{"id":7487,"date":"2020-08-07T23:07:40","date_gmt":"2020-08-08T04:07:40","guid":{"rendered":"https:\/\/cornflowerblue-rail-980953.hostingersite.com\/?p=7487"},"modified":"2020-08-08T03:58:38","modified_gmt":"2020-08-08T08:58:38","slug":"whats-new-in-cardiothoracic-imaging-chinese-august-2020","status":"publish","type":"post","link":"https:\/\/staging-hub.acoredu.com\/zh-hans\/whats-new-in-cardiothoracic-imaging-chinese-august-2020\/","title":{"rendered":"What&#8217;s new in Cardiothoracic Imaging (Chinese) &#8211; August 2020"},"content":{"rendered":"<h3>[embedyt] https:\/\/www.youtube.com\/watch?v=6opJVhfH2IE[\/embedyt]<\/h3>\n<p>&nbsp;<\/p>\n<h3><span style=\"color: #800080;\"><strong>Thoracic<\/strong><\/span><\/h3>\n<h3><span style=\"color: #800080;\"><strong>\u80f8\u90e8<\/strong><\/span><\/h3>\n<p>&nbsp;<\/p>\n<h5><span style=\"color: #3366ff;\"><strong>Using Quantitative Computed Tomographic Imaging to Understand Chronic Obstructive Pulmonary Disease and Fibrotic Interstitial Lung Disease. State of the Art and Future Directions.<\/strong><\/span><\/h5>\n<h5><span style=\"color: #3366ff;\"><strong>\u4f7f\u7528\u5b9a\u91cf\u7684\u8ba1\u7b97\u673a\u65ad\u5c42\u626b\u63cf\u6210\u50cf\u6280\u672f\u8bca\u65ad\u6162\u6027\u963b\u585e\u6027\u80ba\u75be\u75c5\u548c\u7ea4\u7ef4\u95f4\u8d28\u6027\u80ba\u75be\u75c5\uff1a\u65b0\u6280\u672f\u53ca\u672a\u6765\u8d8b\u52bf<\/strong><\/span><\/h5>\n<p>Castillo-Saldana, D., et al. (2020). &#8220;Using Quantitative Computed Tomographic Imaging to Understand Chronic Obstructive Pulmonary Disease and Fibrotic Interstitial Lung Disease: State of the Art and Future Directions.&#8221; <u>J Thorac Imaging<\/u> <strong>35<\/strong>(4): 246-254.<\/p>\n<p>Key Words: COPD, ILD, Quantitative CT<\/p>\n<p>\u5173\u952e\u8bcd\uff1a\u6162\u6027\u963b\u585e\u6027\u80ba\u75be\u75c5\uff1b\u95f4\u8d28\u6027\u80ba\u75be\u75c5\uff1b\u5b9a\u91cfCT\u6210\u50cf<\/p>\n<p><a href=\"https:\/\/journals.lww.com\/thoracicimaging\/Fulltext\/2020\/07000\/Using_Quantitative_Computed_Tomographic_Imaging_to.6.aspx\">https:\/\/journals.lww.com\/thoracicimaging\/Fulltext\/2020\/07000\/Using_Quantitative_Computed_Tomographic_Imaging_to.6.aspx<\/a><\/p>\n<p>\u6765\u81ea\u5723\u4fdd\u7f57\u533b\u9662\u548c\u4e0d\u5217\u98a0\u54e5\u4f26\u6bd4\u4e9a\u5927\u5b66\u7684\u7814\u7a76\u4eba\u5458\u62a5\u9053\u4e86\u5173\u4e8e\u5b9a\u91cfCT\uff08QCT\uff09\u5728\u6162\u6027\u963b\u585e\u6027\u80ba\u75be\u75c5\uff08COPD\uff09\u548c\u95f4\u8d28\u6027\u80ba\u75be\u75c5\uff08ILD\uff09\u9886\u57df\u4e2d\u7684\u5e94\u7528\u3002 