TY - GEN
T1 - Model-Based Iterative Reconstruction for Echo Planar Imaging
T2 - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
AU - Yarach, Uten
AU - Bernstein, Matt A.
AU - Huston, John
AU - In, Myung Ho
AU - Kang, Daehun
AU - Shu, Yunhong
AU - Gray, Erin M.
AU - Meyer, Nolan
AU - Trzasko, Joshua D.
N1 - Funding Information:
This work was supported by NIH U01 EB024450. U Yarach is with Department of Radiology, Mayo Clinic, Rochester, MN, USA and Department of Radiologic Technology, Faculty of Associated Medical Sciences, Chiang Mai University, Chiang Mai, Thailand. JD Trzasko (Trzasko.Joshua@mayo.edu), MA Bernstein, J Huston
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Echo planar imaging is widely-used clinical settings, for its speed. EPI acquisitions typically employ complex sampling protocols, and correspondingly require advanced reconstruction plans both to account for non-uniform data spacing and for managing system imperfections such as B0-field inhomogeneity, gradient non-linearity, and eddy currents, as well as subject-induced non-idealities like susceptibility variation. Such non-idealities are typically managed after the k-space to image domain transformation, which generates geometrical inaccuracies and blurring in images. In this work, we develop and investigate a comprehensive model-based iterative reconstruction (MBIR) framework that prospectively accounts for multiple non-idealities in accelerated single-shot EPI. When necessary, we also employ nonlinear regularization to mitigate noise amplification.
AB - Echo planar imaging is widely-used clinical settings, for its speed. EPI acquisitions typically employ complex sampling protocols, and correspondingly require advanced reconstruction plans both to account for non-uniform data spacing and for managing system imperfections such as B0-field inhomogeneity, gradient non-linearity, and eddy currents, as well as subject-induced non-idealities like susceptibility variation. Such non-idealities are typically managed after the k-space to image domain transformation, which generates geometrical inaccuracies and blurring in images. In this work, we develop and investigate a comprehensive model-based iterative reconstruction (MBIR) framework that prospectively accounts for multiple non-idealities in accelerated single-shot EPI. When necessary, we also employ nonlinear regularization to mitigate noise amplification.
KW - B0 inhomogeneity
KW - and iterative reconstruction
KW - echo planar imaging (EPI)
KW - gradient non-linearity
UR - http://www.scopus.com/inward/record.url?scp=85083310740&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083310740&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF44664.2019.9048792
DO - 10.1109/IEEECONF44664.2019.9048792
M3 - Conference contribution
AN - SCOPUS:85083310740
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 61
EP - 64
BT - Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
A2 - Matthews, Michael B.
PB - IEEE Computer Society
Y2 - 3 November 2019 through 6 November 2019
ER -