Fast parallel spiral chemical shift imaging at 3T using iterative SENSE reconstruction

Stanford University, Richard M. Lucas Center for Magnetic Resonance Spectroscopy and Imaging, Department of Radiology, Stanford, California 94305-5488, USA.
Magnetic Resonance in Medicine (Impact Factor: 3.57). 04/2008; 59(4):891-7. DOI: 10.1002/mrm.21572
Source: PubMed


Spiral chemical shift imaging (CSI) is a fast CSI technique that simultaneously encodes 1D spectral and 2D spatial information. Therefore, it potentially allows one to perform a 2D-CSI experiment in a single shot. However, for most applications, limitations on maximum gradient strength and slew rate make multiple excitations necessary in order to achieve a desired spectral bandwidth. In this work we reduce the number of spatial interleaves and, hence, the minimum total measurement time of spiral CSI by using an iterative sensitivity encoding reconstruction algorithm which utilizes complementary spatial encoding afforded by the spatially inhomogeneous sensitivity profiles of individual receiver coils. The performance of the new method was evaluated in phantom and in vivo experiments. Parallel spiral CSI produced maps of brain metabolites similar to those obtained using conventional gridding reconstruction of the fully sampled data with only a small decrease in time-normalized signal-to-noise ratio and a small increase in noise for higher acceleration factors.

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    • "Acceleration of such non-Cartesian acquisitions through PPI techniques, namely non-Cartesian PPI, is currently a subject of much interests [16]–[27]. Advances on this subject will be a great benefit to many clinical applications, such as neuroimaging [28]–[31], cardiac applications [32], [33], and hyperpolarized MR acquisitions [34]. Despite of these advantages and developments of non-Cartesian PPI, there is no commercial non-Cartesian PPI package available yet. "
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    ABSTRACT: Both acquisition and reconstruction speed are crucial for magnetic resonance (MR) imaging in clinical applications. In this paper, we present a fast reconstruction algorithm for SENSE in partially parallel MR imaging with arbitrary k-space trajectories. The proposed method is a combination of variable splitting, the classical penalty technique and the optimal gradient method. Variable splitting and the penalty technique reformulate the SENSE model with sparsity regularization as an unconstrained minimization problem, which can be solved by alternating two simple minimizations: One is the total variation and wavelet based denoising that can be quickly solved by several recent numerical methods, whereas the other one involves a linear inversion which is solved by the optimal first order gradient method in our algorithm to significantly improve the performance. Comparisons with several recent parallel imaging algorithms indicate that the proposed method significantly improves the computation efficiency and achieves state-of-the-art reconstruction quality.
    03/2011; 30(3):575-85. DOI:10.1109/TMI.2010.2088133
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    • "the spatio-temporal data independently using 2-D image reconstruction techniques [5], [11]. The spectral information is then recovered from these 3-D dataset by evaluating a temporal Fourier transform. "
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    ABSTRACT: In this paper we propose an efficient sparse reconstruction scheme for the parallel MRSI data acquired using a fast spiral scheme. We model the system using the MR priors estimated from the water reference scan. In our sparse reconstruction approach, we minimize the total variation and ℓ<sub>1</sub> norm of the compartmentalized MRSI data in order to reduce noise, inhomogeneity distortions, and spectral leakage. We demonstrate significant improvement for in vivo brain results when compared to classical regularized SENSE MRSI reconstruction.
    Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 03/2011
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    • ", GRAPPA with EPSI [18] [19] and SENSE with spiral trajectories [20], have also been proposed for achieving an even faster scan time. However, the application of these fast techniques for clinical applications might need more evaluation for the SNR, volumetric coverage, possible artifacts and their impact on the metabolite levels and the disease extent definition. "
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    ABSTRACT: The goal of this study was to implement time efficient data acquisition and reconstruction methods for 3D magnetic resonance spectroscopic imaging (MRSI) of gliomas at a field strength of 3T using parallel imaging techniques. The point spread functions, signal to noise ratio (SNR), spatial resolution, metabolite intensity distributions and Cho:NAA ratio of 3D ellipsoidal, 3D sensitivity encoding (SENSE) and 3D combined ellipsoidal and SENSE (e-SENSE) k-space sampling schemes were compared with conventional k-space data acquisition methods. The 3D SENSE and e-SENSE methods resulted in similar spectral patterns as the conventional MRSI methods. The Cho:NAA ratios were highly correlated (P<.05 for SENSE and P<.001 for e-SENSE) with the ellipsoidal method and all methods exhibited significantly different spectral patterns in tumor regions compared to normal appearing white matter. The geometry factors ranged between 1.2 and 1.3 for both the SENSE and e-SENSE spectra. When corrected for these factors and for differences in data acquisition times, the empirical SNRs were similar to values expected based upon theoretical grounds. The effective spatial resolution of the SENSE spectra was estimated to be same as the corresponding fully sampled k-space data, while the spectra acquired with ellipsoidal and e-SENSE k-space samplings were estimated to have a 2.36-2.47-fold loss in spatial resolution due to the differences in their point spread functions. The 3D SENSE method retained the same spatial resolution as full k-space sampling but with a 4-fold reduction in scan time and an acquisition time of 9.28 min. The 3D e-SENSE method had a similar spatial resolution as the corresponding ellipsoidal sampling with a scan time of 4:36 min. Both parallel imaging methods provided clinically interpretable spectra with volumetric coverage and adequate SNR for evaluating Cho, Cr and NAA.
    Magnetic Resonance Imaging 09/2009; 27(9):1249-57. DOI:10.1016/j.mri.2009.05.028 · 2.09 Impact Factor
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