Parallel Algorithms for Maximum-Likelihood Nuclear Magnetic Resonance Spectroscopy

Chen, S.C.; Schaewe, T.J.; Teichman, R.S.; Miller, Michael I.; Nadel, S N; Greene, Andrew S.

doi:10.1006/jmra.1993.1062

Cited by 8 publications

(3 citation statements)

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“…The pattern theoretic methods are being applied in several applications: HANDS (Grenander et al, 1990), LEAVES (Knoerr, 1988), XRAYS (Amit et al, 1991), NMR (Miller and Greene, 1989;Chen et al, 1993), AMOEBA (Joshi et al, 1992;Joshi and Miller, 1993), BRAINS Christensen et al, 1993a,b;Miller, Christensen, Amit and Grenander, 1993), TRACKS Srivastava et al, 1991Srivastava et al, , 1992Miller, Teichman, Srivastava, O'Sullivan and Snyder, 1993), LANGUAGE (Mark and Miller, 1992), ARTERIES (Elion et al, 1991) and COLOR (Grenander and Manbeck, 1993). We shall briefly describe several applications in HANDS, LEAVES and XRAYS, which are projects that have been virtually completed, and then show results from problems in BRAINS and TRACKING, both of which feature the global shape model and random graph (unknown model order) estimation components of the problem.…”

Section: Discussionmentioning

confidence: 99%

Representations of Knowledge in Complex Systems

Grenander

Miller

1994

Journal of the Royal Statistical Society Series B: Statistical Methodology

378

271

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Modern sensor technologies, especially in biomedicine, produce increasingly detailed and informative image ensembles, many extremely complex. It will be argued that pattern theory can supply mathematical representations of subject-matter knowledge that can be used as a basis for algorithmic 'understanding' of such pictures. After a brief survey of the basic principles of pattern theory we shall illustrate them by an application to a concrete situation: high magnification (greater than 15000x) electron micrographs of cardiac muscle cells. The aim is to build algorithms for automatic hypothesis formation concerning the number, location, orientation and shape of mitochondria and membranes. For this we construct a pattern theoretic model in the form of a prior probability measure on the space of configurations describing these hypotheses. This measure is synthesized by solving sequentially a jump-diffusion equation of generalized Langevin form. The jumps occur for the creation-annihilation of hypotheses, corresponding to a jump from one continuum to another in configuration (hypothesis) space. These continua (subhypotheses) are expressed in terms of products of low dimensional Lie groups acting on the generators of a template. We use a modified Bayes approach to obtain the hypothesis formation, also organized by solving a generalized Langevin equation. To justify this it is shown that the resulting jumpdiffusion process is ergodic so that the solution converges to the desired probability measure. To speed up the convergence we reduce the computation of the drift term in the stochastic differential equation analytically to a curvilinear integral, with the random term computed almost instantaneously. The algorithms thus obtained are implemented, both for mitochondria and membranes, on a 4000 processor parallel machine. Photographs of the graphics illustrate how automatic hypothesis formation is achieved. This approach is applied to deformable neuroanatomical atlases and tracking recognition from narrow band and high resolution sensor arrays.

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Section: Discussionmentioning

confidence: 99%

Representations of Knowledge in Complex Systems

Grenander

Miller

1994

Journal of the Royal Statistical Society Series B: Statistical Methodology

378

271

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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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“…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]. In comparison, the proposed method simplifies the problem by separating the determination of the signal amplitudes for the higher spatial resolution from the determination of the spectral parameters.…”

Section: Discussionmentioning

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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Parallel Algorithms for Maximum-Likelihood Nuclear Magnetic Resonance Spectroscopy

Cited by 8 publications

References 0 publications

Representations of Knowledge in Complex Systems

Representations of Knowledge in Complex Systems

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

Improved Reconstruction for MR Spectroscopic Imaging

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