{"id":30168,"date":"2026-03-19T13:21:56","date_gmt":"2026-03-19T12:21:56","guid":{"rendered":"https:\/\/sano.science\/?p=30168"},"modified":"2026-03-27T13:27:22","modified_gmt":"2026-03-27T12:27:22","slug":"denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques","status":"publish","type":"post","link":"https:\/\/sano.science\/denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques\/","title":{"rendered":"Denoising of CT and MRI Images Using Decomposition-Based Curvelet Thresholding and Classical Filtering Techniques"},"content":{"rendered":"\n

Noise\u00a0is\u00a0the\u00a0invisible\u00a0enemy of\u00a0medical\u00a0imaging. In a\u00a0new\u00a0paper,\u00a0Mahmoud\u00a0Nasr<\/a> from Sano, together with Krzysztof Brzostowski, Rafa\u0142 Obuchowicz and Adam Pi\u00f3rkowski, proposes a powerful framework to clean CT and MRI scans while preserving diagnostically critical details.\u00a0\u00a0<\/p>\n\n\n\n

Their method combines multiscale decomposition techniques with curvelet-based denoising and classical spatial filters, allowing it to separate noise from real anatomical structures in a highly targeted way. The framework was tested on controlled phantom data, clinical CT images reconstructed with different kernels, and MRI scans acquired with accelerated protocols, reflecting real-world imaging challenges.<\/p>\n\n\n\n

The hybrid variants using MEMD\u2013Curvelet and VMD\u2013Curvelet consistently achieved high structural similarity and signal quality, outperforming standard filters even for sharp-kernel CT images. In MRI, MEMD\u2013Curvelet and BEMD\u2013Curvelet reduced perceptual distortion, improving image naturalness compared to popular Gaussian and median filters.<\/p>\n\n\n\n

Importantly, the authors also benchmarked their approach against deep learning baselines, showing that it can match high-fidelity methods while remaining computationally efficient\u2014an important factor in clinical workflows. This versatility across modalities and acquisition settings makes the framework a promising candidate for integration into therapeutic and diagnostic pipelines where high-quality denoising is essential under constrained imaging conditions.<\/p>\n\n\n\n

