A Novel Low-Rank Tensor Method for Undersampling Artifact Removal in Respiratory Motion-Resolved Multi-Echo 3D Cones MRI
Year: 2023
Type: preprint
Abstract: We propose a novel low-rank tensor method for respiratory motion-resolved multi-echo image reconstruction. The key idea is to construct a 3-way image tensor (space $\times$ echo $\times$ motion state) from the conventional gridding reconstruction of highly undersampled multi-echo k-space raw data, and exploit low-rank tensor structure to separate it from undersampling artifacts. Healthy volunteers and patients with iron overload were recruited and imaged on a 3T clinical MRI system for this study. Results show that our proposed method Successfully reduced severe undersampling artifacts in respiratory motion-state resolved complex source images, as well as subsequent R2* and quantitative susceptibility mapping (QSM). Compared to conventional respiratory motion-resolved compressed sensing (CS) image reconstruction, the proposed method had a reconstruction time at least three times faster, accounting for signal evolution along the echo dimension in the multi-echo data.
Source: arXiv (Cornell University)
Authors: Jeong, Seongho, Kang, MungSoo, Behr, Gerald, Jeong, Heechul, Kee, Youngwook
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4367701183