[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117592-en":3,"doc-seo-117592-105":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},117592,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning models for PET myocardial perfusion imaging - Healthcare research overview","Rapid progress in machine learning and artificial intelligence is transforming medical imaging, including positron emission tomography (PET) for myocardial perfusion. The content describes how PET myocardial perfusion interpretation integrates diverse image-derived information, with myocardial blood-flow quantification adding value for diagnosis and risk stratification. It highlights model types spanning tabular feature approaches and image-based deep learning, including potential automation of rest and stress assessment to reduce reporting time and improve accuracy.","Machine learning models for PET myocardial perfusion imaging\nMichelle C Williams, MBChB PhD\nBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh\nCorresponding author\nDr Michelle C Williams, michelle.williams@ed.ac.uk\nUniversity/BHF Centre for Cardiovascular Science, Chancellor’s Building, 49 Little France Crescent, Edinburgh. UK. EH16SUF.\nTel: 07939511864   \t\tFax: 01312426379 \t\tTwitter: @imagingmedsci\nDisclosures\nMCW has given talks for Canon Medical Systems and Siemens Healthineers. MCW (FS/ICRF/20/26002) is supported by the British Heart Foundation.\u000f\nThere has been rapid advancement in the research and clinical use of machine learning in medical imaging. This includes machine learning models capable of combining tabular data extracted from images or reports, and deep learning methods which directly use the images themselves. Machine learning, sometimes also called artificial intelligence, refers to the use of computers to perform tasks without them being explicitly programmed. Machine learning models have demonstrated potential to improve care at every step of the patient pathway. For myocardial perfusion imaging this includes machine learning models to improve image acquisition, image quality, image interpretation, data syntheses and report generation \u0013 ADDIN EN.CITE \u003CEndNote>\u003CCite>\u003CAuthor>Slart\u003C/Author>\u003CYear>2021\u003C/Year>\u003CRecNum>1399\u003C/RecNum>\u003CDisplayText>(1)\u003C/DisplayText>\u003Crecord>\u003Crec-number>1399\u003C/rec-number>\u003Cforeign-keys>\u003Ckey app=\"EN\" db-id=\"rt9zp99p0wwdtretz0k5xtd65eeeet9zvxxf\" timestamp=\"1703670981\" guid=\"af72413f-a9af-43e2-b405-1dab23861734\">1399\u003C/key>\u003C/foreign-keys>\u003Cref-type name=\"Journal Article\">17\u003C/ref-type>\u003Ccontributors>\u003Cauthors>\u003Cauthor>Slart, Riemer H. J. A.\u003C/author>\u003Cauthor>Williams, Michelle C.\u003C/author>\u003Cauthor>Juarez-Orozco, Luis Eduardo\u003C/author>\u003Cauthor>Rischpler, Christoph\u003C/author>\u003Cauthor>Dweck, Marc R.\u003C/author>\u003Cauthor>Glaudemans, Andor W. J. M.\u003C/author>\u003Cauthor>Gimelli, Alessia\u003C/author>\u003Cauthor>Georgoulias, Panagiotis\u003C/author>\u003Cauthor>Gheysens, Olivier\u003C/author>\u003Cauthor>Gaemperli, Oliver\u003C/author>\u003Cauthor>Habib, Gilbert\u003C/author>\u003Cauthor>Hustinx, Roland\u003C/author>\u003Cauthor>Cosyns, Bernard\u003C/author>\u003Cauthor>Verberne, Hein J.\u003C/author>\u003Cauthor>Hyafil, Fabien\u003C/author>\u003Cauthor>Erba, Paola A.\u003C/author>\u003Cauthor>Lubberink, Mark\u003C/author>\u003Cauthor>Slomka, Piotr\u003C/author>\u003Cauthor>Išgum, Ivana\u003C/author>\u003Cauthor>Visvikis, Dimitris\u003C/author>\u003Cauthor>Kolossváry, Márton\u003C/author>\u003Cauthor>Saraste, Antti\u003C/author>\u003C/authors>\u003C/contributors>\u003Ctitles>\u003Ctitle>Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT\u003C/title>\u003Csecondary-title>European Journal of Nuclear Medicine and Molecular Imaging\u003C/secondary-title>\u003C/titles>\u003Cperiodical>\u003Cfull-title>European Journal of Nuclear Medicine and Molecular Imaging\u003C/full-title>\u003C/periodical>\u003Cdates>\u003Cyear>2021\u003C/year>\u003Cpub-dates>\u003Cdate>2021/04/17\u003C/date>\u003C/pub-dates>\u003C/dates>\u003Corig-pub>European Journal of Nuclear Medicine and Molecular Imaging\u003C/orig-pub>\u003Cisbn>1619-7089\u003C/isbn>\u003Curls>\u003Crelated-urls>\u003Curl>https://doi.org/10.1007/s00259-021-05341-z \u003C/url>\u003C/related-urls>\u003C/urls>\u003Celectronic-resource-num>10.1007/s00259-021-05341-z\u003C/electronic-resource-num>\u003C/record>\u003C/Cite>\u003C/EndNote>\u0014(1)\u0015. Interpretation of positron emission tomography (PET) myocardial perfusion imaging involves the integration of multiple pieces of information and quantification of myocardial blood flow provides additive value for diagnosis and risk stratification \u0013 ADDIN EN.CITE \u0013 ADDIN EN.CITE.DATA \u0015\u0014(2)\u0015. A machine learning model that could automatically assess rest and stress myocardial perfusion imaging could decrease reporting time for PET myocardial perfusion imaging.\nIn this edition of the journal Berman et al \u0013 ADDIN EN.CITE \u003CEndNote>\u003CCite>\u003CAuthor>Berman\u003C/Author>\u003CYear>2024\u003C/Year>\u003CRecNum>4126\u003C/RecNum>\u003CDisplayText>(3)\u003C/DisplayText>\u003Crecord>\u003Crec-number>4126\u003C/rec-number>\u003Cforeign-keys>\u003Ckey app=\"EN\" db-id=\"rt9zp99p0wwdtretz0k5xtd65eeeet9zvxxf\" timestamp=","cbCait5UdDnS7Dg3","https://ap.wps.com/l/cbCait5UdDnS7Dg3","docx",39420,1,7,"English","en",105,"# Machine learning models in medical imaging\n## PET myocardial perfusion imaging workflow and value\n## Model types: tabular features vs deep learning\n## Potential clinical impact: acquisition, quality, interpretation, synthesis, report generation","[{\"question\":\"What role does myocardial blood-flow quantification play in PET myocardial perfusion imaging?\",\"answer\":\"Quantification of myocardial blood flow adds diagnostic and risk stratification value by complementing integrated information from PET myocardial perfusion interpretation.\"},{\"question\":\"How can machine learning reduce workflow time in PET myocardial perfusion imaging?\",\"answer\":\"A machine learning model that automatically assesses rest and stress myocardial perfusion imaging could decrease reporting time for PET myocardial perfusion imaging.\"},{\"question\":\"What kinds of machine learning methods are discussed for ischemia and scar detection?\",\"answer\":\"The content covers traditional statistical models trained on tabular rest/stress myocardial blood-flow data, as well as machine learning and deep learning models that directly use PET images.\"}]","Machine learning models for PET myocardial perfusion imaging - 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