See\u00a0how\u00a0smarter\u00a0denoising\u00a0can\u00a0sharpen\u00a0medical\u00a0diagnosis\u00a0 \u2014\u00a0read\u00a0the\u00a0full\u00a0paper:\u00a0https:\/\/www.mdpi.com\/2076-3417\/16\/3\/1335?utm_source=researchgate.net&utm_medium=article<\/a>\u00a0<\/p>\n","protected":false},"excerpt":"New publication by Mahmoud Nasr, Krzysztof Brzostowski, Rafa\u0142 Obuchowicz and Adam Pi\u00f3rkowski","author":8,"featured_media":28690,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"editor_plus_post_options":"{}","editor_plus_copied_stylings":"{}","footnotes":""},"categories":[1],"tags":[],"class_list":["post-30168","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"\nDenoising of CT and MRI Images Using Decomposition-Based Curvelet Thresholding and Classical Filtering Techniques - 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\/denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Denoising of CT and MRI Images Using Decomposition-Based Curvelet Thresholding and Classical Filtering Techniques\" \/>\n<meta property=\"og:description\" content=\"New publication by Mahmoud Nasr, Krzysztof Brzostowski, Rafa\u0142 Obuchowicz and Adam Pi\u00f3rkowski\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques\/\" \/>\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:published_time\" content=\"2026-03-19T12:21:56+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-27T12:27:22+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/02\/sano-publications.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2535\" \/>\n\t<meta property=\"og:image:height\" content=\"1348\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Sano\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Sano\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/sano.science\\\/denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/sano.science\\\/denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques\\\/\"},\"author\":{\"name\":\"Sano\",\"@id\":\"https:\\\/\\\/sano.science\\\/#\\\/schema\\\/person\\\/561b69b48b6a8f7904aed06df5d03c98\"},\"headline\":\"Denoising of CT and MRI Images Using Decomposition-Based Curvelet Thresholding and Classical Filtering Techniques\",\"datePublished\":\"2026-03-19T12:21:56+00:00\",\"dateModified\":\"2026-03-27T12:27:22+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/sano.science\\\/denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques\\\/\"},\"wordCount\":235,\"publisher\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/sano.science\\\/denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/sano.science\\\/wp-content\\\/uploads\\\/2026\\\/02\\\/sano-publications.jpg\",\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques\\\/\",\"name\":\"Denoising of CT and MRI Images Using Decomposition-Based Curvelet Thresholding and Classical Filtering Techniques - 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In a\u00a0new\u00a0paper,\u00a0<a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud\u00a0Nasr<\/a> from Sano, together with Krzysztof Brzostowski, Rafa\u0142 Obuchowicz and Adam Pi\u00f3rkowski, proposes a powerful framework to clean CT and MRI scans while preserving diagnostically critical details.\u00a0\u00a0<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Noise\u00a0is\u00a0the\u00a0invisible\u00a0enemy of\u00a0medical\u00a0imaging. In a\u00a0new\u00a0paper,\u00a0<a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud\u00a0Nasr<\/a> from Sano, together with Krzysztof Brzostowski, Rafa\u0142 Obuchowicz and Adam Pi\u00f3rkowski, proposes a powerful framework to clean CT and MRI scans while preserving diagnostically critical details.\u00a0\u00a0<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-KhBBlD","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Their method combines multiscale decomposition techniques with curvelet-based denoising and classical spatial filters, allowing it to separate noise from real anatomical structures in a highly targeted way. The framework was tested on controlled phantom data, clinical CT images reconstructed with different kernels, and MRI scans acquired with accelerated protocols, reflecting real-world imaging challenges.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Their method combines multiscale decomposition techniques with curvelet-based denoising and classical spatial filters, allowing it to separate noise from real anatomical structures in a highly targeted way. The framework was tested on controlled phantom data, clinical CT images reconstructed with different kernels, and MRI scans acquired with accelerated protocols, reflecting real-world imaging challenges.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-ZvsWVK","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The hybrid variants using MEMD\u2013Curvelet and VMD\u2013Curvelet consistently achieved high structural similarity and signal quality, outperforming standard filters even for sharp-kernel CT images. In MRI, MEMD\u2013Curvelet and BEMD\u2013Curvelet reduced perceptual distortion, improving image naturalness compared to popular Gaussian and median filters.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The hybrid variants using MEMD\u2013Curvelet and VMD\u2013Curvelet consistently achieved high structural similarity and signal quality, outperforming standard filters even for sharp-kernel CT images. In MRI, MEMD\u2013Curvelet and BEMD\u2013Curvelet reduced perceptual distortion, improving image naturalness compared to popular Gaussian and median filters.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-bwSkhr","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Importantly, the authors also benchmarked their approach against deep learning baselines, showing that it can match high-fidelity methods while remaining computationally efficient\u2014an important factor in clinical workflows. This versatility across modalities and acquisition settings makes the framework a promising candidate for integration into therapeutic and diagnostic pipelines where high-quality denoising is essential under constrained imaging conditions.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Importantly, the authors also benchmarked their approach against deep learning baselines, showing that it can match high-fidelity methods while remaining computationally efficient\u2014an important factor in clinical workflows. This versatility across modalities and acquisition settings makes the framework a promising candidate for integration into therapeutic and diagnostic pipelines where high-quality denoising is essential under constrained imaging conditions.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-VoGImr","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">See\u00a0how\u00a0smarter\u00a0denoising\u00a0can\u00a0sharpen\u00a0medical\u00a0diagnosis\u00a0 \u2014\u00a0read\u00a0the\u00a0full\u00a0paper:\u00a0<a href=\"https:\/\/www.mdpi.com\/2076-3417\/16\/3\/1335?utm_source=researchgate.net&utm_medium=article\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.mdpi.com\/2076-3417\/16\/3\/1335?utm_source=researchgate.net&utm_medium=article<\/a>\u00a0<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">See\u00a0how\u00a0smarter\u00a0denoising\u00a0can\u00a0sharpen\u00a0medical\u00a0diagnosis\u00a0 \u2014\u00a0read\u00a0the\u00a0full\u00a0paper:\u00a0<a href=\"https:\/\/www.mdpi.com\/2076-3417\/16\/3\/1335?utm_source=researchgate.net&utm_medium=article\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.mdpi.com\/2076-3417\/16\/3\/1335?utm_source=researchgate.net&utm_medium=article<\/a>\u00a0<\/p>\n"]}],"meta_data":{"has_thumbnail_pattern":false,"share_on_social_media":{"has_social_media":false}},"featured_image":{"url":"https:\/\/sano.science\/wp-content\/uploads\/2026\/02\/sano-publications-1024x545.jpg"},"main_category":{"name":"Uncategorized"},"prev_page":{"slug":"a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma"},"next_page":{"slug":"supporting-innovation-and-the-future-of-health-technologies-in-europe"},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/30168","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/comments?post=30168"}],"version-history":[{"count":6,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/30168\/revisions"}],"predecessor-version":[{"id":30176,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/posts\/30168\/revisions\/30176"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/28690"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=30168"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/categories?post=30168"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/tags?post=30168"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}