Open AccessMotion correction of chemical exchange saturation transfer MRI series using robust principal component analysis (RPCA) and PCA
Author(s) -
Chongxue Bie,
Yuhua Liang,
Lihong Zhang,
Yuyan Zhao,
Yanrong Chen,
Xueru Zhang,
Xiaowei He,
Xiaolei Song
Publication year - 2019
Publication title -
quantitative imaging in medicine and surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.766
H-Index - 21
eISSN - 2223-4292
pISSN - 2223-4306
DOI - 10.21037/qims.2019.09.14
Subject(s) - principal component analysis , artificial intelligence , computer science , residual , image registration , computer vision , imaging phantom , pattern recognition (psychology) , robust principal component analysis , image (mathematics) , algorithm , nuclear medicine , medicine
Chemical exchange saturation transfer (CEST) MRI requires the acquisition of multiple saturation-weighted images and can last several minutes. Misalignments among these images, which are often due to the inevitable motion of the subject, will corrupt CEST contrast maps and result in large quantification errors. Therefore, the registration of the CEST series is critical. However, registration is challenging since common intensity-based registration algorithms may fail to differentiate CEST signals from motion artifacts. Herein, we studied how different patterns of motion affect CEST quantification and proposed a cascaded two-step registration scheme by utilizing features extracted from the entire Z-spectral image series instead of direct registration to a single image.