[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121706-en":3,"doc-seo-121706-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121706,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",7,"Healthcare","SUPERVISED MACHINE LEARNING TECHNIQUE FOR REDUCTION OF RADIATION DOSE IN COMPUTED TOMOGRAPHY IMAGING","Substantial reduction of the radiation dose in computed tomography (CT) imaging is achieved using a machine-learning dose-reduction technique. The disclosed methods include enhancing low-radiation-dose images, not merely reducing noise, and combining the approach with other radiation-dose-reduction strategies such as adaptive exposure and iterative reconstruction. The result supports higher-quality CT outputs while lowering patient exposure, improving image usability for diagnostic workflows.","(54)SUPERVISED MACHINE LEARNINGTECHNIQUE FOR REDUCTION OFRADIATION DOSE IN COMPUTEDTOMOGRAPHY IMAGING  \n(71)Applicant:THE UNIVERSITY OF CHICAGO,Chicago,IL(US)  \n(72)Inventor:Kenji Suzuki,Homewood,IL(US)  \n(73)Assignee:The University of Chicago,Chicago,IL(US)  \n(*)Notice:Subject to any disclaimer,the term of thispatent is extended or adjusted under 35U.S.C.154(b)by 0 days  \n# (21)Appl.No.:\n\n14/423,997  \n# (22)PCT Filed:\n\nAug.30,2013  \n(86)PCT No.:PCT/US2013/057641§371(c)(1),  \n# (2)Date:Feb.25,2015\n\n(87)PCT Pub.No.:WO2014/036473  \nPCT Pub.Date:Mar.6,2014  \n(65)  \nPrior Publication Data  \nUS 2015/0201895A1  \nJul.23,2015  \nRelated U.S.Application Data  \n# (60)Provisional application No.61/695,698,filed on Aug.\n\n31,2012.  \n# (51)Int.Cl.\n\n(2006.01)  \n(2006.01)  \n(2006.01)  \n(Continued)  \n# (52)U.S.Cl.\n\nCPC……………A61B 6/5211 (2013.01);A61B6/032  \nUS 9,332,953 B2  \n# (10)Patent No.:\n\nMay 10,2016  \n# (45)Date of Patent:\n\n(2013.01);A61B 6/488(2013.01);A61B6/5282(2013.01);A61B 6/542(2013.01);A61B 6/545(2013.01);G06K 9/66(2013.01);G06T 3/4046(2013.01);G06T 5/002(2013.01);G06T 5/50(2013.01);G06T 11/008(2013.01);G06T2207/10081(2013.01);G06T2207/30028(2013.01);G06T 2207/30061(2013.01)  \n# (58)Field of Classification Search\n\nCPC………………A61N 2005/1061;A61N 5/1071;A61N 5/10;A61B 6/032;A61B 6/4258;A61B 17/1703;G06T 2207/10116;G06T2207/10081  \nSee application file for complete search history.  \n(56)  \nReferences Cited  \nU.S.PATENT DOCUMENTS  \n6,016,333 A 1/2000 Kalvin  \n6,754,380 B1   6/2004 Suzuki et al.  \n(Continued)  \nOTHER PUBLICATIONS  \nSuzuki et al.,\"Computer-Aided Diagnostic Scheme for DistinctionBetween Benign and Malignant Nodules in Thoracic Low-Dose CTby Use of Massive Training Artificial Neural Network,\"2005,IEEETansactions on Medical Imaging,vol.24,No.9.pp.1138-1150.*  \n(Continued)  \nPrimary Examiner—Nirav G PatelAssistant Examiner—Kenny Cese  \nABSTRACT  \n(57)  \nSubstantial reduction of the radiation dose in computedtomography(CT)imaging is shown using a machine-learningdose-reduction technique.Techniques are provided that(1)enhance low-radiation dosage images,beyond just reducingnoise,and (2)may be combined with other approaches,suchas adaptive exposure techniques and iterative reconstruction,for radiation dose reduction.  \n23 Claims,7 Drawing Sheets  \n(51)Int.Cl.  \nG06T 5/50  \n(2006.01)  \n(2006.01)  \nA61B 6/03  \nG06T 11/00  \n(2006.01)  \n(2006.01)  \nG06K 9/66  \n(56)  \nReferences Cited  \nU.S.PATENT DOCUMENTS  \n6,819,790 B211/2004 Suzuki et al.  \n7,545,965 B26/2009 Suzuki et al.2006/0018524 A₁1/2006 Suzuki et al.2010/0020208 A1*1/2010 Barbu…………………G06K 9/40348/2502010/0067772 A1*  3/2010 Kitamura………   A61B6/482382/132  \n2010/0177943 A₁7/2010 Zhao et al.2011/0268334 A₁11/2011 Ra et al.  \n2013/0051516 A1*2/2013 Yang ……………………A61B6/03378/42013/0116554 A1*  5/2013 Kaiser……………A61K49/0438600/425  \nOTHER PUBLICATIONS  \nGoodband et al.“Artifical Neural Networks in Radiation Therapy,”Intelligent and Adaptive Systems in Medicine,2008,pp.213-257.*International Search Report and Written Opinion,InternationalApplication No.PCT/US2013/057641,mailed Dec.13,2013.Suzuki,Pixel-based machine learning in medical imaging,Int.J.Biomed.Imaging,18pp.(2012).  \n*cited by examiner  \nFigure 1  \nMay 10,2016  \nSheet 2 of 7  \nMay 10,2016  \nSheet 3 of 7  \nOutput HD-like CTimage  \nNon-training ultra-ultra-LDCT  \n(SNR=9.7 dB)  \nimage(e.g0.1 mSv)  \n(SNR=4.2 dB)  \nFigure 3B  \nFigure 3A  \nOriginal ULDCT (0.1 mSv)image (SNRave =3.4 dB)  \nFigure 4A  \nU.S.Patent  \nMay 10,2016  \nSheet 4 of 7  \nCorresponding HD-like CT outputimage of the trained MTANNconverter (SNRave =14.3 dB)  \nUS 9,332,953 B2  \nFigure 4B  \nOriginal ULDCT(0.1 mSv)image(SNRave=3.4 dB)  \nFigure 5A  \nU.S.Patent  \nMay 10,2016  \nSheet 5 of 7  \nCorresponding HD-like CT outputimage of the trained MTANNconverter(SNRave=14.3 dB)  \nUS 9,332,953 B2  \nFigure 5B  \nULDCT (0.1 mSv)image(SNRave=5.4 dB±2.6 dB)  \nFigure 6A  \nU.S.Patent  \nMay 10,2016  \nSheet 6of 7  \nReference HDCT(1.5mSv)image(SNRave=16.9 dB±1.4 dB)  \nUS 9,332,953","cbCaie5gqKJtnjXO","https://ap.wps.com/l/cbCaie5gqKJtnjXO","pdf",801590,1,15,"English","en",105,"# Brief Description of the Drawings\n## FIG.1 training phase and application phase\n# Field of the Invention\n## Converting low-dose CT images to higher-quality images\n# Background\n## CT imaging for cancer detection and challenges\n## Automated CT analysis and limitations","[{\"question\":\"What is the main goal of the technique in this document?\",\"answer\":\"To substantially reduce radiation dose in computed tomography imaging while still producing higher-quality images suitable for diagnostic use.\"},{\"question\":\"How does the invention improve low-dose CT images?\",\"answer\":\"It uses a machine-learning dose-reduction technique that enhances low-radiation-dose images beyond simply reducing noise.\"},{\"question\":\"Can the proposed method be combined with other dose-reduction approaches?\",\"answer\":\"Yes. The document describes combining the machine-learning technique with approaches such as adaptive exposure techniques and iterative reconstruction for radiation dose reduction.\"}]","SUPERVISED MACHINE LEARNING TECHNIQUE FOR REDUCTION OF RADIATION DOSE IN COMPUTED TOMOGRAPHY IMAGING | PDF",1785806378,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"supervised-machine-learning-technique-for-reduction-of-radiation-dose-in-computed-tomography-imaging","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/supervised-machine-learning-technique-for-reduction-of-radiation-dose-in-computed-tomography-imaging/121706/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the technique in this document?","Question",{"text":75,"@type":76},"To substantially reduce radiation dose in computed tomography imaging while still producing higher-quality images suitable for diagnostic use.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the invention improve low-dose CT images?",{"text":80,"@type":76},"It uses a machine-learning dose-reduction technique that enhances low-radiation-dose images beyond simply reducing noise.",{"name":82,"@type":73,"acceptedAnswer":83},"Can the proposed method be combined with other dose-reduction approaches?",{"text":84,"@type":76},"Yes. 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