04989nam 22007335 450 991033825260332120201123162449.03-030-05831-X10.1007/978-3-030-05831-9(CKB)4100000008103760(MiAaPQ)EBC5776094(DE-He213)978-3-030-05831-9(PPN)236522523(EXLCZ)99410000000810376020190502d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierComputational Diffusion MRI International MICCAI Workshop, Granada, Spain, September 2018 /edited by Elisenda Bonet-Carne, Francesco Grussu, Lipeng Ning, Farshid Sepehrband, Chantal M. W. Tax1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (403 pages)Mathematics and Visualization,1612-37863-030-05830-1 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.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. .Mathematics and Visualization,1612-3786BiomathematicsNumerical analysisComputer science—MathematicsOptical data processingComputer simulationArtificial intelligenceMathematical and Computational Biologyhttps://scigraph.springernature.com/ontologies/product-market-codes/M31000Numeric Computinghttps://scigraph.springernature.com/ontologies/product-market-codes/I1701XMath Applications in Computer Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/I17044Image Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021Simulation and Modelinghttps://scigraph.springernature.com/ontologies/product-market-codes/I19000Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Biomathematics.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.616.07548616.07548Bonet-Carne Elisendaedthttp://id.loc.gov/vocabulary/relators/edtGrussu Francescoedthttp://id.loc.gov/vocabulary/relators/edtNing Lipengedthttp://id.loc.gov/vocabulary/relators/edtSepehrband Farshidedthttp://id.loc.gov/vocabulary/relators/edtTax Chantal M. Wedthttp://id.loc.gov/vocabulary/relators/edtBOOK9910338252603321Computational diffusion MRI1409948UNINA