{"product_id":"9783658507404","title":"Deep Learning Innovations in MRI Reconstruction and Analysis Enhancing Image Quality for Robust Image Processing and Clinical Decision Making","description":"\u003ch1\u003eDeep Learning Innovations in MRI Reconstruction and Analysis\u003c\/h1\u003e\u003ch2\u003eEnhancing Image Quality for Robust Image Processing and Clinical Decision Making\u003c\/h2\u003e\u003ch3\u003eSoumick Chatterjee\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Data Science \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\u003cp\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-bidi-theme-font: minor-latin;\"\u003eHigh-resolution magnetic resonance imaging (MRI) is clinically vital but inherently slow. Accelerating acquisition via undersampling introduces artefacts, whereas long scans risk motion blur; traditional solutions, such as compressed sensing, often fail under such heavy corruption. Consequently, this thesis investigates deep learning methods to correct these artefacts. It develops pipelines for the reconstruction of undersampled (Cartesian and radial) and motion-corrupted data, and for super-resolution, whilst exploring the integration of prior knowledge and complex-valued convolutions. Beyond visual diagnostics, the thesis examines the impact of reconstruction on automated image processing. It proposes and evaluates pipelines for classification, segmentation (supervised and weakly\/semi-supervised), anomaly detection, and registration. Validated on brain tumour and vessel tasks, the study demonstrates that the proposed deep learning-based reconstruction effectively supports both clinical inspection and robust automated decision-making systems.\u003c\/span\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\u003cp class=\"MsoNormal\"\u003e\u003cspan lang=\"EN-US\" style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-ansi-language: EN-US;\"\u003e\u003cstrong\u003eDr Soumick Chatterjee\u003c\/strong\u003e is a postdoctoral researcher at Human Technopole in Milan, Italy. He is also a lecturer in AI for medical imaging at Otto von Guericke University Magdeburg, Germany, where he completed his PhD. His primary area of research focuses on machine learning, specifically deep learning, and its applications in medical imaging and genetics.\u003c\/span\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e30 June 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Fachmedien Wiesbaden\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer Vieweg\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783658507404\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003ePaperback \/ softback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e419\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Fachmedien Wiesbaden","offers":[{"title":"Default Title","offer_id":45228885573772,"sku":"9783658507404","price":107.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783658507404.jpg?v=1780603911","url":"https:\/\/lateknightbooks.com\/products\/9783658507404","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}