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ABSTRACT: PURPOSE: To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. METHODS: Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. RESULTS: Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1 -SPIRiT reconstructions with the same number of measurements. CONCLUSION: The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. Magn Reson Med, 2013. © 2013 Wiley Periodicals, Inc.
Magnetic Resonance in Medicine 03/2013; · 2.96 Impact Factor
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ABSTRACT: GRAPPA linearly combines the undersampled k-space signals to estimate the missing k-space signals where the coefficients are obtained by fitting to some auto-calibration signals (ACS) sampled with Nyquist rate based on the shift-invariant property. At high acceleration factors, GRAPPA reconstruction can suffer from a high level of noise even with a large number of auto-calibration signals. In this work, we propose a nonlinear method to improve GRAPPA. The method is based on the so-called kernel method which is widely used in machine learning. Specifically, the undersampled k-space signals are mapped through a nonlinear transform to a high-dimensional feature space, and then linearly combined to reconstruct the missing k-space data. The linear combination coefficients are also obtained through fitting to the ACS data but in the new feature space. The procedure is equivalent to adding many virtual channels in reconstruction. A polynomial kernel with explicit mapping functions is investigated in this work. Experimental results using phantom and in vivo data demonstrate that the proposed nonlinear GRAPPA method can significantly improve the reconstruction quality over GRAPPA and its state-of-the-art derivatives.
Magnetic Resonance in Medicine 12/2011; 68(3):730-40. · 2.96 Impact Factor
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ABSTRACT: Compressed sensing (CS) has been used in dynamic cardiac MRI to reduce the data acquisition time. The sparseness of the dynamic image series in the spatial- and temporal-frequency (x-f) domain has been exploited in existing works. In this article, 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 based on the theory of CS with partially known support. The proposed method uses an iterative procedure for alternating between image reconstruction and support detection in x-f space. At each iteration, a truncated ℓ(1) minimization is applied to obtain the reconstructed image in x-f space using the support information from the previous iteration. Subsequently, by thresholding the reconstruction, we update the support information to be used in the next iteration. Experimental results demonstrate that the proposed k-t ISD method improves the reconstruction quality of dynamic cardiac MRI over the basic CS method in which support information is not exploited.
Magnetic Resonance in Medicine 11/2011; 68(1):41-53. · 2.96 Impact Factor
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ABSTRACT: In conventional generalized autocalibrating partially parallel acquisitions, the autocalibration signal (ACS) lines are acquired with a frequency-encoding direction in parallel to other undersampled lines. In this study, a cross sampling method is proposed to acquire the ACS lines orthogonal to the undersampled lines. This cross sampling method increases the amount of calibration data along the direction, where k-space is undersampled, and especially improves the calibration accuracy when a small number of ACS lines are acquired. The cross sampling method is implemented with swapped frequency and phase encoding gradients. In addition, an iterative coregistration method is also developed to correct the inconsistency between the ACS and undersampled data, which are acquired separately in two orthogonal directions. The same calibration and reconstruction procedure as conventional generalized autocalibrating partially parallel acquisitions is then applied to the corrected data to recover the unacquired k-space data and obtain the final image. Reconstruction results from simulations, phantom and in vivo human brain experiments have distinctly demonstrated that the proposed method, named cross-sampled generalized autocalibrating partially parallel acquisitions, can effectively reduce the aliasing artifacts of conventional generalized autocalibrating partially parallel acquisitions when very few ACS lines are acquired, especially at high outer k-space reduction factors.
Magnetic Resonance in Medicine 08/2011; 67(4):1042-53. · 2.96 Impact Factor
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ABSTRACT: 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 signals. In this work, we improve the GRAPPA model using a kernel approach. Specifically, the acquired k-space data are mapped through a nonlinear transform to a high-dimensional space and then linearly combined to estimate the missing k-space data. A polynomial kernel is investigated in this work. Experimental results using phantom and in vivo data demonstrate that the proposed kernel GRAPPA method can significantly improve the reconstruction quality over the existing methods.
Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on; 05/2011
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ABSTRACT: In sensitivity encoding reconstruction, the issue of ill conditioning becomes serious and thus the signal-to-noise ratio becomes poor when a large acceleration factor is employed. Total variation (TV) regularization has been used to address this issue and shown to better preserve sharp edges than Tikhonov regularization but may cause blocky effect. In this article, we study nonlocal TV regularization for noise suppression in sensitivity encoding reconstruction. The nonlocal TV regularization method extends the conventional TV norm to a nonlocal version by introducing a weighted nonlocal gradient function calculated from the weighted difference between the target pixel and its generalized neighbors, where the weights incorporate the prior information of the image structure. The method not only inherits the edge-preserving advantage of TV regularization but also overcomes the blocky effect. The experimental results from in vivo data show that nonlocal TV regularization is superior to the existing competing methods in preserving fine details and reducing noise and artifacts.
