{"id":5584,"date":"2023-09-04T03:10:22","date_gmt":"2023-09-04T03:10:22","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/ct-images-in-covid-19\/"},"modified":"2023-09-13T11:58:28","modified_gmt":"2023-09-13T11:58:28","slug":"ct-images-in-covid-19","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/ct-images-in-covid-19\/","title":{"rendered":"CT-IMAGES-IN-COVID-19"},"featured_media":7779,"template":"","citation-tax":[],"cancer_types":["COVID-19 (non-cancer)"],"citations":[4446,4447,2925,4448],"collection_doi":"10.7937\/TCIA.2020.GQRY-NC81","collection_download_info":"Click the Versions tab for more info about data releases.\nPlease contact <a href=\"mailto:help@cancerimagingarchive.net\">help@cancerimagingarchive.net<\/a> with any questions regarding usage.","collection_downloads":[4996,4997],"full_export":"<h1 id=\"CTImagesinCOVID19-Summary\">Summary<\/h1><span style=\"color: rgb(0,0,0);\"><span class=\"confluence-embedded-file-wrapper image-right-wrapper confluence-embedded-manual-size\"><img class=\"confluence-embedded-image image-right\" draggable=\"false\" height=\"250\" src=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/embedded-page\/Public\/CT%20Images%20in%20COVID-19\/3D%20coronal%20CT.png?api=v2\"><\/span><span class=\"confluence-embedded-file-wrapper image-right-wrapper confluence-embedded-manual-size\"><img class=\"confluence-embedded-image image-right\" draggable=\"false\" height=\"250\" src=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/embedded-page\/Public\/CT%20Images%20in%20COVID-19\/COVID-TCIA1.jpg?api=v2\"><\/span>These retrospective NIfTI image datasets consists of unenhanced chest CTs:\u00a0<\/span><\/p><ul><li>First dataset - from 632 patients\u00a0with COVID-19 infections at initial point of care, and<\/li><li>Second dataset - a second set of 121<span style=\"color: inherit;\">\u00a0CTs from 29 patients<\/span><span>\u00a0<\/span>with COVID-19 infections with serial \/ sequential CTs.<\/li><\/ul><p>The initial images for both datasets were acquired\u00a0at the point of care in an outbreak setting\u00a0from patients with Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2.<\/p><p>Patients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. All patients had a positive RT-PCR for SARS-CoV-2 from a sample obtained within 1 day of the initial CT. CT exams were performed without intravenous contrast and with a soft tissue reconstruction algorithm.\u00a0 The DICOM images were subsequently converted into NIfTI format.\u00a0The second dataset also had other follow up CTs, in addition to the initial point of care CT.<\/p><p><span style=\"color: rgb(32,31,30);\">A multidisciplinary team trained several models using portions of the first dataset<\/span><span style=\"color: rgb(32,31,30);\">, along with additional CTs and manually annotated images from other sources. A classification model derived in part from the first dataset is described in a Nature Communications manuscript at<\/span>:\u00a0\u00a0<a rel=\"nofollow\" href=\"https:\/\/doi.org\/10.1038\/s41467-020-17971-2\" class=\"external-link\">https:\/\/doi.org\/10.1038\/s41467-020-17971-2<\/a>.\u00a0 <span style=\"color: rgb(32,31,30);\">The\u00a0NVIDIA-related frameworks and models specific to this publication are available at no cost as part of the NVIDIA Clara Train SDK at<\/span><span>\u00a0<\/span><a href=\"https:\/\/ngc.nvidia.com\/catalog\/containers\/nvidia:clara:ai-covid-19\" class=\"external-link\" rel=\"nofollow\">https:\/\/ngc.nvidia.com\/catalog\/containers\/nvidia:clara:ai-covid-19<\/a>.\u00a0<span style=\"color: rgb(32,31,30);\">This includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions.\u00a0The second data set of<span>\u00a0<\/span><\/span><span style=\"color: rgb(32,31,30);\"><span style=\"color: rgb(0,0,0);\">121 serial \/ sequential CTs in 29<\/span> patient<\/span><span style=\"color: rgb(32,31,30);\">s is reported in a Scientific Reports manuscript at\u00a0<span>\u00a0<\/span><a href=\"https:\/\/doi.org\/10.1038\/s41598-021-85694-5\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1038\/s41598-021-85694-5<\/a>.\u00a0<\/span><h3 id=\"CTImagesinCOVID19-Acknowledgements\"><span>Acknowledgements<\/span><\/h3><p>The Imaging AI in COVID team would like to acknowledge the following individuals who supported this multi-disciplinary multi-national team effort:<\/p><ul><li>All frontline workers and Peng An, Sheng Xu, Evrim B. Turkbey, Stephanie A. Harmon, Thomas H. Sanford, Amel Amalou, Michael Kassin, Nicole Varble, Maxime Blain, Dilara Long, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Mona Flores, Francesca Patella, Maurizio Cariati, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Ulas Bagci, Daguang Xu, Hayet Amalou, Robert Suh, Gianpaolo Carrafiello, Baris Turkbey, Bradford J. Wood.<\/li><li>Thanks for leadership support to:\u00a0 John Gallin, Steve Holland, Cliff Lane, Bruce Tromberg, Tom Misteli, Bill Dahut.<\/li><li>Supported by the NIH Center for Interventional Oncology and the NIH Intramural Targeted Anti-COVID-19 (ITAC) Program.<\/li><\/ul><h3 id=\"CTImagesinCOVID19-TCIACOVID-19Datasets\"><span style=\"color: rgb(33,37,41);\">TCIA COVID-19 Datasets<\/span><\/h3><p><span style=\"color: rgb(33,37,41);\">Additional datasets and information about TCIA efforts to support COVID-19 research can be found\u00a0<a href=\"https:\/\/wiki.cancerimagingarchive.net\/x\/aI0vB\" rel=\"nofollow\">here<\/a>.<\/span><\/p><div class=\"tab-style-builtin\"><div class=\"localtabs-macro\"><div class=\"aui-tabs horizontal-tabs\" role=\"application\" data-aui-responsive=\"true\"><ul class=\"tabs-menu\"><li class=\"menu-item bv-localtab  active-tab \"><a href=\"#70227107b92475d33ae7421a9b9c426f5bb7d5b3\"><strong>Data Access<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#70227107e690fb9e67f8493db4bcc4c38e860369\"><strong>Detailed Description<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#70227107347cd6d9a7bc447a8c4f0fcd6e3585dd\"><strong>Citations &amp; Data Usage Policy<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#702271075e5f34eb10734b148c49ef9664aa2eb3\"><strong>Versions<\/strong><\/a> <\/li><\/ul><div class=\"tabs-pane  active-pane \" id=\"70227107b92475d33ae7421a9b9c426f5bb7d5b3\" active=\"true\" name=\"Data Access\" ><h3 id=\"CTImagesinCOVID19-DataAccess\">Data Access<\/h3><div class=\"table-wrap\"><table class=\"wrapped confluenceTable\"><colgroup><col\/><col\/><col\/><\/colgroup><tbody><tr><th class=\"confluenceTh\">Data Type<\/th><th class=\"confluenceTh\">Download all or Query\/Filter<\/th><th class=\"confluenceTh\">License<\/th><\/tr><tr><td class=\"confluenceTd\"><p>Images (NIfTI, 12.71 GB)<\/p><p>First dataset<\/p><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p><br\/>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/faspex.cancerimagingarchive.net\/aspera\/faspex\/external_deliveries\/44?passcode=7f03b74391b2da371d9e393255847aa60027f6c0\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><br\/><\/p><p>(Download and apply the\u00a0<a href=\"https:\/\/www.ibm.com\/aspera\/connect\/\" class=\"external-link\" rel=\"nofollow\">IBM-Aspera-Connect plugin\u00a0<\/a>to your browser to retrieve this faspex package)\u00a0<\/p><\/div><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\" class=\"external-link\" rel=\"nofollow\">CC BY 4.0<\/a><\/p><\/div><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Images (NIfTI, 2 GB)<\/p><p>Second dataset<\/p><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p><br\/>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/faspex.cancerimagingarchive.net\/aspera\/faspex\/external_deliveries\/134?passcode=c6849b3481a1b240daebc7e3c50e1e4f7e3fa424\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><p class=\"auto-cursor-target\">(Download and apply the\u00a0<a href=\"https:\/\/www.ibm.com\/aspera\/connect\/\" class=\"external-link\" rel=\"nofollow\">IBM-Aspera-Connect plugin\u00a0<\/a>to your browser to retrieve this faspex package)<\/p><\/div><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\" class=\"external-link\" rel=\"nofollow\">CC BY 4.0<\/a><\/p><\/div><\/td><\/tr><\/tbody><\/table><\/div><p>Click the Versions tab for more info about data releases.<\/p><p><span style=\"color: rgb(23,43,77);\">Please contact <a href=\"mailto:help@cancerimagingarchive.net\" class=\"external-link\" rel=\"nofollow\">help@cancerimagingarchive.net<\/a> with any questions regarding usage.<\/span><\/p><\/div><div class=\"tabs-pane \" id=\"70227107e690fb9e67f8493db4bcc4c38e860369\" name=\"Detailed Description\" ><h3 id=\"CTImagesinCOVID19-DetailedDescription\">Detailed Description<\/h3><div class=\"table-wrap\"><table class=\"wrapped confluenceTable\"><colgroup><col\/><col\/><\/colgroup><tbody><tr><th class=\"confluenceTh\"><p>Image Statistics<\/p><\/th><th class=\"confluenceTh\"><br\/><\/th><\/tr><tr><td class=\"confluenceTd\"><p>Modalities<\/p><\/td><td class=\"confluenceTd\"><p>CT<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Patients<\/p><\/td><td class=\"confluenceTd\"><p>661<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Series<\/p><\/td><td class=\"confluenceTd\">771<\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Images Size (GB)<\/td><td colspan=\"1\" class=\"confluenceTd\">14.7<\/td><\/tr><\/tbody><\/table><\/div><p><br\/><\/p><p>Link to publication below contains AI model that was only partly derived from this data, and also from other data not present here on TCIA.<\/p><ul><li>Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., Blain, M., Kassin, M., Long, D., Varble, N., Walker, S. M., Bagci, U., Ierardi, A. M., Stellato, E., Plensich, G. G., Franceschelli, G., Girlando, C., Irmici, G., Labella, D., Hammoud, D., Malayeri, A., Jones, E., Summers, R. M., Choyke, P.L., Xu, D., Flores, M., Tamura, K., Obinata, H., Mori, H., Patella, F., Cariati, M., Carrafiello, G., An, P., Wood, B. J., &amp; Turkbey, B. (2020). Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nature Communications, 11(1). <a href=\"https:\/\/doi.org\/10.1038\/s41467-020-17971-2\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1038\/s41467-020-17971-2<\/a><\/li><\/ul><\/div><div class=\"tabs-pane \" id=\"70227107347cd6d9a7bc447a8c4f0fcd6e3585dd\" name=\"Citations BITVOODOO_ANDamp; Data Usage Policy\" ><h3 id=\"CTImagesinCOVID19-Citations&amp;DataUsagePolicy\">Citations &amp; Data Usage Policy<\/h3><p><span>\n<p>\nUsers must abide by the <a href=\"https:\/\/wiki.cancerimagingarchive.net\/x\/c4hF\" class=\"external-link\" rel=\"nofollow\">TCIA Data Usage Policy and Restrictions<\/a>. Attribution should include references to the following citations:\n<\/p><\/span><\/p><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">Data Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p><span style=\"color: black;\"><span><span style=\"color: rgb(102,102,102);\"><span style=\"color: rgb(0,0,0);\">An, P., Xu, S., Harmon, S. A., Turkbey, E. B., Sanford, T. H., Amalou, A., Kassin, M., Varble, N., Blain, M., Anderson, V., Patella, F., Carrafiello, G., Turkbey, B. T., &amp; Wood, B. J. (2020). <strong>CT Images in COVID-19 [Data set].