Ryerson\u7b49\u4eba\u63d0\u51faQCT\u53ef\u7528\u4e8e\u5b9a\u6027\u8bc4\u4f30COPD\u548cILD\u8fd9\u4e24\u79cd\u590d\u6742\u75be\u75c5\u7c7b\u578b\u7684\u8bca\u65ad\u53ca\u75c5\u7a0b\u8fdb\u5c55\u968f\u8bbf\u3002\u5728\u8bca\u65adCOPD\u65f6QCT\u53ef\u4ee5\u51c6\u786e\u5730\u663e\u793a\u80ba\u6c14\u80bf\u533a\u57df\u3001\u53d1\u73b0\u6c14\u9053\u589e\u539a\u3001\u8fdb\u884c\u652f\u6c14\u7ba1\u68113D\u91cd\u5efa\u4ee5\u53d1\u73b0\u7ec8\u672b\u7ec6\u652f\u6c14\u7ba1\u7684\u65e9\u671f\u75c5\u53d8\u3002 QCT\u5728COPD\u4e34\u5e8a\u5e94\u7528\u7684\u4e3b\u8981\u7684\u5c40\u9650\u6027\u5305\u62ec\u7535\u79bb\u8f90\u5c04\u7684\u66b4\u9732\u3001CT\u626b\u63cf\u4eea\u548c\u60a3\u8005\u7684\u53ef\u53d8\u6027\uff0c\u4ee5\u53ca\u624b\u5de5\u540e\u5904\u7406\u5f15\u8d77\u7684\u8d28\u91cf\u63a7\u5236\u95ee\u9898\u3002QCT\u53ef\u4ee5\u5728\u901a\u8fc7\u68c0\u6d4b\u80ba\u5b9e\u8d28\u5bc6\u5ea6\u53d8\u5316\u5e76\u7ed8\u5236\u5bc6\u5ea6\u76f4\u65b9\u56fe\uff0c\u5728\u968f\u8bbfILD\u7684\u75be\u75c5\u8fdb\u5c55\u65f6\u8d77\u91cd\u8981\u4f5c\u7528\u3002\u8fd9\u4e9b\u5ba2\u89c2\u6570\u636e\u4e0e\u5b9a\u6027\u8bc4\u4f30\u76f8\u7ed3\u5408\u53ef\u4e3a\u4e34\u5e8a\u533b\u751f\u968f\u8bbf\u9ad8\u5371\u60a3\u8005\u7fa4\u4f53\u7684\u75be\u75c5\u8fdb\u5c55\u548c\u6cbb\u7597\u6548\u679c\u63d0\u4f9b\u4e00\u79cd\u66f4\u7cbe\u786e\u7684\u65b9\u6cd5\u3002QCT\u7684\u5b9a\u91cf\u7b97\u6cd5\u5728\u4e34\u5e8a\u5e94\u7528\u4e4b\u524d\u4ecd\u9700\u8981\u8fdb\u884c\u8f83\u5927\u7684\u6539\u8fdb\uff0c\u4f46\u73b0\u5728\u7684\u7ed3\u679c\u5df2\u7ecf\u662f\u5341\u5206\u4ee4\u4eba\u9f13\u821e\u7684\u53d1\u5c55\u3002<\/p>\n<p><strong>\u00a0<\/strong><\/p>\n<h3><span style=\"color: #800080;\"><strong>COVID<\/strong><\/span><\/h3>\n<h3><span style=\"color: #800080;\"><strong>\u51a0\u72b6\u75c5\u6bd2<\/strong><\/span><\/h3>\n<p>&nbsp;<\/p>\n<h5><span style=\"color: #3366ff;\"><strong>Pulmonary Thromboembolism in COVID-19: Venous Thromboembolism or Arterial Thrombosis?<\/strong><\/span><\/h5>\n<h5><span style=\"color: #3366ff;\"><strong>COVID-19\u4e2d\u7684\u80ba\u6813\u585e\uff1a\u9759\u8109\u8840\u6813\u6813\u585e\u8fd8\u662f\u52a8\u8109\u8840\u6813\u5f62\u6210\uff1f<\/strong><\/span><\/h5>\n<p>Ferrari, F., et al. (2020). \u201cPulmonary Thromboembolism in COVID-19: Venous Thromboembolism or Arterial Thrombosis?\u201d RSNA<\/p>\n<p>Cavagna, E., et al. (2020). &#8220;Pulmonary Thromboembolism in COVID-19: Venous Thromboembolism or Arterial Thrombosis?&#8221; <u>Radiology: Cardiothoracic Imaging<\/u> <strong>2<\/strong>(4): e200289.