Model-Based Maximum-Likelihood Estimation for Phase- and Frequency-Encoded Magnetic-Resonance-Imaging Data

Miller, Michael I.; Schaewe, T.J.; Bosch, Coleen S.; Ackerman, Joseph J. H.

doi:10.1006/jmrb.1995.1081

Cited by 10 publications

(8 citation statements)

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“…Some methods are particularly sensitive in terms of efficiency to the number of components. For example, in [53][54][55], the expectation-maximization (EM) algorithm is proposed to be applied to NMR. This algorithm divides the problem into K independent optimizations, K being the number of components in the signal, and allows computations on parallel computers to reduce its characteristic high computation load.…”

Section: Use Of a Basis Set Of Metabolite Profiles In The Model Functmentioning

confidence: 99%

MRS signal quantitation: A review of time- and frequency-domain methods

Poullet

Sima

Huffel

2008

Journal of Magnetic Resonance

129

126

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Section: Use Of a Basis Set Of Metabolite Profiles In The Model Functmentioning

confidence: 99%

MRS signal quantitation: A review of time- and frequency-domain methods

Poullet

Sima

Huffel

2008

Journal of Magnetic Resonance

129

126

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“…An alternative approach to enhanced reconstruction of low spatial resolution MRSI by making use of a priori spatial information is a full parametric modeling and optimization approach, similar to that proposed for MRI [13]. For this, however, the addition of at least seven parameters in the spectral dimension, for the relatively simple metabolite model used in this report, makes this type of approach computationally difficult, undoubtedly requiring parallel processing implementation [28].…”

Section: Discussionmentioning

confidence: 99%

“…The estimated R is further used in the M-step (12) to obtain the subregional tissue metabolite intensity b mur (13) Upon estimating b mur , the function S b can then be obtained using the signal model of (4) and (5). This can be done for a wider k-space extent than the acquired data so as to include highfrequency k-space information corresponding to the detailed edge definition of each region.…”

Section: A Theoretical Formulation Of the Improved Mrsi Reconstructimentioning

confidence: 99%

Improved Reconstruction for MR Spectroscopic Imaging

Bao

Maudsley

2007

IEEE Trans. Med. Imaging

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Sensitivity limitations of in vivo magnetic resonance spectroscopic imaging (MRSI) require that the extent of spatial-frequency (k-space) sampling be limited, thereby reducing spatial resolution and increasing the effects of Gibbs ringing that is associated with the use of Fourier transform reconstruction. Additional problems occur in the spectral dimension, where quantitation of individual spectral components is made more difficult by the typically low signal-to-noise ratios, variable lineshapes, and baseline distortions, particularly in areas of significant magnetic field inhomogeneity. Given the potential of in vivo MRSI measurements for a number of clinical and biomedical research applications, there is considerable interest in improving the quality of the metabolite image reconstructions. In this report, a reconstruction method is described that makes use of parametric modeling and MRI-derived tissue distribution functions to enhance the MRSI spatial reconstruction. Additional preprocessing steps are also proposed to avoid difficulties associated with image regions containing spectra of inadequate quality, which are commonly present in the in vivo MRSI data. KeywordsBrain metabolite images; image reconstruction; magnetic resonance spectroscopic imaging (MRSI)

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“…Reconstruction to higher resolution using Bayesian image analysis methods for multi-contrast structural MRI is presented in [8]; however, they do not incorporate a full signal model and hence are subject to all of the problems with artifacts that are incurred from use of the DFT for reconstruction of low-resolution modalities. A model that incorporates a raw signal model for structural MRI was presented for maximum likelihood estimation in [9] and Bayesian maximum a posteriori reconstruction in [10]; however, the first model's focus was on high-resolution structural MRI, and the only prior information considered in [10] was a notion of overall a priori smoothness. Denney and Reeves [11] have developed a Bayesian approach for MRSI reconstruction that models the data in k -space-time and utilizes an edge preserving prior.…”

Section: Introductionmentioning

confidence: 99%

Bayesian $k$-Space–Time Reconstruction of MR Spectroscopic Imaging for Enhanced Resolution

Kornak

Young

Soher

et al. 2010

IEEE Trans. Med. Imaging

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A k-space-time Bayesian statistical reconstruction method (K-Bayes) is proposed for the reconstruction of metabolite images of the brain from proton (1H) Magnetic Resonance Spectroscopic Imaging (MRSI) data. K-Bayes performs full spectral fitting of the data while incorporating structural (anatomical) spatial information through the prior distribution. K-Bayes provides increased spatial resolution over conventional discrete Fourier transform (DFT) based methods by incorporating structural information from higher-resolution co-registered and segmented structural Magnetic Resonance Images. The structural information is incorporated via a Markov random field (MRF) model that allows for differential levels of expected smoothness in metabolite levels within homogeneous tissue regions and across tissue boundaries. By further combining the structural prior model with a k-space-time MRSI signal and noise model (for a specific set of metabolites and based on knowledge from prior spectral simulations of metabolite signals) the impact of artifacts generated by low-resolution sampling is also reduced. The posterior mode estimates are used to define the metabolite map reconstructions, obtained via a generalized Expectation-Maximization algorithm. K-Bayes was tested using simulated and real MRSI datasets consisting of sets of k-space-time-series (the recorded free induction decays). The results demonstrated that K-Bayes provided qualitative and quantitative improvement over DFT methods.

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Model-Based Maximum-Likelihood Estimation for Phase- and Frequency-Encoded Magnetic-Resonance-Imaging Data

Cited by 10 publications

References 0 publications

MRS signal quantitation: A review of time- and frequency-domain methods

MRS signal quantitation: A review of time- and frequency-domain methods

Improved Reconstruction for MR Spectroscopic Imaging

Bayesian $k$-Space–Time Reconstruction of MR Spectroscopic Imaging for Enhanced Resolution

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