<\/strong> The Cancer Imaging Archive<\/span>. <a href=\"https:\/\/doi.org\/10.7937\/TCIA.2020.GQRY-NC81\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.7937\/TCIA.2020.GQRY-NC81<\/a><\/span><\/span><\/span><\/p><\/div><\/div><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">Acknowledgement<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p><span style=\"color: rgb(31,73,125);\">The Multi-national NIH Consortium for CT AI in COVID-19.<\/span><\/p><\/div><\/div><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">Publication Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p><span style=\"color: rgb(102,102,102);\"><span style=\"color: rgb(0,0,0);\">Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., Blain, M., Kassin, M., Long, D., Varble, N., Walker, S. M., Bagci, U., Ierardi, A. M., Stellato, E., Plensich, G. G., Franceschelli, G., Girlando, C., Irmici, G., Labella, D., Hammoud, D., Malayeri, A., Jones, E., Summers, R. M., Choyke, P.L., Xu, D., Flores, M., Tamura, K., Obinata, H., Mori, H., Patella, F., Cariati, M., Carrafiello, G., An, P., Wood, B. J., &amp; Turkbey, B. (2020). <strong>Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.<\/strong> Nature Communications, 11(1)<\/span>. <a href=\"https:\/\/doi.org\/10.1038\/s41467-020-17971-2\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1038\/s41467-020-17971-2<\/a><\/span><\/p><\/div><\/div><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">TCIA Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p><span style=\"color: rgb(102,102,102);\"><span style=\"color: rgb(0,0,0);\">Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., &amp; Prior, F. (2013). <strong>The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository.<\/strong> Journal of Digital Imaging, 26(6), 1045\u20131057.<\/span> <a href=\"https:\/\/doi.org\/10.1007\/s10278-013-9622-7\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/s10278-013-9622-7<\/a><\/span><\/p><\/div><\/div><h3 id=\"CTImagesinCOVID19-OtherPublicationsUsingThisData\">Other Publications Using This Data<\/h3><p><span>TCIA maintains\u00a0<\/span><a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\" class=\"external-link\" rel=\"nofollow\">a list of publications<\/a><span> which leverage our data. <\/span> If you have a manuscript you'd like to add please <a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\" class=\"external-link\" rel=\"nofollow\">contact the TCIA Helpdesk<\/a>.<\/p><\/div><div class=\"tabs-pane \" id=\"702271075e5f34eb10734b148c49ef9664aa2eb3\" name=\"Versions\" ><h3 id=\"CTImagesinCOVID19-Version2(Current):Updated2021\/05\/25\">Version 2 (Current): Updated 2021\/05\/25<\/h3><div class=\"table-wrap\"><table class=\"fixed-table wrapped confluenceTable\"><colgroup><col style=\"width: 288.0px;\"\/><col style=\"width: 408.0px;\"\/><\/colgroup><tbody><tr><th class=\"confluenceTh\"><span>Data Type<\/span><\/th><th class=\"confluenceTh\"><span>Download all or Query\/Filter<\/span><\/th><\/tr><tr><td class=\"confluenceTd\"><p>Images (NIfTI, 12.71 GB)<\/p><p>First dataset<\/p><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p><br\/>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/faspex.cancerimagingarchive.net\/aspera\/faspex\/external_deliveries\/44?passcode=7f03b74391b2da371d9e393255847aa60027f6c0\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Images (NIfTI, 2 GB)<\/p><p>Second dataset<\/p><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p><br\/>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/faspex.cancerimagingarchive.net\/aspera\/faspex\/external_deliveries\/134?passcode=c6849b3481a1b240daebc7e3c50e1e4f7e3fa424\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><\/tr><\/tbody><\/table><\/div><p>Added second dataset, 29 patients\/121 CT images.