longitudinal cartilage morphology changes in patients with osteoarthritis (OA). Although several analytical methods, such as subregion-based analysis, have been developed to refine and improve quantitative cartilage analyses, they can be suboptimal due to two major issues: the &hellip; Read more","og_url":"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/","og_site_name":"BIG-S2","article_modified_time":"2020-10-13T22:18:29+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/","url":"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/","name":"GHMM: Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data - BIG-S2","isPartOf":{"@id":"https:\/\/www.med.unc.edu\/bigs2\/#website"},"datePublished":"2018-09-18T17:07:19+00:00","dateModified":"2020-10-13T22:18:29+00:00","breadcrumb":{"@id":"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.med.unc.edu\/bigs2\/ghmm\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.med.unc.edu\/bigs2\/ghmm\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.med.unc.edu\/bigs2\/"},{"@type":"ListItem","position":2,"name":"GHMM: Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data"}]},{"@type":"WebSite","@id":"https:\/\/www.med.unc.edu\/bigs2\/#website","url":"https:\/\/www.med.unc.edu\/bigs2\/","name":"BIG-S2","description":"Biostatistics and Imaging Genomics analysis lab - Statistics &amp; Signal","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.med.unc.edu\/bigs2\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links_to":[],"_links_to_target":[],"_links":{"self":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages\/2986","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/users\/1503"}],"replies":[{"embeddable":true,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/comments?post=2986"}],"version-history":[{"count":0,"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/pages\/2986\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.med.unc.edu\/bigs2\/wp-json\/wp\/v2\/media?parent=2986"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}