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A sparse representation is an essential part of compressed sensing (CS). The discrete wavelet transform has been widely used to sparsely represent magnetic resonance images for CS applications. Artifacts usually exist in CS reconstruction when the wavelet transform is used alone. In this work, we investigate improving the image reconstruction quality through redundant translational-invariant sparsifying...
GRAPPA has been widely used as a k-space-based parallel MRI reconstruction technique. It linearly combines the acquired k-space signals to estimate the missing k-space signals where the coefficients are obtained by linear regression using auto-calibration signals. At high acceleration factors, GRAPPA reconstruction can suffer from a high level of noise even with a large number of auto-calibration...
In this paper, we propose a new k-t Iterative Support Detection (k-t ISD) method to improve the CS reconstruction for dynamic cardiac MRI by incorporating additional information on the support of the dynamic image in x-f space. The proposed method uses an iterative procedure for alternating image reconstruction and support detection in x-f space. Experimental results demonstrate that the proposed...
In this paper we consider image reconstruction from multichannel phased array MRI data without prior knowledge of the coil sensitivity functions. A new framework based on multichannel blind deconvolution (MBD) is developed for joint estimation of the image function and the sensitivity functions in k-space. By exploiting the smoothness of the estimated functions in the spatial domain, we develop a...
Compressed Sensing (CS) has recently been applied to dynamic MRI to improve the acquisition speed. Existing methods exploit the information that the dynamic images are sparse in the spatial and temporal-frequency (y-f) domain. In this paper, we propose to use the additional prior information in CS reconstruction that the support of y-f space is partially known from the motion pattern of dynamic MR...
As one widely-used parallel-imaging method, Generalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) technique reconstructs the missing k-space data by a linear combination of the acquired data using a set of weights. These weights are usually derived from auto-calibration signal (ACS) lines that are acquired in parallel to the reduced lines. In this paper, a cross sampling method is...
Ill-conditioning is serious problem in SENSE reconstruction, especially when large acceleration factors are employed. For Cartesian SENSE, Tikhonov regularization and total variation have been commonly used. However, the Tikhonov regularized image usually tends to blur edges and total variation regularization has a blocky effect. In this paper, we propose a new SENSE regularization technique that...
The paper presents a novel approach of pseudo 2D random sampling scheme for application of compressed sensing in Cartesian magnetic resonance imaging (MRI). The proposed scheme is realized by a pulse sequence program which switches the directions of phase encoding and frequency encoding during data acquisition such that both kx and ky directions can be undersampled randomly. The resulting random sampling...
Parallel magnetic resonance imaging (pMRI) cannot achieve its maximum reduction factor due to practical limitations. The combination of pMRI and distributed compressed sensing (DCS) for further acceleration is of great interest. In this paper, we propose a method to combine sensitivity encoding (SENSE), one of the standard methods for pMRI, and M-FOCUSS, an algorithm solving DCS reconstruction problem...
Compressed Sensing (CS), as a new framework for data acquisition and signal recovery, has been applied to accelerate conventional magnetic resonance imaging (MRI) with Fourier encoding. However, Fourier encoding is not universal and weakly spreads out the energy of most natural images. This limits the achievable reduction factors. In this paper, we propose a Toeplitz random encoding method that is...
The least squares quantization table (LSQT) method is proposed to accelerate the direct Fourier transform for reconstructing images from nonuniformly sampled data, similar to the look-up table (LUT) and equal-phase-line (EPL) methods published recently. First, it classifies all the image pixels into several groups using the Lloyd-Max quantization scheme, and stores the representative value of each...
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