An augmented lagrangian based compressed sensing reconstruction for non-cartesian magnetic resonance imaging without gridding and regridding at every iteration

Mehmet Akçakaya, Seunghoon Nam, Tamer A. Basha, Keigo Kawaji, Vahid Tarokh, Reza Nezafat

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Non-Cartesian trajectories are used in a variety of fast imaging applications, due to the incoherent image domain artifacts they create when undersampled. While the gridding technique is commonly utilized for reconstruction, the incoherent artifacts may be further removed using compressed sensing (CS). CS reconstruction is typically done using conjugate-gradient (CG) type algorithms, which require gridding and regridding to be performed at every iteration. This leads to a large computational overhead that hinders its applicability. Methods: We sought to develop an alternative method for CS reconstruction that only requires two gridding and one regridding operation in total, irrespective of the number of iterations. This proposed technique is evaluated on phantom images and whole-heart coronary MRI acquired using 3D radial trajectories, and compared to conventional CS reconstruction using CG algorithms in terms of quantitative vessel sharpness, vessel length, computation time, and convergence rate. Results: Both CS reconstructions result in similar vessel length (P = 0.30) and vessel sharpness (P = 0.62). The per-iteration complexity of the proposed technique is approximately 3-fold lower than the conventional CS reconstruction (17.55 vs. 52.48 seconds in C++). Furthermore, for in-vivo datasets, the convergence rate of the proposed technique is faster (60613 vs. 4556320 iterations) leading to a ~23-fold reduction in reconstruction time. Conclusions:The proposed reconstruction provides images of similar quality to the conventional CS technique in terms of removing artifacts, but at a much lower computational complexity.

Original languageEnglish (US)
Article numbere107107
JournalPloS one
Volume9
Issue number9
DOIs
StatePublished - Sep 2014

Bibliographical note

Funding Information:
The project described was supported by NIH R01EB008743-01A2 and NIH K99HL111410-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2014 Akçakaya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Fingerprint Dive into the research topics of 'An augmented lagrangian based compressed sensing reconstruction for non-cartesian magnetic resonance imaging without gridding and regridding at every iteration'. Together they form a unique fingerprint.

Cite this