<\/p><h3 id=\"CTImagesinCOVID19-Version1:Updated2020\/08\/31\">Version 1: Updated 2020\/08\/31<\/h3><div class=\"table-wrap\"><table class=\"wrapped fixed-table confluenceTable\"><colgroup><col style=\"width: 288.0px;\"\/><col style=\"width: 422.0px;\"\/><\/colgroup><tbody><tr><th class=\"confluenceTh\"><span>Data Type<\/span><\/th><th class=\"confluenceTh\"><span>Download all or Query\/Filter<\/span><\/th><\/tr><tr><td class=\"confluenceTd\"><p><span>Images (NIfTI, 12.71 GB)<\/span><\/p><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p><br\/>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/faspex.cancerimagingarchive.net\/aspera\/faspex\/external_deliveries\/44?passcode=7f03b74391b2da371d9e393255847aa60027f6c0\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/div><\/div><p><br\/><\/p><p><br\/><\/p><p><br\/><\/p><p><br\/><\/p><p><br\/><\/p><p><br\/><\/p><p><br\/><\/p><p><br\/><\/p>","versions":false,"additional_resources":"","cancer_locations":["Lung"],"collection_page_accessibility":"Public","publications_related":"","version_change_log":"","version_change_log_archived":"","analysis_results":"","collection_status":"Complete","publications_using":"TCIA maintains\u00a0<a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\">a list of publications<\/a> which leverage our data.  If you have a manuscript you'd like to add please <a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\">contact the TCIA Helpdesk<\/a>.","species":["Human"],"collection_title":"CT Images in COVID-19","detailed_description":"<br\/>\nLink to publication below contains AI model that was only partly derived from this data, and also from other data not present here on TCIA.\n<ul><li>Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., Blain, M., Kassin, M., Long, D., Varble, N., Walker, S. M., Bagci, U., Ierardi, A. M., Stellato, E., Plensich, G. G., Franceschelli, G., Girlando, C., Irmici, G., Labella, D., Hammoud, D., Malayeri, A., Jones, E., Summers, R. M., Choyke, P.L., Xu, D., Flores, M., Tamura, K., Obinata, H., Mori, H., Patella, F., Cariati, M., Carrafiello, G., An, P., Wood, B. J., &amp; Turkbey, B. (2020). Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nature Communications, 11(1). <a href=\"https:\/\/doi.org\/10.1038\/s41467-020-17971-2\">https:\/\/doi.org\/10.1038\/s41467-020-17971-2<\/a><\/li><\/ul>","related_analysis_results":false,"subjects":"661","collection_short_title":"CT Images in COVID-19","data_types":["CT"],"date_updated":"2023-09-13","collection_browse_title":"","supporting_data":false,"collection_featured_image":{"ID":"7779","post_author":"6","post_date":"2023-09-13 03:42:31","post_date_gmt":"2023-09-13 03:42:31","post_content":"","post_title":"3D-coronal-CT","post_excerpt":"","post_status":"inherit","comment_status":"open","ping_status":"closed","post_password":"","post_name":"3d-coronal-ct","to_ping":"","pinged":"","post_modified":"2023-09-13 11:58:29","post_modified_gmt":"2023-09-13 11:58:29","post_content_filtered":"","post_parent":"5584","guid":"https:\/\/cm.vastapps.dev\/wp-content\/uploads\/3D-coronal-CT.png","menu_order":"0","post_type":"attachment","post_mime_type":"image\/png","comment_count":"0","pod_item_id":"7779"},"collection_summary":"<div class=\"cm-content-image\"><a href=\"\/wp-content\/uploads\/COVID-TCIA1.jpg\" rel=\"prettyPhoto noopener\" target=\"_blank\"><img src=\"\/wp-content\/uploads\/COVID-TCIA1.jpg\"\/><\/a><\/div>These retrospective NIfTI image datasets consists of unenhanced chest CTs:\u00a0<ul><li>First dataset - from 632 patients\u00a0with COVID-19 infections at initial point of care, and<\/li><li>Second dataset - a second set of 121\u00a0CTs from 29 patients\u00a0with COVID-19 infections with serial \/ sequential CTs.