{"id":21024,"date":"2025-01-28T10:29:01","date_gmt":"2025-01-28T09:29:01","guid":{"rendered":"https:\/\/sano.science\/?post_type=seminars&#038;p=21024"},"modified":"2025-03-19T11:21:08","modified_gmt":"2025-03-19T10:21:08","slug":"augmenting-mri-scan-data-with-real-time-predictions","status":"publish","type":"seminars","link":"https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/","title":{"rendered":"156. Augmenting MRI scan data with real-time predictions"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-abstract\">Abstract:<\/h2>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Simulations of tumor growth rely on mathematical models describing the tumor evolution and efficient and accurate numerical solvers performing 3D computations, including spatial and temporal discretizations. The finite difference 3D tumor growth solver [1]\u00a0 based on advection\u2013diffusion\u2013reaction equations, can solve one time step on 250 \u00d7 250 \u00d7 250 stencil points using one GPU within 100 sec. The higher-order finite element method solver takes 2122s on CPU or 12 sec on two GPUs to compute a one-time step on 256 \u00d7 256 \u00d7 256 mesh using isogeometric finite element method solver\u00a0 [2].\u00a0 Summing up, efficient tumor simulators require parallel computations. In this talk, we present a novel computational method, the exponential integrators, that replaces the time integration scheme with integral equations that can be solved efficiently on a laptop. It can be applied to large class of PDEs and different spatial discretizations. In our presentation we focus on the glioblastoma brain tumor modeled with the Fisher\u2013Kolmogorov diffusion\u2013reaction model with logistic growth. We present a MATLAB simulator that can compute one time steps on 128 x 128 x 128 finite difference stencil within 7 sec on a laptop with single CPU. The input is the MRI scans of the human head and the initial tumor location. The simulation uses the finite difference formulation in space and the ultra-fast exponential integrators method in time. The output from the code is the input data for ParaView visualization. While there are many brain tumor simulation codes, our method\u2019s novelty lies in its implementation using exponential integrators. We propose a new algorithm for the fast computation of exponential integrators [3]. Regarding execution time on a laptop with Win10, using MATLAB, with 11th Gen Intel(R) Core(TM) i5-11500H, 2.92 GHz, and 32 GB of RAM, the algorithm outperforms the state-of-the-art routines from Al-Mohy and Higham [4]. We also compare our method with an implicit, unconditionally stable Crank\u2013Nicolson time integration scheme based on the finite difference method. We show that our method is two orders of magnitude faster than the Crank\u2013Nicolson method with finite difference discretization in space on a laptop equipped with MATLAB. The brain tumor&#8217;s two-year future prediction using a computational grid and 100-time steps, built over the MRI scans of the human head, takes less than 10 minutes on the laptop.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">[1] A. K\u0142usek, M. \u0141o\u015b, M. Paszy\u0144ski, W. Dzwinel, Efficient model of tumor dynamics simulated in multi-GPU environment, International Journal of High Performance Computer Applications,\u00a0 33\u00a0 (2019), 489-506<br>[2] L. Siwik, M. \u0141o\u015b, A. K\u0142usek, A. Paszy\u0144ska, K. Pingali, W. Dzwinel, M. Paszy\u0144ski, Tuning three-dimensional tumor progression simulations on a cluster of GPGPUs, Journal of Computational and Applied Mathematics,\u00a0 412\u00a0 (2022), Article\u00a0 114308<br>[3] M. Pabisz, J. Munoz-Matute, M. Paszy\u0144ski, Augmenting MRI scan data with real-time predictions of glioblastoma brain tumor evolution using faster exponential time integrators, Journal of Computational Science, 85 (2025), Article 102493<br>[4] A. Al-Mohy, N. Higham, Computing the action of the matrix exponential, with an application to exponential integrators, SIAM Journal of Scientiffic Computing, 33 (2011) 488-511<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-about-the-author\">About the author:<\/h2>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Maciej Paszy\u0144ski<\/strong>, Faculty of Computer Science, AGH University<\/p>\n\n\n\n<p class=\" eplus-wrapper\">Full Professor of Computer Science<br>Ph.D. in Mathematics with Applications to Computer Science\u00a0<\/p>\n\n\n\n<p class=\" eplus-wrapper\">h-index: 23 (Web of Science) 22 (Scopus) 28 (Google Scholar)<\/p>\n\n\n\n<p class=\" eplus-wrapper\">He leads the group&nbsp;<a href=\"http:\/\/a2s.agh.edu.pl\/\" target=\"_blank\" rel=\"noreferrer noopener\">&#8220;Adaptive Algorithms and Systems (A2S)&#8221;<\/a>&nbsp;working in the area of applications of artificial intelligence (AI) and high-performance computing (HPC) in advanced simulations.