Y
Yong Chen
Researcher at Case Western Reserve University
Publications - 42
Citations - 1300
Yong Chen is an academic researcher from Case Western Reserve University. The author has contributed to research in topics: Imaging phantom & Deep learning. The author has an hindex of 16, co-authored 42 publications receiving 918 citations. Previous affiliations of Yong Chen include Siemens & University of North Carolina at Chapel Hill.
Papers
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Journal ArticleDOI
MR Fingerprinting for Rapid Quantitative Abdominal Imaging
Yong Chen,Yun Jiang,Shivani Pahwa,Dan Ma,Lan Lu,Michael Twieg,Katherine L. Wright,Nicole Seiberlich,Mark A. Griswold,Vikas Gulani +9 more
TL;DR: A rapid technique for quantitative abdominal imaging was developed that allows simultaneous quantification of multiple tissue properties within one 19-second breath hold, with measurements comparable to those in published literature.
Journal ArticleDOI
Slice profile and B1 corrections in 2D magnetic resonance fingerprinting
Dan Ma,Simone Coppo,Yong Chen,Debra McGivney,Yun Jiang,Shivani Pahwa,Vikas Gulani,Mark A. Griswold +7 more
TL;DR: The goal of this study is to characterize and improve the accuracy of 2D magnetic resonance fingerprinting (MRF) scans in the presence of slice profile (SP) and B1 imperfections, which are two main factors that affect quantitative results in MRF.
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Fast 3D magnetic resonance fingerprinting for a whole-brain coverage.
TL;DR: The purpose of this study was to accelerate the acquisition and reconstruction time of 3D magnetic resonance fingerprinting scans.
Journal ArticleDOI
Multiscale reconstruction for MR fingerprinting.
TL;DR: To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting, a new approach is proposed to combine X-ray diffraction and Evans-Bouchut analysis.
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Erratum to “Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting”
TL;DR: A spatially constrained quantification method that uses the signals at multiple neighboring pixels to better estimate tissue properties at the central pixel is proposed and a unique two-step deep learning model is designed that learns the mapping from the observed signals to the desired properties for tissue quantification.