<\/li><\/ul><p>The initial images for both datasets were acquired\u00a0at the point of care in an outbreak setting\u00a0from patients with Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2.<\/p><p>Patients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. All patients had a positive RT-PCR for SARS-CoV-2 from a sample obtained within 1 day of the initial CT. CT exams were performed without intravenous contrast and with a soft tissue reconstruction algorithm.\u00a0 The DICOM images were subsequently converted into NIfTI format.\u00a0The second dataset also had other follow up CTs, in addition to the initial point of care CT.<\/p><p>A multidisciplinary team trained several models using portions of the first dataset, along with additional CTs and manually annotated images from other sources. A classification model derived in part from the first dataset is described in a Nature Communications manuscript at:\u00a0\u00a0<a href=\"https:\/\/doi.org\/10.1038\/s41467-020-17971-2\">https:\/\/doi.org\/10.1038\/s41467-020-17971-2<\/a>.\u00a0 The\u00a0NVIDIA-related frameworks and models specific to this publication are available at no cost as part of the NVIDIA Clara Train SDK at\u00a0<a href=\"https:\/\/ngc.nvidia.com\/catalog\/containers\/nvidia:clara:ai-covid-19\">https:\/\/ngc.nvidia.com\/catalog\/containers\/nvidia:clara:ai-covid-19<\/a>.\u00a0This includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions.\u00a0The second data set of\u00a0121 serial \/ sequential CTs in 29 patients is reported in a Scientific Reports manuscript at\u00a0\u00a0<a href=\"https:\/\/doi.org\/10.1038\/s41598-021-85694-5\">https:\/\/doi.org\/10.1038\/s41598-021-85694-5<\/a>.\u00a0<\/p>","collection_acknowledgements":"The Imaging AI in COVID team would like to acknowledge the following individuals who supported this multi-disciplinary multi-national team effort:\n<ul><li>All frontline workers and Peng An, Sheng Xu, Evrim B. Turkbey, Stephanie A. Harmon, Thomas H. Sanford, Amel Amalou, Michael Kassin, Nicole Varble, Maxime Blain, Dilara Long, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Mona Flores, Francesca Patella, Maurizio Cariati, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Ulas Bagci, Daguang Xu, Hayet Amalou, Robert Suh, Gianpaolo Carrafiello, Baris Turkbey, Bradford J. Wood.<\/li><li>Thanks for leadership support to:\u00a0 John Gallin, Steve Holland, Cliff Lane, Bruce Tromberg, Tom Misteli, Bill Dahut.<\/li><li>Supported by the NIH Center for Interventional Oncology and the NIH Intramural Targeted Anti-COVID-19 (ITAC) Program.<\/li><\/ul>\n<h3>TCIA COVID-19 Datasets<\/h3>\nAdditional datasets and information about TCIA efforts to support COVID-19 research can be found\u00a0<a href=\"https:\/\/wiki.cancerimagingarchive.net\/x\/aI0vB\">here<\/a>.","collection_funding":"","hide_from_browse_table":[],"_links":{"self":[{"href":"https:\/\/cm.vastapps.dev\/api\/v1\/collections\/5584"}],"collection":[{"href":"https:\/\/cm.vastapps.dev\/api\/v1\/collections"}],"about":[{"href":"https:\/\/cm.vastapps.dev\/api\/wp\/v2\/types\/tcia_collection"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cm.vastapps.dev\/api\/wp\/v2\/media\/7779"}],"wp:attachment":[{"href":"https:\/\/cm.vastapps.dev\/api\/wp\/v2\/media?parent=5584"}],"wp:term":[{"taxonomy":"tcia_citation_tax","embeddable":true,"href":"https:\/\/cm.vastapps.dev\/api\/v1\/citation-tax?post=5584"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}