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We recently proposed an accelerated dynamic magnetic resonance imaging (MRI) reconstruction algorithm that exploits the underlying low rank and sparse properties of the data to achieve highly accelerated reconstructions. In this paper, we validate our algorithm in the context of dynamic free breathing cardiac Perfusion MRI on the Physiologically Improved Non Uniform Cardiac Torso Phantom, PINCAT phantom...
We propose a novel spatial spectral model for the reconstruction of magnetic resonance spectroscopic imaging (MRSI) signal. We penalize the compartmentalized spatial total variation norm of the signal to exploit the spatial properties of the metabolite peaks. The spectral signal is modeled as a sparse linear combination of spikes and polynomials to capture the peaks and baseline induced by unsuppressed...
In this paper we reconstruct the 3-D MR spectroscopic data acquired using an echo-planar spectroscopic imaging (EPSI) sequence. We propose to compensate for B0 inhomogeneity artifact by using the B0 field map and T2* decay estimated from a higher resolution, multi-slice proton imaging data scanned with similar shimming to the EPSI one. We employ total variation (TV) regularization to obtain more uniform...
In this paper, we focus on the reconstruction of iDQC MR spectroscopy data. Unlike standard 1-D MR spectroscopy schemes, iDQC acquires 2-D spectral data to decouple the magnetic field inhomogeneity effects from chemical shift information. This method suffers from a few limitations such as low intrinsic SNR, long acquisition time and the presence of a strong residual water peak. The standard IFFT based...
Magnetic resonance spectroscopy imaging (MRSI) is an attractive tool for medical imaging. However, its practical use is often limited by the intrinsic low spatial resolution and long acquisition time. Spectral localization by imaging (SLIM) has been proposed as a non-Fourier reconstruction algorithm that incorporates spatial a priori information about spectroscopically uniform compartments. Unfortunately,...
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