Mohammad H. Kayvanrad

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In addition to coil sensitivity data (parallel imaging), sparsity constraints are often used as an additional lp-penalty for under-sampled MRI reconstruction (compressed sensing). Penalizing the traditional decimated wavelet transform (DWT) coefficients, however, results in visual pseudo-Gibbs artifacts, some of which are attributed to the lack of(More)
Echo hiding methods have good perceptual quality and they are robust to intentional and unintentional modifications. Unfortunately these methods are not quite transparent and are not suitable for steganography applications. Specifically, this point became more obvious after a recent steganalysis investigation where both parameters and the hidden message(More)
Motivated by the well-known Papoulis-Gerchberg algorithm, an iterative thresholding algorithm for recovery of sparse signals from few observations is proposed. The sequence of iterates turns out to be similar to that of the thresholded Landweber iterations, although not the same. The performance of the proposed algorithm is experimentally evaluated and(More)
PURPOSE To determine the efficacy of compressed sensing (CS) reconstructions for specific clinical magnetic resonance neuroimaging applications beyond more conventional acceleration techniques such as parallel imaging (PI) and low-resolution acquisitions. MATERIALS AND METHODS Raw k-space data were acquired from five healthy volunteers on a 3T scanner(More)
This article presents a compressive sensing approach for reducing data acquisition time in cardiac cine magnetic resonance imaging (MRI). In cardiac cine MRI, several images are acquired throughout the cardiac cycle, each of which is reconstructed from the raw data acquired in the Fourier transform domain, traditionally called k-space. In the proposed(More)
One approach to accelerating data acquisition in magnetic resonance imaging is acquisition of partial k-space data and recovery of missing data based on the sparsity of the image in the wavelet transform domain. We hypothesize that the application of stationary wavelet transform (SWT) thresholding as a sparsity-promoting operation results in improved(More)
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