Computational Diffusion MRI [[electronic resource] ] : International MICCAI Workshop, Granada, Spain, September 2018 / / edited by Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M. W. Tax

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
• Edizione: 1st ed. 2019.
• Descrizione fisica: 1 online resource (403 pages)
• Disciplina: 616.07548
• Soggetto topico: Biomathematics; Numerical analysis; Computer science—Mathematics; Optical data processing; Computer simulation; Artificial intelligence; Mathematical and Computational Biology; Numeric Computing; Math Applications in Computer Science; Image Processing and Computer Vision; Simulation and Modeling; Artificial Intelligence
• Persona (resp. second.): Bonet-CarneElisenda; GrussuFrancesco; NingLipeng; SepehrbandFarshid; TaxChantal M. W
• Nota di contenuto: Part I Diffusion MRI signal acquisition and processing strategies -- Part II Machine learning for diffusion MRI -- Part III Diffusion MRI signal harmonisation -- Part IV Diffusion MRI outside the brain and clinical applications -- Part V Tractography and connectivity mapping -- Index.
• Sommario/riassunto: This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI’18), which was held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention in Granada, Spain on September 20, 2018. It presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find papers on a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as harmonisation and frontline applications in research and clinical practice. The respective papers constitute invited works from high-profile researchers with a specific focus on three topics that are now gaining momentum within the diffusion MRI community: i) machine learning for diffusion MRI; ii) diffusion MRI outside the brain (e.g. in the placenta); and iii) diffusion MRI for multimodal imaging. The book shares new perspectives on the latest research challenges for those currently working in the field, but also offers a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. It includes rigorous mathematical derivations, a wealth of full-colour visualisations, and clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics alike. .
• Titolo autorizzato: Computational diffusion MRI
• ISBN: 3-030-05831-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910338252603321
• Lo trovi qui: Univ. Federico II
• Serie: Mathematics and Visualization, . 1612-3786