Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions

Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions

Head motion during MRI acquisition presents significant challenges for neuroimaging analyses. In this work, we present a retrospective motion correction framework built on a Fourier domain motion simulation model combined with established 3D convolutional neural network (CNN) architectures. Quantita...

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Bibliographic Details
Main Authors: Ben A Duffy, Lu Zhao, Farshid Sepehrband, Joyce Min, Danny JJ Wang, Yonggang Shi, Arthur W Toga, Hosung Kim
Format: Article
Language:English
Published: Elsevier 2021-04-01
Series:NeuroImage
Subjects:
Motion artifact
T1
Image quality
Cortical surface
Cortical thickness
Parkinson's disease
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811921000331

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http://www.sciencedirect.com/science/article/pii/S1053811921000331

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