Enhancing the acquisition efficiency of fast magnetic resonance imaging via broadband encoding of signal content

Mitsouras, Dimitris; Zientara, Gary P.; Edelman, Alan; Rybicki, Frank J.

doi:10.1016/j.mri.2006.07.003

Cited by 2 publications

(5 citation statements)

References 41 publications

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“…Non-Fourier encoding schemes using selective excitation have been investigated previously (see [43]–[46] and their references), though outside of the context of CS-MRI. In contrast to these previous works, we use selective excitation to implement an encoding scheme similar to the “universal” encoding suggested by the CS literature [3].…”

Section: Cs-mri With Random Encodingmentioning

confidence: 99%

“…Given the limitations of current multidimensional excitation technology, this necessitates the use of multiple pulses in practice. This limitation is common to other 2D non-Fourier encoding schemes that use spatially-selective excitation (e.g., [43], [46]), though can be overcome if the RF encoding is applied only along the third dimension of a 3D experiment (e.g., [8], [45]). In addition, the use of varying excitation angles can complicate steady-state behavior [49].…”

Section: Cs-mri With Random Encodingmentioning

confidence: 99%

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Compressed-Sensing MRI With Random Encoding

Haldar

Hernando

Liang

2011

IEEE Trans. Med. Imaging

259

172

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Compressed sensing (CS) has the potential to reduce magnetic resonance (MR) data acquisition time. In order for CS-based imaging schemes to be effective, the signal of interest should be sparse or compressible in a known representation, and the measurement scheme should have good mathematical properties with respect to this representation. While MRimages are often compressible, the second requirement is often only weakly satisfied with respect to commonly used Fourier encoding schemes. This paper investigates the use of random encoding for CS-MRI, in an effort to emulate the “universal” encoding schemes suggested by the theoretical CS literature. This random encoding is achieved experimentally with tailored spatially-selective radio-frequency (RF) pulses. Both simulation and experimental studies were conducted to investigate the imaging properties of this new scheme with respect to Fourier schemes. Results indicate that random encoding has the potential to outperform conventional encoding in certain scenarios. However, our study also indicates that random encoding fails to satisfy theoretical sufficient conditions for stable and accurate CS reconstruction in many scenarios of interest. Therefore, there is still no general theoretical performance guarantee for CS-MRI, with or without random encoding, and CS-based methods should be developed and validated carefully in the context of specific applications.

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Section: Cs-mri With Random Encodingmentioning

confidence: 99%

Section: Cs-mri With Random Encodingmentioning

confidence: 99%

Compressed-Sensing MRI With Random Encoding

Haldar

Hernando

Liang

2011

IEEE Trans. Med. Imaging

259

172

View full text Add to dashboard Cite

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“…background noise in the images suggest that indeed higher SNR can be achieved by manipulating the density compensation function. This is likely related to the fact that uneven weighting of samples has a direct effect on image SNR (31)(32)(33)(34). Furthermore, neither visual sharpness nor fidelity appear compromised when the Cross-Correlations method is used to achieve higher SNR.…”

Section: Resultsmentioning

confidence: 99%

“…[14]). It was expected that by constraining the amplitude of samples the amplification of noise can be limited thereby avoiding the SNR degradation inherent to uneven amplification of samples, or equivalently, image subspaces (32)(33)(34). Using this extension, SNR gains of 15-30% were presented in conjunction with reduced NRMSE.…”

Section: Discussionmentioning

confidence: 99%

“…When samples correspond to phase encoding functions that are not orthogonal within the extent of the FOV, some subspaces in the entire space spanned by the basis are more heavily sampled than others. If all information accessed by the samples is equalized to contribute to the reconstructed image, some image subspaces will require larger amplification, and this uneven weighting results in reduced SNR (31)(32)(33)(34).…”

Section: Discussionmentioning

confidence: 99%

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Fast, exact k‐space sample density compensation for trajectories composed of rotationally symmetric segments, and the SNR‐optimized image reconstruction from non‐Cartesian samples

Mitsouras

Mulkern

Rybicki

2008

Magnetic Resonance in Med

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A recently developed method for exact density compensation of non uniformly arranged samples relies on the analytically known cross-correlations of Fourier basis functions corresponding to the traced k -space trajectory. This method produces a linear system whose solution represents compensated samples that normalize the contribution of each independent element of information that can be expressed by the underlying trajectory. Unfortunately, linear system-based density compensation approaches quickly become computationally demanding with increasing number of samples (i.e., image resolution). Here, it is shown that when a trajectory is composed of rotationally symmetric interleaves, such as spiral and PROPELLER trajectories, this cross-correlations method leads to a highly simplified system of equations. Specifically, it is shown that the system matrix is circulant block-Toeplitz so that the linear system is easily block-diagonalized. The method is described and demonstrated for 32-way interleaved spiral trajectories designed for 256 image matrices; samples are compensated non iteratively in a few seconds by solving the small independent block-diagonalized linear systems in parallel. Because the method is exact and considers all the interactions between all acquired samples, up to a 10% reduction in reconstruction error concurrently with an up to 30% increase in signal to noise ratio are achieved compared to standard density compensation methods. Magn Reson Med 60:339-349, 2008.

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Enhancing the acquisition efficiency of fast magnetic resonance imaging via broadband encoding of signal content

Cited by 2 publications

References 41 publications

Compressed-Sensing MRI With Random Encoding

Compressed-Sensing MRI With Random Encoding

Fast, exact k‐space sample density compensation for trajectories composed of rotationally symmetric segments, and the SNR‐optimized image reconstruction from non‐Cartesian samples

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