Magnetic Resonance in Medicine 05/2011; 65(5):1384-92. · 2.96 Impact Factor
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Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
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Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011
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ABSTRACT: 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 proposed to acquire the ACS lines orthogonal to the reduced lines. This cross sampling method increases the amount of calibration data along the direction that the k-space is undersampled and thus improves the calibration accuracy, especially when a small number of ACS lines are acquired. Both phantom and in vivo experiments demonstrate that the proposed method, named cross-sampled GRAPPA (CS-GRAPPA), can effectively reduce the aliasing artifacts of GRAPPA when high acceleration is desired.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: 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 images. The reconstruction is then formulated as a truncated ℓ<sub>1</sub> minimization problem. Experimental results show that the dynamic image reconstruction quality of the proposed method is superior to that of existing methods when the same number of measurements is used.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: 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 regularization approach in conjunction with MBD to obtain good reconstruction of the image function. Experimental results using simulated and real data demonstrate that the proposed reconstruction algorithm can better removes the sensitivity weighting in the reconstructed images compared to the sum-of-squares (SoS) approach.
Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on; 05/2010
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ABSTRACT: 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 images. The reconstruction is then formulated as a truncated ℓ(1) minimization problem. Experimental results show that the dynamic image reconstruction quality of the proposed method is superior to that of existing methods when the same number of measurements is used.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:2829-32.
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ABSTRACT: 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 proposed to acquire the ACS lines orthogonal to the reduced lines. This cross sampling method increases the amount of calibration data along the direction that the k-space is undersampled and thus improves the calibration accuracy, especially when a small number of ACS lines are acquired. Both phantom and in vivo experiments demonstrate that the proposed method, named cross-sampled GRAPPA (CS-GRAPPA), can effectively reduce the aliasing artifacts of GRAPPA when high acceleration is desired.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:3325-8.
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ABSTRACT: Both parallel MRI and compressed sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data. The combination of parallel MRI and CS for further acceleration is of great interest. In this paper, we propose a novel method to combine sensitivity encoding (SENSE), one of the standard methods for parallel MRI, and compressed sensing for rapid MR imaging (SparseMRI), a recently proposed method for applying CS in MR imaging with Cartesian trajectories. The proposed method, named CS-SENSE, sequentially reconstructs a set of aliased reduced-field-of-view images in each channel using SparseMRI and then reconstructs the final image from the aliased images using Cartesian SENSE. The results from simulations and phantom and in vivo experiments demonstrate that CS-SENSE can achieve a reduction factor higher than those achieved by SparseMRI and SENSE individually and outperform the existing method that combines parallel MRI and CS.
Magnetic Resonance in Medicine 09/2009; 62(6):1574-84. · 2.96 Impact Factor
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I. J. Functional Informatics and Personalised Medicine. 01/2009; 2:394-406.
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ABSTRACT: 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 k(x) and k(y) directions can be undersampled randomly. The resulting random sampling pattern approximates the ideal but impractical 2D patterns. Both the simulation and experiment results show the proposed method is superior to the existing 1D random sampling and similar to the ideal 2D random sampling in terms of the reconstruction quality. This method can potentially improve the MR imaging speed through the application of compressed sensing in conventional MRI.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:2672-5.
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ABSTRACT: 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 is based on nonlocal total variation with Bregman iteration. It penalizes highly oscillatory noise and allows sharp edges and fine textures in reconstruction. The method is shown to be able to significantly reduce the artifacts in SENSE reconstruction.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:1032-5.
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ABSTRACT: 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 universal and spreads out the image energy more evenly. The MR physical feasibility of the proposed encoding method is verified by Bloch simulation, and the superior performance of the proposed method is demonstrated in simulation results.
Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28 - July 1, 2009; 01/2009
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ABSTRACT: The combination of Compressed Sensing (CS) and SENSitivity Encoding (SENSE) for improving MRI acquisition speed and robustness has recently drawn great attentions. However, in the direct combination, the encoding matrix which represents the Fourier transform of channel-specific sensitivity modulation is not guaranteed to be a good CS matrix. In this paper, we propose a different approach that applies CS and SENSE sequentially. The method first uses CS to reconstruct a set of aliased images in each coil, and then applies the basic SENSE on these images to reconstruct the final image. The total reduction factor can achieve the product of the factors of each individual method. The experimental results show that overall performance of our proposed method is superior to the direct combination method with the same reduction factor.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2008; 2008:1667-70.
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Journal of Multimedia. 01/2008; 3:20-27.
