{"id":125,"date":"2010-02-08T21:11:52","date_gmt":"2010-02-09T03:11:52","guid":{"rendered":"http:\/\/fs-s-wpmu-02.facsci.ualberta.ca\/ammi\/?page_id=125"},"modified":"2020-08-14T21:30:59","modified_gmt":"2020-08-14T21:30:59","slug":"parallel-visualization-of-large-medical-data","status":"publish","type":"page","link":"https:\/\/spaces.facsci.ualberta.ca\/ahci\/projects\/computer-graphic-projects\/parallel-visualization-of-large-medical-data\/","title":{"rendered":"Real-time Rendering\/Processing of Temporal Volumetric Data on a GPU"},"content":{"rendered":"<p>Real-time rendering of static volumetric data is generally known to be a memory and computationally intensive process. With the advance of graphic hardware, especially GPU, it is now possible to do this using desktop computers. However, with the evolution of real-time CT and MRI technologies, volumetric rendering is an even bigger challenge. The first one is how to reduce the data transmission between the main memory and the graphic memory. The second one is how to efficiently take advantage of the time redundancy which exists in\u00a0 time varying volumetric data. We proposed an optimized compression scheme that explores the time redundancy as well as space redundancy of time-varying volumetric data. The compressed data is then transmitted to graphic memory and directly rendered by the GPU, reducing significantly the data transfer between main memory and graphic memory.<\/p>\n<div class=\"note\">We are looking for a masters student that will be fully paid for two years to continue the development of this system using an eight GPU cluster.<\/div>\n<table border=\"0\">\n<tbody>\n<tr>\n<td>\n<p><div id=\"attachment_1067\" style=\"width: 452px\" class=\"wp-caption alignnone\"><a class=\"thickbox\" title=\"System\" href=\"https:\/\/spaces.facsci.ualberta.ca\/ammi\/wp-content\/uploads\/sites\/4\/2010\/02\/31\" rel=\"same-post-125\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-1067\" class=\"size-full wp-image-1067 \" title=\"System\" src=\"https:\/\/spaces.facsci.ualberta.ca\/ammi\/wp-content\/uploads\/sites\/4\/2010\/02\/31\" alt=\"Overview of the rendering system\" width=\"442\" height=\"262\" \/><\/a><p id=\"caption-attachment-1067\" class=\"wp-caption-text\">Overview of the rendering system<\/p><\/div><\/td>\n<\/tr>\n<tr>\n<td>\n<p><div id=\"attachment_1077\" style=\"width: 384px\" class=\"wp-caption alignnone\"><a class=\"thickbox\" title=\"Reconstruction\" href=\"https:\/\/spaces.facsci.ualberta.ca\/ammi\/wp-content\/uploads\/sites\/4\/2010\/02\/34\" rel=\"same-post-125\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-1077\" class=\"size-full wp-image-1077 \" title=\"Reconstruction\" src=\"https:\/\/spaces.facsci.ualberta.ca\/ammi\/wp-content\/uploads\/sites\/4\/2010\/02\/34\" alt=\"Reconstruction Using Various Compression Techniques\" width=\"374\" height=\"391\" \/><\/a><p id=\"caption-attachment-1077\" class=\"wp-caption-text\">Reconstruction Using Various Compression Techniques<\/p><\/div><\/td>\n<\/tr>\n<tr>\n<td>\n<p><div id=\"attachment_1066\" style=\"width: 296px\" class=\"wp-caption alignnone\"><a class=\"thickbox\" title=\"heart\" href=\"https:\/\/spaces.facsci.ualberta.ca\/ammi\/wp-content\/uploads\/sites\/4\/2010\/02\/30\" rel=\"same-post-125\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-1066\" class=\"size-full wp-image-1066 \" title=\"heart\" src=\"https:\/\/spaces.facsci.ualberta.ca\/ammi\/wp-content\/uploads\/sites\/4\/2010\/02\/30\" alt=\"Arteries and Veins Using Coronary CT Angiography\" width=\"286\" height=\"309\" \/><\/a><p id=\"caption-attachment-1066\" class=\"wp-caption-text\">Arteries and Veins Using Coronary CT Angiography<\/p><\/div><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"<p>Real-time rendering of static volumetric data is generally known to be a memory and computationally intensive process. With the advance of graphic hardware, especially GPU, it is now possible to do this using desktop computers. However, with the evolution of real-time CT and MRI technologies, volumetric rendering is an even bigger challenge. The first one<\/p>\n<p><a href=\"https:\/\/spaces.facsci.ualberta.ca\/ahci\/projects\/computer-graphic-projects\/parallel-visualization-of-large-medical-data\/\" class=\"more-link themebutton\">Read More<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1618,"menu_order":1,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-125","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/spaces.facsci.ualberta.ca\/ahci\/wp-json\/wp\/v2\/pages\/125","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/spaces.facsci.ualberta.ca\/ahci\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/spaces.facsci.ualberta.ca\/ahci\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/spaces.facsci.ualberta.ca\/ahci\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/spaces.facsci.ualberta.ca\/ahci\/wp-json\/wp\/v2\/comments?post=125"}],"version-history":[{"count":2,"href":"https:\/\/spaces.facsci.ualberta.ca\/ahci\/wp-json\/wp\/v2\/pages\/125\/revisions"}],"predecessor-version":[{"id":2628,"href":"https:\/\/spaces.facsci.ualberta.ca\/ahci\/wp-json\/wp\/v2\/pages\/125\/revisions\/2628"}],"up":[{"embeddable":true,"href":"https:\/\/spaces.facsci.ualberta.ca\/ahci\/wp-json\/wp\/v2\/pages\/1618"}],"wp:attachment":[{"href":"https:\/\/spaces.facsci.ualberta.ca\/ahci\/wp-json\/wp\/v2\/media?parent=125"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}