Fractional flow reserve score estimation method and device based on graph neural network

The invention provides a fractional flow reserve estimation method and device based on a graph neural network, and the method comprises the steps: obtaining a contrastographic image of a target coronary artery, and determining the prior information of the target coronary artery according to the cont...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: ZHANG HEYE, GAO ZHIFAN, HE YING, ZHANG QI, LIU XIUJIAN, XIE BAIHONG
Format: Patent
Sprache:chi ; eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator ZHANG HEYE
GAO ZHIFAN
HE YING
ZHANG QI
LIU XIUJIAN
XIE BAIHONG
description The invention provides a fractional flow reserve estimation method and device based on a graph neural network, and the method comprises the steps: obtaining a contrastographic image of a target coronary artery, and determining the prior information of the target coronary artery according to the contrastographic image; condition features constrained by the target coronary artery topology are determined according to the prior information; generating a blood pressure prediction value and a blood flow prediction value of the target coronary artery according to the prior information and the condition characteristics; and generating the fractional flow reserve of the target coronary artery according to the blood pressure predicted value and the blood flow predicted value. When the FFR is calculated, only non-invasive imaging data is needed, and invasive examination is avoided; compared with an existing CFD model, the method has the advantages that the deep learning method is combined, the fractional flow reserve ca
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN117838177A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN117838177A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN117838177A3</originalsourceid><addsrcrecordid>eNqNjLEOgjAURbs4GPUfnh_g0DDAaojEycnBjTzbixBKS14r_L418QOcznBOzlY9GmGThuDZUefCSoIIWUDRBAEhpmHir6cJqQ-W2FuyWAYDenKEpaxewnNPHm_JF4-0Bhn3atOxizj8uFPH5nKvryfMoUWc2SCXbX3TuqyKSpflufin-QC9DzqI</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Fractional flow reserve score estimation method and device based on graph neural network</title><source>esp@cenet</source><creator>ZHANG HEYE ; GAO ZHIFAN ; HE YING ; ZHANG QI ; LIU XIUJIAN ; XIE BAIHONG</creator><creatorcontrib>ZHANG HEYE ; GAO ZHIFAN ; HE YING ; ZHANG QI ; LIU XIUJIAN ; XIE BAIHONG</creatorcontrib><description>The invention provides a fractional flow reserve estimation method and device based on a graph neural network, and the method comprises the steps: obtaining a contrastographic image of a target coronary artery, and determining the prior information of the target coronary artery according to the contrastographic image; condition features constrained by the target coronary artery topology are determined according to the prior information; generating a blood pressure prediction value and a blood flow prediction value of the target coronary artery according to the prior information and the condition characteristics; and generating the fractional flow reserve of the target coronary artery according to the blood pressure predicted value and the blood flow predicted value. When the FFR is calculated, only non-invasive imaging data is needed, and invasive examination is avoided; compared with an existing CFD model, the method has the advantages that the deep learning method is combined, the fractional flow reserve ca</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DIAGNOSIS ; ELECTRIC DIGITAL DATA PROCESSING ; HUMAN NECESSITIES ; HYGIENE ; IDENTIFICATION ; MEDICAL OR VETERINARY SCIENCE ; PHYSICS ; SURGERY</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240409&amp;DB=EPODOC&amp;CC=CN&amp;NR=117838177A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240409&amp;DB=EPODOC&amp;CC=CN&amp;NR=117838177A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHANG HEYE</creatorcontrib><creatorcontrib>GAO ZHIFAN</creatorcontrib><creatorcontrib>HE YING</creatorcontrib><creatorcontrib>ZHANG QI</creatorcontrib><creatorcontrib>LIU XIUJIAN</creatorcontrib><creatorcontrib>XIE BAIHONG</creatorcontrib><title>Fractional flow reserve score estimation method and device based on graph neural network</title><description>The invention provides a fractional flow reserve estimation method and device based on a graph neural network, and the method comprises the steps: obtaining a