<\/p>\n<p><a href=\"https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020200289\">https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020200289<\/a><\/p>\n<p>Key Words: COVID-19, Pulmonary Embolus, Thromboembolism<\/p>\n<p>\u5173\u952e\u8bcd\uff1aCOVID-19\uff0c\u80ba\u6813\u585e\uff0c\u8840\u6813\u6813\u585e<\/p>\n<p>Cavagna\u7b49\u610f\u5927\u5229\u7684\u653e\u5c04\u5b66\u5bb6\u63d0\u4f9b\u4e86\u5c06\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u5371\u91cd\u75c7\u60a3\u8005\u4e0e\u8840\u6813\u6813\u585e\u76f8\u5173\u8054\u7684\u89c6\u89d2\u3002COVID-19\u60a3\u8005\u88ab\u53d1\u73b0\u6709\u66f4\u9ad8\u7684\u7684\u80ba\u52a8\u8109\u6813\u585e\uff08PE\uff09\u53d1\u751f\u7387\uff0c\u66f4\u4ee4\u4eba\u62c5\u5fe7\u7684\u662f\u5371\u91cd\u75c7\u60a3\u8005\u7684\u53d1\u75c5\u7387\u66f4\u9ad8\u3002 \u5728\u8fd9\u9879\u56de\u987e\u6027\u7814\u7a76\u4e2d\uff0c 109\u4f8bCOVID-19\u60a3\u8005\u8868\u73b0\u7591\u4f3cPE\u7684\u75c7\u72b6\u800c\u4e14\u884cCT\u80ba\u8840\u7ba1\u6210\u50cf\uff08CTPA\uff09\u6392\u9664\u80ba\u6813\u585e\u88ab\u7eb3\u5165\u8be5\u7814\u7a76\u3002\u91c7\u7528\u534a\u5b9a\u91cf\u8bc4\u5206\uff080-5\uff09\u7684\u65b9\u6cd5\u6765\u8bc4\u4f30\u60a3\u8005\u76845\u4e2a\u80ba\u53f6\u53d7\u7d2f\u60c5\u51b5\uff0c\u5f53 \u60a3\u8005CT\u8bc4\u5206\u8f83\u9ad8\u4e14\u5b9e\u9a8c\u5ba4\u503c\u51fa\u73b0\u5f02\u5e38\uff08\u4f8b\u5982LDH\uff0cCRP\u548cd-dimer\uff09\u65f6\u80ba\u8840\u6813\u6813\u585e\u53d1\u751f\u7387\u66f4\u9ad8\u3002\u8840\u6db2\u9ad8\u51dd\u72b6\u6001\u6613\u7d2f\u53ca\u6700\u6613\u88abCOVID\u611f\u67d3\u7684\u80ba\u5b9e\u8d28\u7684\u80ba\u6bb5\u52a8\u8109\uff0890.2\uff05\uff09\u548c\u4e9a\u80ba\u6bb5\u52a8\u8109\uff0861\uff05\uff09\u3002\u4f5c\u8005\u63a8\u6d4b\u80ba\u52a8\u8109\u8840\u6813\u5f62\u6210\u53ef\u80fd\u662f\u7ee7\u53d1\u4e8e\u5c40\u90e8\u80ba\u90e8\u708e\u75c7\u548c\u9ad8\u51dd\u72b6\u6001\uff0c\u800c\u975e\u6813\u5b50\u6240\u81f4\u3002\u5c3d\u7ba1\u672c\u7814\u7a76\u672a\u80fd\u8bc1\u5b9e\u8fd9\u4e00\u5047\u8bbe\uff0c\u4f46\u4f5c\u8005\u501f\u9274\u4e86\u5176\u4ed6\u7684\u7814\u7a76\u6210\u679c\u5bf9\u5176\u5047\u8bbe\u8fdb\u4e00\u6b65\u8bba\u8bc1\uff0c\u4e5f\u5e0c\u671b\u5c06\u6765\u53ef\u4ee5\u901a\u8fc7\u66f4\u5927\u5b97\u7684\u6570\u636e\u7684\u7814\u7a76\u6765\u9a8c\u8bc1\u5176\u5047\u8bbe\u3002<\/p>\n<p>&nbsp;<\/p>\n<h5><strong><span style=\"color: #3366ff;\">Radiological Society of North America Chest CT Classification System for Reporting COVID-19 Pneumonia: Interobserver Variability and Correlation with RT-PCR<\/span><\/strong><\/h5>\n<h5><strong><span style=\"color: #3366ff;\">\u5317\u7f8e\u653e\u5c04\u533b\u5b66\u4f1aCOVID-19\u80ba\u708e\u7684\u80f8\u90e8CT\u5206\u7c7b\u548c\u62a5\u544a\u7cfb\u7edf\uff1a\u89c2\u5bdf\u8005\u95f4\u7684\u5dee\u5f02\u6027\u53ca\u4e0eRT-PCR\u7ed3\u679c\u7684\u76f8\u5173\u6027<\/span><\/strong><\/h5>\n<p>Jaegere, T. M. H. d., et al. (2020). &#8220;Radiological Society of North America Chest CT Classification System for Reporting COVID-19 Pneumonia: Interobserver Variability and Correlation with RT-PCR.