Author ORCID Identifier
Document Type
Article
Publication Date
2013
College/Unit
Statler College of Engineering and Mining Resources
Department/Program/Center
Lane Department of Computer Science and Electrical Engineering
Abstract
We attempt to revitalize researchers' interest in algebraic reconstruction techniques (ART) by expanding their capabilities and demonstrating their potential in speeding up the process of MRI acquisition. Using a continuous-to-discrete model, we experimentally study the application of ART into MRI reconstruction which unifies previous nonuniform-fast-Fourier-transform- (NUFFT-) based and gridding-based approaches. Under the framework of ART, we advocate the use of nonlocal regularization techniques which are leveraged from our previous research on modeling photographic images. It is experimentally shown that nonlocal regularization ART (NR-ART) can often outperform their local counterparts in terms of both subjective and objective qualities of reconstructed images. On one real-world k-space data set, we find that nonlocal regularization can achieve satisfactory reconstruction from as few as one-third of samples. We also address an issue related to image reconstruction from real-world k-space data but overlooked in the open literature: the consistency of reconstructed images across different resolutions. A resolution-consistent extension of NR-ART is developed and shown to effectively suppress the artifacts arising from frequency extrapolation. Both source codes and experimental results of this work are made fully reproducible.
Digital Commons Citation
Li, Xin, "Nonlocal Regularized Algebraic Reconstruction Techniques for MRI: An Experimental Study" (2013). Faculty & Staff Scholarship. 2660.
https://researchrepository.wvu.edu/faculty_publications/2660
Source Citation
Li, X. (2013). Nonlocal Regularized Algebraic Reconstruction Techniques for MRI: An Experimental Study. Mathematical Problems in Engineering, 2013, 1–11. https://doi.org/10.1155/2013/192895
Comments
Copyright © 2013 Xin Li. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.