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Traditional CS with dictionary learning (DL) algorithm can be applied in reconstruction for dynamic cardiac imaging (DCI), which is realized by multi-slice two-dimensional format (2D-DLDCI) or directly three-dimensional format (3D-DLDCI). It was reported that dual-dictionary learning algorithm can improve the reconstruction quality for the 3D magnetic resonance imaging (MRI) by introducing prior information...
Recently, we have developed a tensor-decomposition based compressed sensing (CS) method for dynamic magnetic resonance imaging (dMRI) [1]. The proposed CS-dMRI method exploits the sparsity of the multi-dimensional MRI signal using Higher-order singular value decomposition (HOSVD). Our preliminary study indicates that, compared with conventional approaches, the proposed CS method offers further acceleration...
Motion prediction algorithms are often used in dynamic magnetic resonance imaging to improve the compressed sensing based reconstruction. Previously, the difference calculation (DC) between the current frame (to be reconstructed) and the estimated frame was used as sparse residual signals. In order to obtain sparser signal, an improved Motion Estimation (ME) and Motion Compensation (MC) method was...
The radar target's reconstructed high-resolution images provide important features for target identification. Normally, the radar images are obtained with the cascaded steps of translational motion compensation (TMC), image reconstruction and rotation velocity (RV) retrieval for inverse synthetic aperture radar (ISAR). It is found in this paper that the high-resolution target's image can be obtained...
Traditional video coding method is able to achieve wonderful performance in data compressing. However, it has high complexity, which is not suitable for some environments where low complexity coding is needed. A new method of video coding which is based on compressive sensing is proposed. In this system, the sparsity of residual of successive frames is exploited, which is a crucial requirement in...
In this study, an electromagnetics-based inverse sensitivity mapping method is introduced for applications in high field MRI. Instead of using simplistic numerical approximations, the sensitivity of the radio-frequency coil was determined through a field approach by using iterative optimization. The current study is an extension to previous studies on the inverse method, which has restricted itself...
Magnetic Resonance Imaging (MRI) is an essential medical imaging tool limited by the data acquisition speed. Compressed Sensing is a newly proposed technique applied in MRI for fast imaging with the prior knowledge that the signals are sparse in a special mathematic basis (called the ‘sparsity’ basis). During the exploitation of the sparsity in MR images, there are two kinds of ‘sparsifying’ transforms:...
Derived from the individual surface coil images, a new method based on anisotropic diffusion for estimating the coil sensitivity is proposed in this paper. When the coil sensitivity maps estimated by this method are applied to reconstruct the magnetic resonance image from the under-sampled images by parallel imaging reconstruction Sensitivity Encoding method, the quality of the reconstructed image...
Directly from the surface coil images, a new adaptive smoothing method for estimating the surface coil sensitivity is proposed in this paper. When the coil sensitivity maps estimated by this method are applied to reconstruct the full Field-Of-View image from the under-sampled images, the quality of the reconstructed image in parallel magnetic resonance imaging is improved. As a result, without using...
Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinear equation system for better image quality and reconstruction speed. However, there are no explicit criteria for an optimal CS algorithm selection in the practical MRI application. A...
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