{"id":50,"date":"2024-01-02T20:05:24","date_gmt":"2024-01-02T18:05:24","guid":{"rendered":"https:\/\/webs.uab.cat\/midalab\/?page_id=50"},"modified":"2025-04-07T12:54:42","modified_gmt":"2025-04-07T10:54:42","slug":"publications","status":"publish","type":"page","link":"https:\/\/webs.uab.cat\/midalab\/publications\/","title":{"rendered":"Featured Publications"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"365\" src=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/04\/MNSFT-2-1024x365.jpg\" alt=\"\" class=\"wp-image-233\" srcset=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/04\/MNSFT-2-1024x365.jpg 1024w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/04\/MNSFT-2-300x107.jpg 300w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/04\/MNSFT-2-768x274.jpg 768w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/04\/MNSFT-2-1536x547.jpg 1536w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/04\/MNSFT-2-2048x730.jpg 2048w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/04\/MNSFT-2-1200x427.jpg 1200w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/04\/MNSFT-2-1980x705.jpg 1980w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><strong><a href=\"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/10.1002\/nbm.70032\">Machine Learning Analysis of Single-Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas<\/a><\/strong><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/doi.org\/10.1002\/nbm.5095\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"588\" src=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5095-toc-0001-m-1024x588.jpg\" alt=\"\" class=\"wp-image-154\" srcset=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5095-toc-0001-m-1024x588.jpg 1024w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5095-toc-0001-m-300x172.jpg 300w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5095-toc-0001-m-768x441.jpg 768w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5095-toc-0001-m-1200x689.jpg 1200w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5095-toc-0001-m.jpg 1500w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><strong>Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous<\/strong><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/www.mdpi.com\/2072-6694\/16\/2\/300\"><img loading=\"lazy\" decoding=\"async\" width=\"856\" height=\"626\" src=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/Pitarch-2.png\" alt=\"\" class=\"wp-image-146\" srcset=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/Pitarch-2.png 856w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/Pitarch-2-300x219.png 300w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/Pitarch-2-768x562.png 768w\" sizes=\"auto, (max-width: 856px) 100vw, 856px\" \/><\/a><figcaption class=\"wp-element-caption\"><strong><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10814384\/\" data-type=\"link\" data-id=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10814384\/\">Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology<\/a><\/strong><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"550\" height=\"235\" data-id=\"96\" src=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/cancers-15-03709-ag-550-2.jpg\" alt=\"\" class=\"wp-image-96\" srcset=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/cancers-15-03709-ag-550-2.jpg 550w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/cancers-15-03709-ag-550-2-300x128.jpg 300w\" sizes=\"auto, (max-width: 550px) 100vw, 550px\" \/><figcaption class=\"wp-element-caption\"><br><br><strong><a rel=\"noreferrer noopener\" href=\"https:\/\/www.mdpi.com\/2072-6694\/15\/14\/3709\" target=\"_blank\">Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study<\/a><\/strong><\/figcaption><\/figure>\n<\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"593\" src=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5020-fig-0006-m-3-1024x593.jpg\" alt=\"\" class=\"wp-image-98\" srcset=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5020-fig-0006-m-3-1024x593.jpg 1024w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5020-fig-0006-m-3-300x174.jpg 300w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5020-fig-0006-m-3-768x445.jpg 768w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5020-fig-0006-m-3-1536x890.jpg 1536w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5020-fig-0006-m-3-2048x1187.jpg 2048w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5020-fig-0006-m-3-1200x695.jpg 1200w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2024\/01\/nbm5020-fig-0006-m-3-1980x1147.jpg 1980w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><a rel=\"noreferrer noopener\" href=\"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/10.1002\/nbm.5020\" target=\"_blank\"><strong>A comparison of non-negative matrix underapproximation methods for the decomposition of magnetic resonance spectroscopy data from human brain tumors<\/strong><\/a><\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"472\" height=\"244\" src=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/01\/nbm4054-toc-0001-m.jpg\" alt=\"\" class=\"wp-image-217\" style=\"width:700px;height:auto\" srcset=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/01\/nbm4054-toc-0001-m.jpg 472w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/01\/nbm4054-toc-0001-m-300x155.jpg 300w\" sizes=\"auto, (max-width: 472px) 100vw, 472px\" \/><\/figure>\n\n\n\n<p><a href=\"https:\/\/portalrecerca.uab.cat\/es\/publications\/cancer-metabolism-in-a-snapshot-mrsi-2\"><strong>Cancer metabolism in a snapshot- MRS(I)<\/strong><\/a><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/webs.uab.cat\/midalab\/publications\/graphical-abstract-figure\/\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"577\" src=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/02\/Graphical-abstract-figure-1024x577.png\" alt=\"\" class=\"wp-image-222\" srcset=\"https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/02\/Graphical-abstract-figure-1024x577.png 1024w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/02\/Graphical-abstract-figure-300x169.png 300w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/02\/Graphical-abstract-figure-768x433.png 768w, https:\/\/webs.uab.cat\/midalab\/wp-content\/uploads\/sites\/322\/2025\/02\/Graphical-abstract-figure.png 1138w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><figcaption class=\"wp-element-caption\"><a href=\"https:\/\/ddd.uab.cat\/search?f=title&amp;p=Extraction%20of%20artefactual%20MRS%20patterns%20from%20a%20large%20database%20using%20non-negative%20matrix%20factorization&amp;sc=1&amp;ln=ca\"><strong>Extraction of artefactual MRS patterns from a large database using non-negative matrix factorization<\/strong><\/a><\/figcaption><\/figure>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cancer metabolism in a snapshot- MRS(I)<\/p>\n","protected":false},"author":2550,"featured_media":0,"parent":0,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"sidebar.php","meta":{"footnotes":""},"class_list":["post-50","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/webs.uab.cat\/midalab\/wp-json\/wp\/v2\/pages\/50","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/webs.uab.cat\/midalab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/webs.uab.cat\/midalab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/webs.uab.cat\/midalab\/wp-json\/wp\/v2\/users\/2550"}],"replies":[{"embeddable":true,"href":"https:\/\/webs.uab.cat\/midalab\/wp-json\/wp\/v2\/comments?post=50"}],"version-history":[{"count":16,"href":"https:\/\/webs.uab.cat\/midalab\/wp-json\/wp\/v2\/pages\/50\/revisions"}],"predecessor-version":[{"id":234,"href":"https:\/\/webs.uab.cat\/midalab\/wp-json\/wp\/v2\/pages\/50\/revisions\/234"}],"wp:attachment":[{"href":"https:\/\/webs.uab.cat\/midalab\/wp-json\/wp\/v2\/media?parent=50"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}