&nbsp;<\/p>\n\n\n\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/home.agh.edu.pl\/~paszynsk\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">https:\/\/home.agh.edu.pl\/~paszynsk\/<\/a><\/p>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"536\" src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski-1024x536.jpg\" alt=\"\" class=\"wp-image-21617\" srcset=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski-1024x536.jpg 1024w, https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski-300x157.jpg 300w, https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski-768x402.jpg 768w, https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski.jpg 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/sano.science\/terms-and-conditions-of-participation-in-sano-seminars\/\" target=\"_blank\" rel=\"noreferrer noopener\">regulations<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Maciej Paszy\u0144ski, Faculty of Computer Science, AGH University<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-21024","seminars","type-seminars","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>156. Augmenting MRI scan data with real-time predictions - Centre for Computational Personalized Medicine<\/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:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"156. Augmenting MRI scan data with real-time predictions\" \/>\n<meta property=\"og:description\" content=\"Maciej Paszy\u0144ski, Faculty of Computer Science, AGH University\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2025-03-19T10:21:08+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/augmenting-mri-scan-data-with-real-time-predictions\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/seminars\\\/augmenting-mri-scan-data-with-real-time-predictions\\\/\",\"name\":\"156. Augmenting MRI scan data with real-time predictions - Centre for Computational Personalized Medicine\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/augmenting-mri-scan-data-with-real-time-predictions\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/augmenting-mri-scan-data-with-real-time-predictions\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2025\\\/01\\\/Sano_Seminar_Paszynski-1024x536.jpg\",\"datePublished\":\"2025-01-28T09:29:01+00:00\",\"dateModified\":\"2025-03-19T10:21:08+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/augmenting-mri-scan-data-with-real-time-predictions\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/sano.science\\\/seminars\\\/augmenting-mri-scan-data-with-real-time-predictions\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/augmenting-mri-scan-data-with-real-time-predictions\\\/#primaryimage\",\"url\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2025\\\/01\\\/Sano_Seminar_Paszynski.jpg\",\"contentUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2025\\\/01\\\/Sano_Seminar_Paszynski.jpg\",\"width\":1200,\"height\":628},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/augmenting-mri-scan-data-with-real-time-predictions\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/sano.science\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Seminars\",\"item\":\"https:\\\/\\\/sano.science\\\/seminars\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"156. Augmenting MRI scan data with real-time predictions\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/sano.science\\\/#website\",\"url\":\"https:\\\/\\\/sano.science\\\/\",\"name\":\"Centre for Computational Personalized Medicine\",\"description\":\"Sano \u2013 Centre for Computational Medicine\",\"publisher\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/sano.science\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/sano.science\\\/#organization\",\"name\":\"Sano \u2013 Centre for Computational Medicine\",\"alternateName\":\"Sano\",\"url\":\"https:\\\/\\\/sano.science\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/sano.science\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/logo_sano_podstawowe.png\",\"contentUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/logo_sano_podstawowe.png\",\"width\":700,\"height\":265,\"caption\":\"Sano \u2013 Centre for Computational Medicine\"},\"image\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/sano.science\\\/\",\"https:\\\/\\\/x.com\\\/sanoscience\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/sanoscience\\\/\",\"https:\\\/\\\/www.youtube.com\\\/channel\\\/UCDZ_8TcjMWUG2ZcgKKgfpwQ\",\"https:\\\/\\\/bsky.app\\\/profile\\\/sanoscience.bsky.social\"]}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"156. Augmenting MRI scan data with real-time predictions - Centre for Computational Personalized Medicine","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/","og_locale":"en_US","og_type":"article","og_title":"156. Augmenting MRI scan data with real-time predictions","og_description":"Maciej Paszy\u0144ski, Faculty of Computer Science, AGH University","og_url":"https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/","og_site_name":"Centre for Computational Personalized Medicine","article_publisher":"https:\/\/www.facebook.com\/sano.science\/","article_modified_time":"2025-03-19T10:21:08+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_site":"@sanoscience","twitter_misc":{"Est. reading time":"3 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/","url":"https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/","name":"156. Augmenting MRI scan data with real-time predictions - Centre for Computational Personalized