Artificial Intelligence in Diffusion MRI: Enhanced Cuckoo Search Algorithm with Metaheuristic Components for Extracting the Maxima of the Orientation ... (Studies in Computational Intelligence, 877) - Image 1

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Springer Artificial Intelligence in Diffusion MRI: Enhanced Cuckoo Search Algorithm with Metaheuristic Components for Extracting the Maxima of the Orientation ... (Studies in Computational Intelligence, 877)

Name: Artificial Intelligence in Diffusion MRI: Enhanced Cuckoo Search Algorithm with Metaheuristic Components for Extracting the Maxima of the Orientation ... (Studies in Computational Intelligence, 877)
Brand: Springer
Price: 100.11 GBP
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This book focuses on the use of artificial intelligence to address a specific problem in the brain – the orientation distribution function. It discusses three aspects: (i) Preparing, enhancing and evaluating one of the cuckoo search algorithms (CSA); (ii) Describing the problem: Diffusion-weighted magnetic resonance imaging (DW-MRI) is used for non-invasive investigations of anatomical connectivity in the human brain, while Q-ball imaging (QBI) is a diffusion MRI reconstruction technique based on the orientation distribution function (ODF), which detects the dominant fiber orientations; however, ODF lacks local estimation accuracy along the path. (iii) Evaluating the performance of the CSA versions in solving the ODF problem using synthetic and real-world data. This book appeals to both postgraduates and researchers who are interested in the fields of medicine and computer science. From the Back Cover This book focuses on the use of artificial intelligence to address a specific problem in the brain – the orientation distribution function. It discusses three aspects: (i) Preparing, enhancing and evaluating one of the cuckoo search algorithms (CSA); (ii) Describing the problem: Diffusion-weighted magnetic resonance imaging (DW-MRI) is used for non-invasive investigations of anatomical connectivity in the human brain, while Q-ball imaging (QBI) is a diffusion MRI reconstruction technique based on the orientation distribution function (ODF), which detects the dominant fiber orientations; however, ODF lacks local estimation accuracy along the path. (iii) Evaluating the performance of the CSA versions in solving the ODF problem using synthetic and real-world data. This book appeals to both postgraduates and researchers who are interested in the fields of medicine and computer science.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3030360857
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 29 November 2020
Listed Since: 02 November 2020

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