{"id":3053,"date":"2025-02-13T16:37:45","date_gmt":"2025-02-13T16:37:45","guid":{"rendered":"https:\/\/hawkcell.com\/?page_id=3053"},"modified":"2025-02-13T16:45:42","modified_gmt":"2025-02-13T16:45:42","slug":"un-algorithme-de-debruitage-par-apprentissage-profond-base-sur-dicom-veterinaire-peut-ameliorer-la-qualite-subjective-et-objective-des-images-dirm-cerebrale","status":"publish","type":"page","link":"https:\/\/hawkcell.com\/fr\/un-algorithme-de-debruitage-par-apprentissage-profond-base-sur-dicom-veterinaire-peut-ameliorer-la-qualite-subjective-et-objective-des-images-dirm-cerebrale\/","title":{"rendered":"Un algorithme de d\u00e9bruitage par apprentissage profond bas\u00e9 sur DICOM v\u00e9t\u00e9rinaire peut am\u00e9liorer la qualit\u00e9 subjective et objective des images d’IRM c\u00e9r\u00e9brale."},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-landing-page.php","meta":{"_acf_changed":false,"content-type":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"class_list":["post-3053","page","type-page","status-publish","hentry"],"acf":{"home_sections":[{"acf_fc_layout":"hero_section","title":"Un algorithme de d\u00e9bruitage par apprentissage profond bas\u00e9 sur DICOM v\u00e9t\u00e9rinaire peut am\u00e9liorer la qualit\u00e9 subjective et objective des images d'IRM c\u00e9r\u00e9brale.","subtitle":"Publi\u00e9 le 13 f\u00e9vrier 2025","background_image":3050,"add_cta":["yes"],"button_text":"D\u00e9couvrir le papier complet","button_url":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/vru.70015"},{"acf_fc_layout":"single_column_text","title":"R\u00e9sum\u00e9","subtitle":"","content":"Dans cette \u00e9tude analytique transversale de comparaison de m\u00e9thodes, nous avons \u00e9valu\u00e9 les images IRM c\u00e9r\u00e9brales de 30 chiens et chats, avec et sans l'utilisation d'un algorithme de d\u00e9bruitage bas\u00e9 sur le deep learning (DL) et d\u00e9velopp\u00e9 sp\u00e9cifiquement pour les patients v\u00e9t\u00e9rinaires. La comparaison quantitative a \u00e9t\u00e9 r\u00e9alis\u00e9e en mesurant les rapports signal\/bruit (SNR) et contraste\/bruit (CNR) sur les m\u00eames images IRM pond\u00e9r\u00e9es T2 (T2W), T2-FLAIR et Gradient Echo (GRE) chez chaque patient (images natives et apr\u00e8s d\u00e9bruitage) dans des r\u00e9gions d'int\u00e9r\u00eat identiques.\r\n\r\nDes comparaisons qualitatives ont ensuite \u00e9t\u00e9 effectu\u00e9es : trois radiologues v\u00e9t\u00e9rinaires exp\u00e9riment\u00e9s ont \u00e9valu\u00e9 ind\u00e9pendamment chaque s\u00e9rie d'images T2W, T2-FLAIR et GRE de chaque patient. Les images natives et d\u00e9bruit\u00e9es ont \u00e9t\u00e9 \u00e9valu\u00e9es s\u00e9par\u00e9ment, et les observateurs \u00e9taient aveugles quant au type d\u2019images qu\u2019ils analysaient. Pour chaque type d'image (native et d\u00e9bruit\u00e9e) et chaque s\u00e9quence d'impulsion, ils ont attribu\u00e9 une note subjective concernant la granulosit\u00e9, le contraste et la qualit\u00e9 globale.\r\n\r\nPour toutes les s\u00e9ries d'images test\u00e9es (T2W, T2-FLAIR et GRE), les SNR de la substance grise corticale, de la substance blanche sous-corticale, de la substance grise profonde et de la capsule interne \u00e9taient statistiquement significativement plus \u00e9lev\u00e9s sur les images trait\u00e9es avec l'algorithme de d\u00e9bruitage DL que sur les images natives. De m\u00eame, pour tous les types de s\u00e9ries d'images test\u00e9es, les CNR entre la substance grise corticale et la substance blanche, ainsi qu\u2019entre la substance grise profonde et la capsule interne, \u00e9taient significativement plus \u00e9lev\u00e9s sur les images trait\u00e9es par l'algorithme DL que sur les images natives.\r\n\r\nL'analyse qualitative a confirm\u00e9 ces r\u00e9sultats, avec des scores g\u00e9n\u00e9ralement meilleurs en termes de granulosit\u00e9, de contraste et de qualit\u00e9 globale pour les images trait\u00e9es avec l\u2019algorithme de d\u00e9bruitage DL.\r\n\r\nDans cette \u00e9tude, cet algorithme de d\u00e9bruitage bas\u00e9 sur le deep learning et compatible DICOM a permis de r\u00e9duire le bruit sur les images IRM c\u00e9r\u00e9brales de chiens et chats en 1,5T, et la qualit\u00e9 per\u00e7ue des images par les radiologues s'est am\u00e9lior\u00e9e.","button_text":"D\u00e9couvrir le papier complet","button_url":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/vru.70015","button_2_text":"","button_2_url":"","add_bulletpoints":false,"bulletpoints":null,"layout":"single","background":"white"},{"acf_fc_layout":"contact","title":"Essayez-nous maintenant","subtitle":"Planifiez un appel de d\u00e9couverte avec l'un de nos repr\u00e9sentants","contact_form_embed":"[contact-form-7 id=\"900\" title=\"Contact Form FR\"]","background":"grey"}]},"yoast_head":"\nUn algorithme de d\u00e9bruitage par apprentissage profond bas\u00e9 sur DICOM v\u00e9t\u00e9rinaire peut am\u00e9liorer la qualit\u00e9 subjective et objective des images d'IRM c\u00e9r\u00e9brale. - 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