{"id":1386,"date":"2021-05-10T10:31:54","date_gmt":"2021-05-10T08:31:54","guid":{"rendered":"https:\/\/portale.unime.it\/modellopersona\/?p=77"},"modified":"2025-10-13T23:37:53","modified_gmt":"2025-10-13T21:37:53","slug":"notizia-3","status":"publish","type":"post","link":"https:\/\/portale2.unime.it\/ai-healthlab\/notizia-3\/","title":{"rendered":"Riconoscimento degli Artefatti nelle MRI"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"1386\" class=\"elementor elementor-1386\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7fad0cb e-flex e-con-boxed e-con e-parent\" data-id=\"7fad0cb\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5683664a elementor-widget elementor-widget-text-editor\" data-id=\"5683664a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>I grandi dataset di immagini mediche stanno diventando uno standard, ma resta una sfida cruciale: garantire che ogni scansione MRI sia di qualit\u00e0 sufficiente, senza artefatti che possano compromettere le successive analisi o il percorso diagnostico.<\/p>\n<p><br><\/p>\n<p><strong>Motivazioni &amp; Obiettivi<\/strong><\/p>\n<ul>\n<li>Automatizzare il controllo qualit\u00e0 delle immagini MRI, riducendo il lavoro manuale e i bias soggettivi.<\/li>\n<li>Superare la scarsit\u00e0 di dati reali affetti da artefatti attraverso il data augmentation sintetico, abilitato da simulatori basati sulla fisica dell\u2019MRI.<\/li>\n<li>Fornire strumenti rapidi, efficienti e affidabili \u2014 a supporto di pipeline cliniche high-throughput.<\/li>\n<\/ul>\n<p><br><\/p>\n<p><strong>Metodi<\/strong><\/p>\n<ul>\n<li><b>Generatori di artefatti<\/b> ispirati alla fisica della risonanza magnetica (MRI) per simulare errori, distorsioni e rumore su immagini cerebrali.<\/li>\n<li><b>Estrazione di feature astratte e ingegnerizzate<\/b>, capace di descrivere compattamente le immagini e facilitare la classificazione degli artefatti.<\/li>\n<li><b>Selezione automatica delle feature<\/b> specifica per ciascun tipo di artefatto, ottenendo il massimo potere discriminativo per la detection multisito\/multiscanner.<\/li>\n<li><b>Classificatori SVM<\/b> robusti, addestrati su feature selezionate, per identificare automaticamente nove tipologie di artefatto MRI.<\/li>\n<\/ul>\n<p><br><\/p>\n<p><strong>Novit\u00e0 &amp; Contributi<\/strong><\/p>\n<ul>\n<li>Generatori di artefatti fisici per ampliare enormemente i dataset, rendendo meno essenziale la raccolta manuale di casi rari.<\/li>\n<li>Definizione e validazione di un pool ampio di feature per la detection di nove classi di artefatti nell\u2019MRI strutturale.<\/li>\n<li>Modulo di feature selection specifico per artefatto \u2014 ottimizzazione \u201cclasse per classe\u201d.<\/li>\n<\/ul>\n<p><br><\/p>\n<p><strong>Risultati &amp; Validazione<\/strong><\/p>\n<ul>\n<li>Performance valutate su database misti (artefatti sintetici e trial clinico su sclerosi multipla con label esperti): fino a <b>+12,5 punti percentuali<\/b> di incremento in accuratezza, F1, F2, precisione e richiamo rispetto ai metodi convenzionali.<\/li>\n<li>Pipeline computazionalmente leggera: <b>&lt;1 secondo a scansione<\/b>, ideale per implementazione real-time in reparti clinici e grandi biobanche.<\/li>\n<\/ul>\n<p><br><\/p>\n<h3><b>Articoli Scientifici Correlati<\/b><\/h3>\n<ul>\n<li>\nAn efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training<br><a href=\"https:\/\/arxiv.org\/abs\/2206.03359\" target=\"_blank\">arXiv preprint arXiv:2206.03359 (2022)<\/a><br><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/38000256\/\" target=\"_blank\">PubMed<\/a><br><strong>Autori:<\/strong> Daniele Ravi, Frederik Barkhof, Daniel C. Alexander, Lemuel Puglisi, Geoffrey JM Parker, Arman Eshaghi (per ADNI)\n<\/li>\n<\/ul>\n<p><br><\/p>\n<h3><b>Codice Repository<\/b><\/h3>\n<ul>\n<li>\n<b>Automatic Quality Control (artifact generator, SVM, feature selection):<\/b> <a href=\"https:\/\/github.com\/daniravi\/automatic-quality-control\" target=\"_blank\">daniravi\/automatic-quality-control<\/a><br>Principali componenti: <code>src\/qcs\/artefacts\/<\/code> (generatori artefatti), <code>src\/qcs\/feature_extraction.py<\/code> (feature), <code>src\/qcs\/feature_selection.py<\/code>\n<\/li>\n<\/ul>\n<p><br><\/p>\n<h3><b>Team &amp; Autori<\/b><\/h3>\n<div style=\"border:1px solid #ccc;padding:12px\">\n<ul>\n<li><a href=\"https:\/\/github.com\/daniravi\" target=\"_blank\">Daniele Rav\u00ec<\/a> (PI, sviluppo artefact simulator e pipeline SVM)<\/li>\n<li><a href=\"https:\/\/github.com\/LemuelPuglisi\" target=\"_blank\">Lemuel Puglisi<\/a> (Feature engineering, contribuzione codici QSA)<\/li>\n<li>Frederik Barkhof, Daniel C. Alexander, Geoffrey JM Parker, Arman Eshaghi<\/li><\/ul><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>I grandi dataset di immagini mediche stanno diventando uno standard, ma resta una sfida cruciale: garantire che ogni scansione MRI sia di qualit\u00e0 sufficiente, senza artefatti che possano compromettere le successive analisi o il percorso diagnostico. Motivazioni &amp; Obiettivi Automatizzare il controllo qualit\u00e0 delle immagini MRI, riducendo il lavoro manuale e i bias soggettivi. Superare&hellip; <a class=\"more-link\" href=\"https:\/\/portale2.unime.it\/ai-healthlab\/notizia-3\/\">Continua a leggere <span class=\"screen-reader-text\">Riconoscimento degli Artefatti nelle MRI<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":2119,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-1386","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-it","entry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Riconoscimento degli Artefatti nelle MRI - AI-HealthLab<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/portale2.unime.it\/ai-healthlab\/notizia-3\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Riconoscimento degli Artefatti nelle MRI - AI-HealthLab\" \/>\n<meta property=\"og:description\" content=\"I grandi dataset di immagini mediche stanno diventando uno standard, ma resta una sfida cruciale: garantire che ogni scansione MRI sia di qualit\u00e0 sufficiente, senza artefatti che possano compromettere le successive analisi o il percorso diagnostico. Motivazioni &amp; Obiettivi Automatizzare il controllo qualit\u00e0 delle immagini MRI, riducendo il lavoro manuale e i bias soggettivi. Superare&hellip; Continua a leggere Riconoscimento degli Artefatti nelle MRI\" \/>\n<meta property=\"og:url\" content=\"https:\/\/portale2.unime.it\/ai-healthlab\/notizia-3\/\" \/>\n<meta property=\"og:site_name\" content=\"AI-HealthLab\" \/>\n<meta property=\"article:published_time\" content=\"2021-05-10T08:31:54+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-10-13T21:37:53+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/portale2.unime.it\/ai-healthlab\/wp-content\/uploads\/sites\/94\/2025\/10\/3d93c8_270002f4ef5d42f18276965e4caee35dmv2.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1434\" \/>\n\t<meta property=\"og:image:height\" content=\"1056\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"robertam\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Scritto da\" \/>\n\t<meta name=\"twitter:data1\" content=\"robertam\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tempo di lettura stimato\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minuti\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/notizia-3\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/notizia-3\\\/\"},\"author\":{\"name\":\"robertam\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/#\\\/schema\\\/person\\\/52269e7b1f938c4c4f745090dfe43160\"},\"headline\":\"Riconoscimento degli Artefatti nelle MRI\",\"datePublished\":\"2021-05-10T08:31:54+00:00\",\"dateModified\":\"2025-10-13T21:37:53+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/notizia-3\\\/\"},\"wordCount\":355,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/notizia-3\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/wp-content\\\/uploads\\\/sites\\\/94\\\/2025\\\/10\\\/3d93c8_270002f4ef5d42f18276965e4caee35dmv2.jpg\",\"articleSection\":[\"NEWS\"],\"inLanguage\":\"it-IT\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/notizia-3\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/notizia-3\\\/\",\"url\":\"https:\\\/\\\/portale2.unime.it\\\/ai-healthlab\\\/notizia-3\\\/\",\"name\":\"Riconoscimento degli Artefatti nelle MRI - 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