contrastographic image of a target coronary artery, and determining the prior information of the target coronary artery according to the contrastographic image; condition features constrained by the target coronary artery topology are determined according to the prior information; generating a blood pressure prediction value and a blood flow prediction value of the target coronary artery according to the prior information and the condition characteristics; and generating the fractional flow reserve of the target coronary artery according to the blood pressure predicted value and the blood flow predicted value. When the FFR is calculated, only non-invasive imaging data is needed, and invasive examination is avoided; compared with an existing CFD model, the method has the advantages that the deep learning method is combined, the fractional flow reserve ca</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DIAGNOSIS</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>HUMAN NECESSITIES</subject><subject>HYGIENE</subject><subject>IDENTIFICATION</subject><subject>MEDICAL OR VETERINARY SCIENCE</subject><subject>PHYSICS</subject><subject>SURGERY</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjLEOgjAURbs4GPUfnh_g0DDAaojEycnBjTzbixBKS14r_L418QOcznBOzlY9GmGThuDZUefCSoIIWUDRBAEhpmHir6cJqQ-W2FuyWAYDenKEpaxewnNPHm_JF4-0Bhn3atOxizj8uFPH5nKvryfMoUWc2SCXbX3TuqyKSpflufin-QC9DzqI</recordid><startdate>20240409</startdate><enddate>20240409</enddate><creator>ZHANG HEYE</creator><creator>GAO ZHIFAN</creator><creator>HE YING</creator><creator>ZHANG QI</creator><creator>LIU XIUJIAN</creator><creator>XIE BAIHONG</creator><scope>EVB</scope></search><sort><creationdate>20240409</creationdate><title>Fractional flow reserve score estimation method and device based on graph neural network</title><author>ZHANG HEYE ; GAO ZHIFAN ; HE YING ; ZHANG QI ; LIU XIUJIAN ; XIE BAIHONG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN117838177A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DIAGNOSIS</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>HUMAN NECESSITIES</topic><topic>HYGIENE</topic><topic>IDENTIFICATION</topic><topic>MEDICAL OR VETERINARY SCIENCE</topic><topic>PHYSICS</topic><topic>SURGERY</topic><toplevel>online_resources</toplevel><creatorcontrib>ZHANG HEYE</creatorcontrib><creatorcontrib>GAO ZHIFAN</creatorcontrib><creatorcontrib>HE YING</creatorcontrib><creatorcontrib>ZHANG QI</creatorcontrib><creatorcontrib>LIU XIUJIAN</creatorcontrib><creatorcontrib>XIE BAIHONG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHANG HEYE</au><au>GAO ZHIFAN</au><au>HE YING</au><au>ZHANG QI</au><au>LIU XIUJIAN</au><au>XIE BAIHONG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Fractional flow reserve score estimation method and device based on graph neural network</title><date>2024-04-09</date><risdate>2024</risdate><abstract>The invention provides a fractional flow reserve estimation method and device based on a graph neural network, and the method comprises the steps: obtaining a contrastographic image of a target coronary artery, and determining the prior information of the target coronary artery according to the contrastographic image; condition features constrained by the target coronary artery topology are determined according to the prior information; generating a blood pressure prediction value and a blood flow prediction value of the target coronary artery according to the prior information and the condition characteristics; and generating the fractional flow reserve of the target coronary artery according to the blood pressure predicted value and the blood flow predicted value. When the FFR is calculated, only non-invasive imaging data is needed, and invasive examination is avoided; compared with an existing CFD model, the method has the advantages that the deep learning method is combined, the fractional flow reserve ca</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN117838177A
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DIAGNOSIS
ELECTRIC DIGITAL DATA PROCESSING
HUMAN NECESSITIES
HYGIENE
IDENTIFICATION
MEDICAL OR VETERINARY SCIENCE
PHYSICS
SURGERY
title Fractional flow reserve score estimation method and device based on graph neural network
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T20%3A08%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=ZHANG%20HEYE&rft.date=2024-04-09&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN117838177A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true