Medicine","isPartOf":{"@id":"https:\/\/sano.science\/#website"},"primaryImageOfPage":{"@id":"https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/#primaryimage"},"image":{"@id":"https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/#primaryimage"},"thumbnailUrl":"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski-1024x536.jpg","datePublished":"2025-01-28T09:29:01+00:00","dateModified":"2025-03-19T10:21:08+00:00","breadcrumb":{"@id":"https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/#primaryimage","url":"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski.jpg","contentUrl":"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski.jpg","width":1200,"height":628},{"@type":"BreadcrumbList","@id":"https:\/\/sano.science\/seminars\/augmenting-mri-scan-data-with-real-time-predictions\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/sano.science\/"},{"@type":"ListItem","position":2,"name":"Seminars","item":"https:\/\/sano.science\/seminars\/"},{"@type":"ListItem","position":3,"name":"156. Augmenting MRI scan data with real-time predictions"}]},{"@type":"WebSite","@id":"https:\/\/sano.science\/#website","url":"https:\/\/sano.science\/","name":"Centre for Computational Personalized Medicine","description":"Sano \u2013 Centre for Computational Medicine","publisher":{"@id":"https:\/\/sano.science\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sano.science\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/sano.science\/#organization","name":"Sano \u2013 Centre for Computational Medicine","alternateName":"Sano","url":"https:\/\/sano.science\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/","url":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","contentUrl":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","width":700,"height":265,"caption":"Sano \u2013 Centre for Computational Medicine"},"image":{"@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/sano.science\/","https:\/\/x.com\/sanoscience","https:\/\/www.linkedin.com\/company\/sanoscience\/","https:\/\/www.youtube.com\/channel\/UCDZ_8TcjMWUG2ZcgKKgfpwQ","https:\/\/bsky.app\/profile\/sanoscience.bsky.social"]}]}},"acf":[],"gutenberg_blocks":[{"blockName":"custom-styles","attrs":{"styles":""}},{"blockName":"core\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-If0vbn","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-abstract\">Abstract:<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-abstract\">Abstract:<\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-QL8gg1","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-El2kFf","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Simulations of tumor growth rely on mathematical models describing the tumor evolution and efficient and accurate numerical solvers performing 3D computations, including spatial and temporal discretizations. The finite difference 3D tumor growth solver [1]\u00a0 based on advection\u2013diffusion\u2013reaction equations, can solve one time step on 250 \u00d7 250 \u00d7 250 stencil points using one GPU within 100 sec. The higher-order finite element method solver takes 2122s on CPU or 12 sec on two GPUs to compute a one-time step on 256 \u00d7 256 \u00d7 256 mesh using isogeometric finite element method solver\u00a0 [2].\u00a0 Summing up, efficient tumor simulators require parallel computations. In this talk, we present a novel computational method, the exponential integrators, that replaces the time integration scheme with integral equations that can be solved efficiently on a laptop. It can be applied to large class of PDEs and different spatial discretizations. In our presentation we focus on the glioblastoma brain tumor modeled with the Fisher\u2013Kolmogorov diffusion\u2013reaction model with logistic growth. We present a MATLAB simulator that can compute one time steps on 128 x 128 x 128 finite difference stencil within 7 sec on a laptop with single CPU. The input is the MRI scans of the human head and the initial tumor location. The simulation uses the finite difference formulation in space and the ultra-fast exponential integrators method in time. The output from the code is the input data for ParaView visualization. While there are many brain tumor simulation codes, our method\u2019s novelty lies in its implementation using exponential integrators. We propose a new algorithm for the fast computation of exponential integrators [3]. Regarding execution time on a laptop with Win10, using MATLAB, with 11th Gen Intel(R) Core(TM) i5-11500H, 2.92 GHz, and 32 GB of RAM, the algorithm outperforms the state-of-the-art routines from Al-Mohy and Higham [4]. We also compare our method with an implicit, unconditionally stable Crank\u2013Nicolson time integration scheme based on the finite difference method. We show that our method is two orders of magnitude faster than the Crank\u2013Nicolson method with finite difference discretization in space on a laptop equipped with MATLAB. The brain tumor's two-year future prediction using a computational grid and 100-time steps, built over the MRI scans of the human head, takes less than 10 minutes on the laptop.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Simulations of tumor growth rely on mathematical models describing the tumor evolution and efficient and accurate numerical solvers performing 3D computations, including spatial and temporal discretizations. The finite difference 3D tumor growth solver [1]\u00a0 based on advection\u2013diffusion\u2013reaction equations, can solve one time step on 250 \u00d7 250 \u00d7 250 stencil points using one GPU within 100 sec. The higher-order finite element method solver takes 2122s on CPU or 12 sec on two GPUs to compute a one-time step on 256 \u00d7 256 \u00d7 256 mesh using isogeometric finite element method solver\u00a0 [2].