&#8221; <u>Radiology: Cardiothoracic Imaging<\/u> <strong>2<\/strong>(3): e200213.<\/p>\n<p>Keywords: COVID-19, RSNA Classification, CORADS<\/p>\n<p>\u5173\u952e\u8bcd\uff1aCOVID-19\uff0cRSNA\u5206\u7c7b\u65b9\u6cd5\uff0cCORADS<\/p>\n<p><a href=\"https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020200213\">https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020200213<\/a><\/p>\n<p>\u8377\u5170\u7956\u5fb7\u5170\u533b\u5b66\u4e2d\u5fc3\u7684\u653e\u5c04\u79d1\u533b\u751f\u63a2\u8ba8\u4e86\u653e\u5c04\u79d1\u533b\u751f\u5728\u4f7f\u7528RSNA\u7684 COVID-19\u80f8\u90e8CT\u5206\u7c7b\u7cfb\u7edf\u548cCOVID-19\u62a5\u544a\u548c\u6570\u636e\u7cfb\u7edf\uff08CO-RADS\uff09\u65f6\u7684\u89c2\u5bdf\u8005\u95f4\u4e00\u81f4\u6027\uff1b\u5e76\u5c06\u7ed3\u679c\u4e0e\u60a3\u8005\u7684PCR\u7ed3\u679c\u8fdb\u884c\u5bf9\u7167\u3002\u572896\u4f8b\u60a3\u8005\u80f8\u90e8CT\u7684\u56de\u987e\u6027\u5206\u6790\u4e2d\uff0c\u4e24\u540d\u653e\u5c04\u79d1\u4e3b\u6cbb\u533b\u5e08\u548c\u4e00\u540d\u5177\u67095\u5e74\u5de5\u4f5c\u7ecf\u9a8c\u7684\u4f4f\u9662\u533b\u5e08\u5206\u522b\u4f7f\u7528RSNA\u80f8\u90e8CT\u5206\u7c7b\u7cfb\u7edf\u548cCORADS\u5206\u7c7b\u7cfb\u7edf\u5206\u6790\u89c2\u5bdf\u8005\u95f4\u7684\u5dee\u5f02\u6027\u3002\u653e\u5c04\u79d1\u533b\u751f\u6839\u636eCOVID\u7684\u8868\u73b0\u5c06CT\u5206\u4e3a\u201c\u5178\u578b\u201d\u3001\u201c\u4e0d\u786e\u5b9a\u201d\u3001\u201c\u4e0d\u5178\u578b\u201d\u6216\u201c\u9634\u6027\u201d\u51e0\u7c7b\u3002\u672c\u7814\u7a76\u53d1\u73b0\u4e24\u540d\u4e3b\u6cbb\u533b\u5e08\u7684\u89c2\u5bdf\u95f4\u4e00\u81f4\u6027\u8f83\u597d\uff0c\u800c\u4e3b\u6cbb\u533b\u5e08\u4e0e\u4f4f\u9662\u533b\u5e08\u7684\u89c2\u5bdf\u8005\u95f4\u4e00\u81f4\u6027\u4e00\u822c\u3002\u7136\u540e\u4ed6\u4eec\u5c06CT\u5206\u7c7b\u7684\u7ed3\u679c\u4e0e\u60a3\u8005\u7684COVID\u7684RT-PCR \u6d4b\u8bd5\u7ed3\u679c\u8fdb\u884c\u6bd4\u8f83\u300245\u4f8bRT-PCR \u6d4b\u8bd5\u9633\u6027\u7684\u60a3\u8005\u7684CT\u5206\u7c7b\u4e3a\u201c\u5178\u578b\u201d\u7684\u9633\u6027\u7387\u4e3a 76.9-96.6\uff05\uff0c\u201c\u4e0d\u786e\u5b9a\u201d\u7684\u9633\u6027\u7387\u4e3a 51.2-64.1\uff05\uff0c\u201c\u975e\u5178\u578b\u201d\u7684\u9633\u6027\u7387\u4e3a2.8-5.3\uff05\uff0c\u201c\u9634\u6027\u201d\u7684\u9633\u6027\u7387\u4e3a20-25\uff05\u3002\u201c\u4e0d\u5178\u578b\u201d\u7684\u9633\u6027\u7387\u4f4e\u662f\u7531\u4e8e\u5408\u5e76\u5176\u4ed6\u80ba\u90e8\u75be\u75c5\u3002\u201c\u9634\u6027\u201d\u4e2d\u9633\u6027\u7387\u8f83\u9ad8\u503c\u5f97\u601d\u8003\u3002\u4f5c\u8005\u63a8\u6d4b\u7531\u4e8e\u5f71\u50cf\u5b66\u8868\u73b0\u4f1a\u968f\u65f6\u95f4\u53d8\u5316\uff0c\u8be5\u7ec4\u4e2d\u53ef\u80fd\u6709\u60a3\u8005\u5c1a\u672a\u8868\u73b0\u51fa\u4e0eCOVID-19\u611f\u67d3\u76f8\u5173\u7684\u5f71\u50cf\u5b66\u8868\u73b0\u3002\u4f5c\u8005\u4e5f\u4f7f\u7528CO-RADs\u5206\u7c7b\u7cfb\u7edf\uff0c\u5bf9\u4e8e\u4e0d\u53ef\u5ffd\u89c6\u7684CO-RADS 