{"id":29395,"date":"2026-02-25T14:10:20","date_gmt":"2026-02-25T13:10:20","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=29395"},"modified":"2026-02-25T14:13:32","modified_gmt":"2026-02-25T13:13:32","slug":"enhanced-ct-image-reconstruction-using-vmd-based-quaternion-bilateral-filtering","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/enhanced-ct-image-reconstruction-using-vmd-based-quaternion-bilateral-filtering\/","title":{"rendered":"Enhanced CT Image Reconstruction Using VMD-Based Quaternion Bilateral Filtering"},"content":{"rendered":"\n<p class=\" eplus-wrapper\">Computed tomography (CT) plays a vital role in medical diagnostics, where image quality hinges on the choice of reconstruction kernels. Sharp kernels boost spatial resolution but amplify noise, whereas soft kernels reduce noise yet blur edges. This study presents a novel Variational Mode Decomposition combined with Quaternion Bilateral Filtering (VMD-QBF) approach to transform sharp-kernel CT images into soft-kernel equivalents without losing key structural details. The method is benchmarked against standard denoising tools\u2014including Non-Local Means, Anisotropic Diffusion, Bilateral Filtering, and Quaternion Bilateral Filtering (QBF)\u2014across multiple kernels (B50, B46, B41, B36, B35, B31). Performance is measured using Mean Squared Error (MSE), Structural Similarity Index (SSIM), Multiscale SSIM (MS-SSIM), and Peak Signal-to-Noise Ratio (PSNR). Results show VMD-QBF outperforming conventional methods, with the lowest MSE, highest PSNR, and superior structural fidelity across all kernels. These outcomes confirm the method\u2019s effectiveness in noise suppression while preserving vital image features, making it a promising option for enhancing post-reconstruction CT images.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-032-09321-9_19?utm_source=researchgate.net&#038;utm_medium=article\" target=\"_self\"  class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tRead the article\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n\n\n\n\n\n<p class=\" eplus-wrapper\"><strong>Authors:<\/strong>\u00a0<a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud Nasr<\/a>, Krzysztof Brzostowski, Adam Pi\u00f3rkowski<\/p>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: Image Quality Metrics, Computed Tomography (CT) Imaging, Reconstruction Kernels, Image Denoising, Variational Mode Decomposition (VMD), Quaternion Bilateral Filtering (QBF), Noise Reduction<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Conference manuscript in Springer Nature Link, 2025<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[15],"class_list":["post-29395","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-neuroscience"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - 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Sharp kernels boost spatial resolution but amplify noise, whereas soft kernels reduce noise yet blur edges. This study presents a novel Variational Mode Decomposition combined with Quaternion Bilateral Filtering (VMD-QBF) approach to transform sharp-kernel CT images into soft-kernel equivalents without losing key structural details. The method is benchmarked against standard denoising tools\u2014including Non-Local Means, Anisotropic Diffusion, Bilateral Filtering, and Quaternion Bilateral Filtering (QBF)\u2014across multiple kernels (B50, B46, B41, B36, B35, B31). Performance is measured using Mean Squared Error (MSE), Structural Similarity Index (SSIM), Multiscale SSIM (MS-SSIM), and Peak Signal-to-Noise Ratio (PSNR). Results show VMD-QBF outperforming conventional methods, with the lowest MSE, highest PSNR, and superior structural fidelity across all kernels. These outcomes confirm the method\u2019s effectiveness in noise suppression while preserving vital image features, making it a promising option for enhancing post-reconstruction CT images.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Computed tomography (CT) plays a vital role in medical diagnostics, where image quality hinges on the choice of reconstruction kernels. Sharp kernels boost spatial resolution but amplify noise, whereas soft kernels reduce noise yet blur edges. This study presents a novel Variational Mode Decomposition combined with Quaternion Bilateral Filtering (VMD-QBF) approach to transform sharp-kernel CT images into soft-kernel equivalents without losing key structural details. The method is benchmarked against standard denoising tools\u2014including Non-Local Means, Anisotropic Diffusion, Bilateral Filtering, and Quaternion Bilateral Filtering (QBF)\u2014across multiple kernels (B50, B46, B41, B36, B35, B31). Performance is measured using Mean Squared Error (MSE), Structural Similarity Index (SSIM), Multiscale SSIM (MS-SSIM), and Peak Signal-to-Noise Ratio (PSNR). Results show VMD-QBF outperforming conventional methods, with the lowest MSE, highest PSNR, and superior structural fidelity across all kernels. These outcomes confirm the method\u2019s effectiveness in noise suppression while preserving vital image features, making it a promising option for enhancing post-reconstruction CT images.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-8ZIZmh","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":"acf\/button","attrs":{"title":"Read the article","button_type":"link","url":"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-032-09321-9_19?utm_source=researchgate.net&utm_medium=article","button_style":"primary","target":"_self","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]},{"blockName":"acf\/button","attrs":{"title":"","button_type":"link","url":"","button_style":"primary","target":"_self","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-GiYwXe","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Authors:<\/strong>\u00a0<a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud Nasr<\/a>, Krzysztof Brzostowski, Adam Pi\u00f3rkowski<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Authors:<\/strong>\u00a0<a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud Nasr<\/a>, Krzysztof Brzostowski, Adam Pi\u00f3rkowski<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Xq2ef2","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: Image Quality Metrics, Computed Tomography (CT) Imaging, Reconstruction Kernels, Image Denoising, Variational Mode Decomposition (VMD), Quaternion Bilateral Filtering (QBF), Noise Reduction<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: Image Quality Metrics, Computed Tomography (CT) Imaging, Reconstruction Kernels, Image Denoising, Variational Mode Decomposition (VMD), Quaternion Bilateral Filtering (QBF), Noise Reduction<\/p>\n"]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/29395","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":8,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/29395\/revisions"}],"predecessor-version":[{"id":29405,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/29395\/revisions\/29405"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=29395"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=29395"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=29395"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}