\u00a0 Summing up, efficient tumor simulators require parallel computations. In this talk, we present a novel computational method, the exponential integrators, that replaces the time integration scheme with integral equations that can be solved efficiently on a laptop. It can be applied to large class of PDEs and different spatial discretizations. In our presentation we focus on the glioblastoma brain tumor modeled with the Fisher\u2013Kolmogorov diffusion\u2013reaction model with logistic growth. We present a MATLAB simulator that can compute one time steps on 128 x 128 x 128 finite difference stencil within 7 sec on a laptop with single CPU. The input is the MRI scans of the human head and the initial tumor location. The simulation uses the finite difference formulation in space and the ultra-fast exponential integrators method in time. The output from the code is the input data for ParaView visualization. While there are many brain tumor simulation codes, our method\u2019s novelty lies in its implementation using exponential integrators. We propose a new algorithm for the fast computation of exponential integrators [3]. Regarding execution time on a laptop with Win10, using MATLAB, with 11th Gen Intel(R) Core(TM) i5-11500H, 2.92 GHz, and 32 GB of RAM, the algorithm outperforms the state-of-the-art routines from Al-Mohy and Higham [4]. We also compare our method with an implicit, unconditionally stable Crank\u2013Nicolson time integration scheme based on the finite difference method. We show that our method is two orders of magnitude faster than the Crank\u2013Nicolson method with finite difference discretization in space on a laptop equipped with MATLAB. The brain tumor's two-year future prediction using a computational grid and 100-time steps, built over the MRI scans of the human head, takes less than 10 minutes on the laptop.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-uzbON0","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-lczclV","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">[1] A. K\u0142usek, M. \u0141o\u015b, M. Paszy\u0144ski, W. Dzwinel, Efficient model of tumor dynamics simulated in multi-GPU environment, International Journal of High Performance Computer Applications,\u00a0 33\u00a0 (2019), 489-506<br>[2] L. Siwik, M. \u0141o\u015b, A. K\u0142usek, A. Paszy\u0144ska, K. Pingali, W. Dzwinel, M. Paszy\u0144ski, Tuning three-dimensional tumor progression simulations on a cluster of GPGPUs, Journal of Computational and Applied Mathematics,\u00a0 412\u00a0 (2022), Article\u00a0 114308<br>[3] M. Pabisz, J. Munoz-Matute, M. Paszy\u0144ski, Augmenting MRI scan data with real-time predictions of glioblastoma brain tumor evolution using faster exponential time integrators, Journal of Computational Science, 85 (2025), Article 102493<br>[4] A. Al-Mohy, N. Higham, Computing the action of the matrix exponential, with an application to exponential integrators, SIAM Journal of Scientiffic Computing, 33 (2011) 488-511<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">[1] A. K\u0142usek, M. \u0141o\u015b, M. Paszy\u0144ski, W. Dzwinel, Efficient model of tumor dynamics simulated in multi-GPU environment, International Journal of High Performance Computer Applications,\u00a0 33\u00a0 (2019), 489-506<br>[2] L. Siwik, M. \u0141o\u015b, A. K\u0142usek, A. Paszy\u0144ska, K. Pingali, W. Dzwinel, M. Paszy\u0144ski, Tuning three-dimensional tumor progression simulations on a cluster of GPGPUs, Journal of Computational and Applied Mathematics,\u00a0 412\u00a0 (2022), Article\u00a0 114308<br>[3] M. Pabisz, J. Munoz-Matute, M. Paszy\u0144ski, Augmenting MRI scan data with real-time predictions of glioblastoma brain tumor evolution using faster exponential time integrators, Journal of Computational Science, 85 (2025), Article 102493<br>[4] A. Al-Mohy, N. Higham, Computing the action of the matrix exponential, with an application to exponential integrators, SIAM Journal of Scientiffic Computing, 33 (2011) 488-511<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-QL8gg1","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/heading","attrs":{"epAnimationGeneratedClass":"edplus_anim-NzMpbx","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-about-the-author\">About the author:<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-about-the-author\">About the author:<\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-QL8gg1","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-lczclV","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Maciej Paszy\u0144ski<\/strong>, Faculty of Computer Science, AGH University<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Maciej Paszy\u0144ski<\/strong>, Faculty of Computer Science, AGH University<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-lczclV","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Full Professor of Computer Science<br>Ph.D. in Mathematics with Applications to Computer Science\u00a0<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Full Professor of Computer Science<br>Ph.D. in Mathematics with Applications to Computer Science\u00a0<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-yieoh9","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">h-index: 23 (Web of Science) 22 (Scopus) 28 (Google Scholar)<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">h-index: 23 (Web of Science) 22 (Scopus) 28 (Google Scholar)<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-lczclV","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">He leads the group&nbsp;<a href=\"http:\/\/a2s.agh.edu.pl\/\" target=\"_blank\" rel=\"noreferrer noopener\">\"Adaptive Algorithms and Systems (A2S)\"<\/a>&nbsp;working in the area of applications of artificial intelligence (AI) and high-performance computing (HPC) in advanced simulations.