1\u7c7b\u7684\u9633\u6027\u7387\uff0c\u89c2\u5bdf\u8005\u95f4\u4e00\u81f4\u6027\u76f8\u4f3c\u3002\u672c\u7814\u7a76\u7ed3\u679c\u4e0e\u4e34\u5e8a\u5b9e\u8df5\u8f83\u4e3a\u76f8\u4f3c\uff0cCOVID\u60a3\u8005\u5728\u75be\u75c5\u75c5\u7a0b\u7684\u4e0d\u540c\u65f6\u95f4\u6bb5\u51fa\u73b0\u4e34\u5e8a\u75c7\u72b6\uff0c\u56e0\u6b64CT\u5e94\u7528\u4e8e\u51a0\u72b6\u75c5\u6bd2\u7684\u7b5b\u67e5\u4f5c\u7528\u6709\u9650\u3002 \u7531\u4e8e\u4e25\u91cd\u7684COVID-19\u611f\u67d3\u4e0e\u75be\u75c5\u7684\u9ad8\u53d1\u75c5\u7387\u548c\u9ad8\u6b7b\u4ea1\u7387\u76f8\u5173\uff0c CT\u5bf9COVID-19\u60a3\u8005\u7684\u6cbb\u7597\u4e2d\u6709\u4ef7\u503c\u3002<\/p>\n<p>&nbsp;<\/p>\n<h3><span style=\"color: #800080;\"><strong>Cardiac<\/strong><\/span><\/h3>\n<h3><span style=\"color: #800080;\"><strong>\u5fc3\u810f<\/strong><\/span><\/h3>\n<p>&nbsp;<\/p>\n<h5><strong><span style=\"color: #3366ff;\">Feasibility of Cardiovascular Four-dimensional Flow MRI during Exercise in Healthy Participants.<\/span><\/strong><\/h5>\n<h5><strong><span style=\"color: #3366ff;\">\u5065\u5eb7\u5fd7\u613f\u8005\u8fd0\u52a8\u8fc7\u7a0b\u4e2d\u884c\u5fc3\u8840\u7ba14D Flow MRI\u7684\u53ef\u884c\u6027\u5206\u6790<\/span><\/strong><\/h5>\n<p>MacDonald, J.A., et al. (2020). \u201cFeasibility of Cardiovascular Four-dimensional Flow MRI during Exercise in Healthy Participants.\u201d Radiology: Cardiothoracic Imaging 2(3):e190033<\/p>\n<p>Keywords: Cardiac MRI, 4D-Flow<\/p>\n<p>\u5173\u952e\u8bcd\uff1a\u5fc3\u810fMRI\uff0c4D-Flow<\/p>\n<p><a href=\"https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020190033\">https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020190033<\/a><\/p>\n<p>Macdonald\u7b49\u4eba\u5206\u6790\u8bc4\u4f30\u4e86\u5728\u8fd0\u52a8\u8fc7\u7a0b\u4e2d\u81ea\u7531\u547c\u5438\u76844D flow MRI\u7684\u4ef7\u503c\u300210\u540d\u5065\u5eb7\u5fd7\u613f\u8005\u5728\u9759\u606f\u53ca\u8fd0\u52a8\u65f6\u884c\u5fc3\u810fMRI\u68c0\u67e5\u7eb3\u5165\u672c\u7814\u7a76\uff0c\u6807\u51c6\u7684\u8fd0\u52a8\u65b9\u6848\u662f\u901a\u8fc7\u4e00\u4e2a\u4ef0\u5367\u4f4d\u7684MRI\u517c\u5bb9\u7684\u9636\u68af\u5f0f\u6b65\u8fdb\u8bbe\u5907\u4e0a\u8fdb\u884c\u7684\u3002\u672c\u56de\u987e\u6027\u7814\u7a76\u901a\u8fc7\u4f7f\u7528\u95e8\u63a7\u51cf\u5c11\u547c\u5438\u8fd0\u52a8\u4f2a\u5f71\uff0cEKG\u95e8\u63a7\u51cf\u5c11\u5fc3\u810f\u8fd0\u52a8\u3002\u672c\u7814\u7a76\u4f7f\u75284D