[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122474-en":3,"doc-seo-122474-105":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},122474,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Quantification of Myocardial Blood Flow by Machine Learning Analysis of Modified Dual Bolus MRI Examination","Contrast-enhanced magnetic resonance imaging (MRI) offers a promising route to estimate myocardial blood flow (MBF), yet accuracy is commonly degraded by imaging artefacts, including dark rim artefacts that obscure relevant features. This study evaluates machine learning models, notably support vector machines (SVM) and random forests (RF), for MBF estimation from tissue impulse response signals in an animal model. Domestic pigs underwent contrast-enhanced first-pass MRI with rest and stress evaluation, while reference MBF was measured by PET. Classification models separated noisy signals, and regression models predicted MBF from impulse responses. SVM and RF outperformed linear regression with improved correlation and lower error.","Annals of Biomedical Engineering (􀀂 2020) [https://doi.org/10.1007/s10439-020-02591-0](https://doi.org/10.1007/s10439-020-02591-0)  \nBIOMEDICAL ENGINEERING SOCIETY  \nOriginal Article  \nQuantification of Myocardial Blood Flow by Machine Learning Analysis of Modified Dual Bolus MRI Examination  \nMINNA HUSSO , 1 ISAAC O. AFARA,2,3 MIKKO J. NISSI,2 ANTTI KUIVANEN,4 PAAVO HALONEN,4 MIIKKA TARKIA,5 JARMO TEUHO,5 VIRVA SAUNAVAARA,5,6 PAULI VAINIO, 1 PETRI SIPOLA, 1 HANNU MANNINEN, 1 SEPPO YL¨A-HERTTUALA,4,7  \nJUHANI KNUUTI,5 and JUHA T¨OYR¨AS 1,2,3  \n1Diagnostic Imaging Center, Kuopio University Hospital, PO Box 100, 70029 KYS Kuopio, Finland; 2Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; 3School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia; 4A.I. Virtanen Institute for Molecule Sciences, University of Eastern Finland, Kuopio, Finland; 5Turku PET Centre, University Hospital and University of Turku, Turku, Finland; 6Department of Medical Physics, Turku University Hospital, Turku, Finland; and 7Heart Center and Gene Therapy Unit, Kuopio University Hospital,  \nKuopio, Finland  \n(Received 31 March 2020; accepted 11 August 2020)  \nAssociate Editor Umberto Morbiducci oversaw the review of this article.  \nAbstract—Contrast-enhanced magnetic resonance imaging (MRI) is a promising method for estimating myocardial blood flow (MBF) . However, it is often affected by noise from imaging artefacts, such as dark rim artefact obscuring relevant features. Machine learning enables extracting important features from such noisy data and is increasingly applied in areas where traditional approaches are limited. In this study, we investigate the capacity of machine learning, particularly support vector machines (SVM) and random forests (RF), for estimating MBF from tissue impulse response signal in an animal model. Domestic pigs (n = 5) were subjected to contrast enhanced first pass MRI (MRIFP) and the impulse response at different regions of the myocardium (n = 24/pig) were evaluated at rest (n = 120) and stress (n = 96) . Reference MBF was then measured using positron emission tomography (PET) . Since the impulse response may include artefacts, classification models based on SVM and RF were developed to discriminate noisy signal. In addition, regression models based on SVM, RFand linear regression (for comparison) were developed for estimating MBF from the impulse response at rest and stress. The classification and regression models were trained on data from 4 pigs (n = 168) and tested on 1 pig (n = 48) . Models based on SVM and RF outperformed linear regression, with higher correlation (R2SVM = 0 . 81, R2RF = 0 .74,  \nR2linear_regression = 0 .60; qSVM = 0 .76, qRF = 0 .76,  \nqlinear_regression = 0.71) and lower error  \n(RMSESVM = 0.67 mL/g/min, RMSERF = 0.77 mL/g/  \nmin, RMSElinear_regression = 0.96 mL/g/min) for predicting  \nAddress correspondence to Minna Husso, Diagnostic Imaging Center, Kuopio University Hospital, PO Box 100, 70029 KYS Kuopio, Finland. Electronic mail: minna.husso@kuh.fi  \nMBF from MRI impulse response signal. Classifier based on SVM was optimal for detecting impulse response signals with artefacts (accuracy = 92%) . Modified dual bolus MRI signal, combined with machine learning, has potential for accurately estimating MBF at rest and stress states, even from signals with dark rim artefacts. This could provide a protocol for reliable and easy estimation of MBF, although further research is needed to clinically validate the approach.  \nKeywords—Magnetic resonance imaging, Myocardial perfusion imaging, Modified dual bolus method, Machine learning, Random forest, Support vector machine.  \nINTRODUCTION  \nMyocardial blood flow (MBF) is an important parameter for diagnosing heart diseases or examining the state of the myocardium. Positron emission tomography (PET), the gold standard in the diagnostics of the myocardial perfusion, uses radioactive water (i.e","cbCaicCbb3ChrtAo","https://ap.wps.com/l/cbCaicCbb3ChrtAo","pdf",1444683,1,10,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does the study address in estimating myocardial blood flow from MRI?\",\"answer\":\"MRI-based MBF estimation can be impaired by noise from imaging artefacts, especially dark rim artefacts that obscure relevant features.\"},{\"question\":\"Which machine learning methods are used for classification and regression?\",\"answer\":\"Support vector machines (SVM) and random forests (RF) are used to build classification models for noisy signal discrimination and regression models for MBF estimation.\"},{\"question\":\"How is reference myocardial blood flow measured in the study?\",\"answer\":\"Reference MBF is measured using positron emission tomography (PET).\"}]","Quantification of Myocardial Blood Flow by Machine Learning Analysis of Modified Dual Bolus MRI Examination | PDF",1785810847,25,{"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},"quantication-of-myocardial-blood-flow-by-machine-learning-analysis-of-modied-dual-bolus-mri-examination","",{"@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/quantication-of-myocardial-blood-flow-by-machine-learning-analysis-of-modied-dual-bolus-mri-examination/122474/",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 problem does the study address in estimating myocardial blood flow from MRI?","Question",{"text":75,"@type":76},"MRI-based MBF estimation can be impaired by noise from imaging artefacts, especially dark rim artefacts that obscure relevant features.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are used for classification and regression?",{"text":80,"@type":76},"Support vector machines (SVM) and random forests (RF) are used to build classification models for noisy signal discrimination and regression models for MBF estimation.",{"name":82,"@type":73,"acceptedAnswer":83},"How is reference myocardial blood flow measured in the study?",{"text":84,"@type":76},"Reference MBF is measured using positron emission tomography (PET).","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]