Author(s):
1. M. I. Salman:
Electrical Engineering Department, U.E.T. Taxila, Pakistan
2. M. Obaid Ullah:
Electrical Engineering Department, U.E.T. Taxila, Pakistan
3. I. A. Awan:
Computer Engineering Department, Qassim University, Saudi Arabia
Abstract:
State-of-the-art compressed sensing based algorithms recover sparse signals from under sampled incoherent measurements by exploiting their spatial as well as temporal structures. A compressed sensing based dynamic MRI reconstruction algorithm called MASTeR (Motion-Adaptive Spatio-Temporal Regularization) has shown great improvement in spatio-temporal resolution. MASTeR uses motionadaptive linear transformations between neighboring images to model temporal sparsity. In this paper, a computationally efficient MASTeR-based scheme is presented that achieves the same image quality but in less time. The proposed algorithm minimizes a linear combination of three terms (l1-norm, total-variation andleast-square) for initial image reconstruction. Subsequently, least-square and l1-norm with ME/MC i.e., motion estimation and compensation are used to reduce the motion artifacts. The proposed scheme is analyzed for breath-held, steady-state-free-precession MRI scans with prospective cardiac gating.
Page(s):
18-29
DOI:
DOI not available
Published:
Journal: Technical Journal, Volume: 22, Issue: 1, Year: 2017
Keywords:
Least Square Data Fitting
,
Total Variation TV Minimization
,
Composite Problem
,
l1norm regularization
,
Sp a rs e Representation
,
SpatioTemporal Regularization
,
C o m p r e ss e d Se n si n g
References:
References are not available for this document.
Citations
Citations are not available for this document.