flow\u626b\u63cf\u65b9\u6848\u8bc4\u4f30\u9759\u606f\u548c\u538b\u529b\u72b6\u6001\u4e0b\u68c0\u67e5\u4e2d\u5fc3\u810f\u6536\u7f29\u671f\u548c\u8212\u5f20\u671f\u7684\u640f\u52a8\u91cf\uff08SV\uff09\u548c\u5fc3\u5ba4\u52a8\u80fd\uff08KE\uff09\u3002\u4ed6\u4eec\u53d1\u73b0\uff0c\u4e0e\u9759\u606f\u72b6\u6001\u76f8\u6bd4\uff0c\u8fd0\u52a8\u65f6\u5fc3\u8f93\u51fa\u91cf\u589e\u52a0\uff1b\u6709\u8da3\u7684\u662f\uff0c\u5728\u9759\u606f\u65f6\uff0c\u53f3\u5fc3\u5ba4\u52a8\u80fd\uff08KERV\uff09\u663e\u8457\u589e\u52a0\uff0c\u4f46\u5de6\u5fc3\u5ba4\u52a8\u80fd\uff08KELV\uff09\u548cSV\u65e0\u53d8\u5316\u3002 \u65e2\u5f80\u7814\u7a76\u8bc1\u660eSV\u5f88\u5c11\u53d7\u5230\u4ef0\u5367\u8fd0\u52a8\u7684\u5f71\u54cd\u3002\u4e0eKELV\u76f8\u6bd4\uff0c\u6536\u7f29\u671f\u7684KERV\u663e\u8457\u6027\u589e\u9ad8\uff0c\u800c\u8212\u5f20\u671f\u7684KERV\u5dee\u522b\u4e0d\u5927\u3002\u7531\u4e8e\u5de6\u5fc3\u5ba4KE\u53d7\u5230\u8fd0\u52a8\u7684\u5f71\u54cd\u4e0d\u5927\uff0c 4D stress MRI\u7684\u4f5c\u7528\u6709\u9650\u3002 \u4f46\u662fKERV\u53d7\u5230\u8fd0\u52a8\u7684\u5f71\u54cd\uff0c4D flow MRI\u53ef\u4f5c\u4e3a\u8bc4\u4f30\u53f3\u5fc3\u75be\u75c5\u7684\u6709\u4ef7\u503c\u7684\u65b9\u6cd5\u3002\u7531\u4e8e\u5728\u5fc3\u5ba4\u63a9\u853d\u5f81\u8c61\u548cKE\u4f30\u8ba1\u65b9\u9762\u7684\u89c2\u5bdf\u8005\u95f4\u548c\u89c2\u5bdf\u8005\u5185\u7684\u4e00\u81f4\u6027\u6bd4\u8f83\u5dee\uff0c\u5728\u5f53\u524d\u4e34\u5e8a\u5e94\u7528\u4e2d\u4ecd\u5b58\u5728\u660e\u663e\u7684\u5c40\u9650\u6027\u3002<\/p>\n<p>&nbsp;<\/p>\n<h3><span style=\"color: #800080;\"><strong>Machine Learning<\/strong><\/span><\/h3>\n<h3><span style=\"color: #800080;\"><strong>\u673a\u5668\u5b66\u4e60<\/strong><\/span><\/h3>\n<p>&nbsp;<\/p>\n<h5><strong><span style=\"color: #3366ff;\">Value of Machine Learning-based Coronary CT Fractional Flow Reserve Applied to Triple-Rule-Out CT Angiography in Acute Chest Pain.<\/span><\/strong><\/h5>\n<h5><strong><span style=\"color: #3366ff;\">\u57fa\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u51a0\u72b6\u52a8\u8109CT\u7684\u5206\u6570\u8840\u6d41\u50a8\u5907\u5e94\u7528\u4e8e\u6025\u6027\u80f8\u75db\u4e09\u8054\u5f81\u7684CT\u8840\u7ba1\u9020\u5f71\u626b\u63cf\u7684\u4ef7\u503c<\/span><\/strong><\/h5>\n<p>Schoepf, U.J., et al. (2020) \u201cValue of Machine Learning-based Coronary CT Fractional Flow Reserve Applied to Triple-Rule-Out CT Angiography in Acute Chest Pain.