&nbsp;<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">He leads the group&nbsp;<a href=\"http:\/\/a2s.agh.edu.pl\/\" target=\"_blank\" rel=\"noreferrer noopener\">\"Adaptive Algorithms and Systems (A2S)\"<\/a>&nbsp;working in the area of applications of artificial intelligence (AI) and high-performance computing (HPC) in advanced simulations.&nbsp;<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-4zq593","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/home.agh.edu.pl\/~paszynsk\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">https:\/\/home.agh.edu.pl\/~paszynsk\/<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/home.agh.edu.pl\/~paszynsk\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">https:\/\/home.agh.edu.pl\/~paszynsk\/<\/a><\/p>\n"]},{"blockName":"core\/spacer","attrs":{"epAnimationGeneratedClass":"edplus_anim-QL8gg1","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/image","attrs":{"id":21617,"sizeSlug":"large","linkDestination":"none","epAnimationGeneratedClass":"edplus_anim-MX7Lx2","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski-1024x536.jpg\" alt=\"\" class=\"wp-image-21617\"\/><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/Sano_Seminar_Paszynski-1024x536.jpg\" alt=\"\" class=\"wp-image-21617\"\/><\/figure>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-pPEAfD","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/sano.science\/terms-and-conditions-of-participation-in-sano-seminars\/\" target=\"_blank\" rel=\"noreferrer noopener\">regulations<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/sano.science\/terms-and-conditions-of-participation-in-sano-seminars\/\" target=\"_blank\" rel=\"noreferrer noopener\">regulations<\/a><\/p>\n"]}],"meta_data":{"event_day":"2025-03-24","event_time":"14.00 CET","event_guest":"","has_medias":true,"medias":[{"icon":{"ID":1144,"id":1144,"title":"clock","filename":"clock.svg","filesize":1479,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","link":"https:\/\/sano.science\/seminars\/79-digital-behaviour-change-interventions-dbci-from-design-to-implementation\/clock\/","alt":"clock Sano Seminar","author":"7","description":"","caption":"Sano Seminar clock","name":"clock","status":"inherit","uploaded_to":13471,"date":"2023-06-01 13:24:42","modified":"2024-10-09 16:41:04","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":56,"height":57,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","thumbnail-width":147,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","medium-width":294,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","medium_large-width":768,"medium_large-height":783,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","large-width":1004,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","1536x1536-width":56,"1536x1536-height":57,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","2048x2048-width":56,"2048x2048-height":57}},"title":"24th March 2025, 2:00-3:30 PM (CET)","link":""},{"icon":{"ID":1146,"id":1146,"title":"camera","filename":"camera.svg","filesize":1129,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","link":"https:\/\/sano.science\/seminars\/79-digital-behaviour-change-interventions-dbci-from-design-to-implementation\/camera\/","alt":"camera Sano Seminar","author":"7","description":"","caption":"Sano Seminar camera","name":"camera","status":"inherit","uploaded_to":13471,"date":"2023-06-01 13:25:24","modified":"2024-10-09 16:42:29","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":60,"height":38,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","thumbnail-width":150,"thumbnail-height":95,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","medium-width":300,"medium-height":190,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","medium_large-width":768,"medium_large-height":486,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","large-width":1024,"large-height":648,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","1536x1536-width":60,"1536x1536-height":38,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","2048x2048-width":60,"2048x2048-height":38}},"title":"Join via ZOOM on","link":{"title":"seminar.sano.science","url":"https:\/\/us06web.zoom.us\/j\/81263292238#success","target":"_blank"}}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/21024","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/seminars"}],"version-history":[{"count":25,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/21024\/revisions"}],"predecessor-version":[{"id":22513,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/21024\/revisions\/22513"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=21024"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}