\u201d Radiology: Cardiothoracic Imaging 2(3):e190137<\/p>\n<p>Keywords: Machine Learning, Triple Rule Out (TRO), Fractional Flow Reserve (FFR), CT Coronary Angiography<\/p>\n<p>\u5173\u952e\u8bcd\uff1a\u673a\u5668\u5b66\u4e60\uff0c\u6025\u6027\u80f8\u75db\u4e09\u8054\u5f81\uff08TRO\uff09\uff0c\u5206\u6570\u8840\u6d41\u50a8\u5907\uff08FFR\uff09\uff0cCT\u51a0\u72b6\u52a8\u8109\u9020\u5f71<\/p>\n<p><a href=\"https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020190137\">https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020190137<\/a><\/p>\n<p>\u5357\u5361\u7f57\u6765\u7eb3\u5dde\u533b\u79d1\u5927\u5b66\u7684\u653e\u5c04\u5b66\u5bb6\u63a2\u7a76\u4e86\u673a\u5668\u5b66\u4e60\u5728\u6025\u6027\u80f8\u75db\u4e09\u8054\u5f81\u7684CT\u8840\u7ba1\u9020\u5f71\uff08TRO\uff09\u4f30\u7b97\u8840\u6d41\u50a8\u5907\u5206\u6570\u7684\u5e94\u7528\u3002 Schoepf\u7b49\u4eba\u56de\u987e\u5206\u6790\u4e86\u5177\u6709\u4f4e\u81f3\u4e2d\u5ea6\u7684\u5fc3\u810f\u75c5\u7684159\u4f8b\u60a3\u8005\u7684TRO\u7ed3\u679c\u3002\u5e94\u7528\u6df1\u5ea6\u5b66\u4e60\u7b97\u6cd5\u8ba1\u7b97\u5206\u6570\u6d41\u91cf\u50a8\u5907\uff08FFR\uff09\u3002FFR\u5728\u8bc4\u4f30\u4e25\u91cd\u7684\u963b\u585e\u6027\u51a0\u72b6\u52a8\u8109\u75be\u75c5\u663e\u793a\u9ad8\u5ea6\u7684\u8bca\u65ad\u51c6\u786e\u6027\uff0c\u53ef\u8bc4\u4f30\u975e\u5367\u5e8a\u72b6\u6001\u60a3\u8005\u7684\u51a0\u72b6\u52a8\u8109\u75be\u75c5\uff08CAD\uff09\u3002\u8be5\u7814\u7a76\u7ec4\u5c1d\u8bd5\u5728\u7d27\u6025\u60c5\u51b5\u4e0b\u4f7f\u7528\u975e\u5546\u4e1a\u7684\u5b9e\u65f6\u7684FFR\u8ba1\u7b97\u65b9\u6cd5\u786e\u5b9a\u6709\u8840\u6db2\u52a8\u529b\u5b66\u4e0a\u660e\u663e\u7684\u72ed\u7a84\uff0c\u5e76\u4ee5\u51a0\u72b6\u52a8\u8109\u4ecb\u5165\u6216\u4e3b\u8981\u5fc3\u8840\u7ba1\u4e0d\u826f\u4e8b\u4ef6\uff08MACE\uff09\u4e3a\u6807\u51c6\u3002FFR &lt;0.8\u8003\u8651\u4e3a\u8840\u6db2\u52a8\u529b\u5b66\u4e0a\u660e\u663e\u7684\u72ed\u7a84\u3002 \u5728\u884cTRO\u68c0\u67e5\u7684\u60a3\u8005\u4e2d\uff0c\u670955\uff05\u7684\u60a3\u8005\u4e3a\u660e\u663e\u72ed\u7a84\uff08&gt; 50\uff05\uff09\uff0c5\uff05\u4e3a\u4e25\u91cd\u72ed\u7a84\uff08&gt; 70\uff05\uff09\u300252\uff05\u7684\u60a3\u8005\u7684FFR\u7ed3\u679c\u4e0eTRO\u8868\u73b0\u76f8\u4e00\u81f4\uff1a\u5176\u4e2d\uff0c55\uff05\u6709\u51a0\u72b6\u52a8\u8109\u663e\u8457\u72ed\u7a84\u4e14FFR &lt;0.8\uff0c\u800c45\uff05\u7684FFR&gt; 0.8\u4e14TRO\u65e0\u660e\u663e\u72ed\u7a84\u3002\u5728\u75c5\u4f8b\u56de\u987e\u4e2d\uff0c\u4ed6\u4eec\u53d1\u73b027.5%\u7684\u60a3\u8005\u8fdb\u884c\u51a0\u72b6\u52a8\u8109\u91cd\u5efa\u672f\u6216\u53d1\u751f\u4e25\u91cd\u7684MACE\u4e8b\u4ef6\uff0c\u800c\u90a3\u4e9bFFR&lt;0.8\u7684\u60a3\u8005\u8f83\u540c\u9f84\u4eba\u66f4\u6613\u53d1\u751f\u8fd9\u4e24\u7c7b\u4e0d\u826f\u4e8b\u4ef6\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u884cTRO\u68c0\u67e5\u7684\u60a3\u8005\u4e2d\u670967\uff05\u63a5\u53d7\u4e86\u8fdb\u4e00\u6b65\u7684\u68c0\u67e5\uff0c\u8fd9\u662f\u4e00\u79cd\u76f8\u5bf9\u5e38\u89c1\u7684\u73b0\u8c61\u3002\u4f5c\u8005\u8ba4\u4e3a\uff0cFFR &lt;0.8\u53ef\u80fd\u6709\u52a9\u4e8e\u63d0\u793a\u60a3\u8005\u5b58\u5728\u589e\u52a0\u7684\u98ce\u9669\u800c\u4e14\u9700\u8981\u8fdb\u4e00\u6b65\u68c0\u67e5\u3002 \u7136\u800c\uff0c4\u540dFFR&gt; 0.8\u7684\u60a3\u8005\u8fdb\u884c\u4e86\u51a0\u8109\u8840\u7ba1\u91cd\u5efa\u672f\uff0c \u56e0\u6b64\uff0c\u5982\u679c\u5c06FFR\u4f5c\u4e3a\u8fdb\u4e00\u6b65\u51a0\u8109\u91cd\u5efa\u672f\u7684\u4e34\u754c\u503c\uff0c\u90a3\u4e48\u8fd94\u4f8b\u60a3\u8005\u53ef\u80fd\u4f1a\u9519\u8fc7\u4ecb\u5165\u6cbb\u7597\u7684\u673a\u4f1a\u3002\u8fd8\u5e94\u6ce8\u610f\u7684\u662f\uff0c\u670954\uff05\u7684\u60a3\u8005\u540c\u65f6\u63a5\u53d7TRO\u548cSPECT\u68c0\u67e5\uff0c\u670948\uff05\u540c\u65f6\u63a5\u53d7\u4e86TRO\u548c\u8d1f\u8377\u8d85\u58f0\u5fc3\u52a8\u56fe\u68c0\u67e5\uff0c\u8fd9\u4e24\u8005\u7684\u53d1\u73b0\u5e76\u4e0d\u4e00\u81f4\u3002\u5176\u4e2d\uff0c \u5176\u4e2d3\u4f8b\u60a3\u8005\u7684FFR&gt; 0.8\uff0c\u4e14\u8d1f\u8377SPECT \/ CT\u4e3a\u9633\u6027\u3002\u4f46\u662f\uff0c\u8fd9\u9879\u7814\u7a76\u662f\u56de\u987e\u6027\u7684\u7814\u7a76\uff0c\u672a\u4f7f\u7528FFR &lt;0.8\u503c\u4f5c\u4e3a\u6cbb\u7597\u51b3\u5b9a\u7684\u53c2\u8003\u4e34\u754c\u503c\uff0c\u56e0\u6b64\uff0c\u73b0\u5728\u4ecd\u672a\u6e05\u695a\u6700\u7ec8\u6709\u591a\u5c11\u60a3\u8005\u662f\u9634\u6027\u7ed3\u679c\u3002\u5728\u4e2d\u4f4e\u98ce\u9669CAD\u60a3\u8005\u4e2dTRO\u589e\u52a0FFR\u5bf9\u8bc4\u4f30\u6025\u6027\u80f8\u75db\u6709\u4e00\u5b9a\u7684\u8865\u5145\u4ef7\u503c\u3002<\/p>\n<p>&nbsp;<\/p>\n<h5><span style=\"color: #3366ff;\"><strong>CT-based True- and False-Lumen Segmentation in Type B Aortic Dissection Using Machine Learning.<\/strong><\/span><\/h5>\n<h5><span style=\"color: #3366ff;\"><strong>\u57fa\u4e8e\u673a\u5668\u5b66\u4e60\u7684B\u578b\u4e3b\u52a8\u8109\u5939\u5c42\u771f\u5047\u8154\u7684CT\u5206\u5272<\/strong><\/span><\/h5>\n<p>Hahn, L.D., et al. \u201cCT-based True- and False-Lumen Segmentation in Type B Aortic Dissection Using Machine Learning.\u201d Radiology: Cardiothoracic Imaging 2020; 2(3): e190179<\/p>\n<p>Keywords: Aortic Dissection, Machine Learning<\/p>\n<p>\u5173\u952e\u8bcd\uff1a\u4e3b\u52a8\u8109\u5939\u5c42\uff0c\u673a\u5668\u5b66\u4e60<\/p>\n<p><a href=\"https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020190179\">https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020190179<\/a><\/p>\n<p>\u65af\u5766\u798f\u5927\u5b66\u7684\u7814\u7a76\u4eba\u5458\u5229\u7528\u9010\u6b65\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u81ea\u52a8\u751f\u6210\u5355\u7eaf\u7684B\u578b\u4e3b\u52a8\u8109\u5939\u5c42\u7684\u591a\u5e73\u9762\u91cd\u5efa\uff08MPR\uff09\u56fe\u50cf\uff0c\u5e76\u5206\u5272\u771f\u8154\uff08TL\uff09\u548c\u5047\u8154\uff08FL\uff09\u3002Hahn, L.D. 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