788 research outputs found
Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
We propose to use Gaussian process regression to accurately estimate the
diffusion MRI signal at arbitrary locations in q-space. By estimating the
signal on a grid, we can do synthetic diffusion spectrum imaging:
reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier
transform. We also propose an alternative reconstruction method guaranteeing a
nonnegative EAP that integrates to unity. The reconstruction is validated on
data simulated from two Gaussians at various crossing angles. Moreover, we
demonstrate on non-uniformly sampled in vivo data that the method is far
superior to linear interpolation, and allows a drastic undersampling of the
data with only a minor loss of accuracy. We envision the method as a potential
replacement for standard diffusion spectrum imaging, in particular when
acquistion time is limited.Comment: 5 page
Stereochemical Studies on a New Ciramadol Analogue by NMR-Spectroscopy
The absol. configuration of a Ciramadol analogue obtained from (-)-menthone
is established by 'H-NMR-. simulated NMR-, COSY-90-, and NOEmeasurements.
The final compound 2-(a-1 -pyrrolidino)benzy 1-4-isopropyl-
1 -methyl-cyclohexan-3-one (4b), e.g.. has 1R.2S,4S.l IS-configuration
due to stereoselective Michael-type addition of pyrrolidine to the pertinent
benzylidene intermediate 3.
Die absol. Konfiguration einer Ciramadol-analogen Verbindung aus (-)-
Menthon wurde durch 'H-NMR-. simulierte NMR-. COSY-90- und NOEUntersuchungen
geklärt. Danach hat die als Beispiel untersuchte Verbindung
2-(a-1 -Pyrrolidino)benzyl-4-isopropyl-1 -methyl-cyclohexan-3-on
(4b) 1R,2S.4S.l IS-Konfiguration, die durch eine stereoselektive Michael-analoge
Addition des Pyrrolidins an die entspr. Benzyliden-Verbindung 3
entsteht
Bayesian uncertainty quantification in linear models for diffusion MRI
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue
microstructure. By fitting a model to the dMRI signal it is possible to derive
various quantitative features. Several of the most popular dMRI signal models
are expansions in an appropriately chosen basis, where the coefficients are
determined using some variation of least-squares. However, such approaches lack
any notion of uncertainty, which could be valuable in e.g. group analyses. In
this work, we use a probabilistic interpretation of linear least-squares
methods to recast popular dMRI models as Bayesian ones. This makes it possible
to quantify the uncertainty of any derived quantity. In particular, for
quantities that are affine functions of the coefficients, the posterior
distribution can be expressed in closed-form. We simulated measurements from
single- and double-tensor models where the correct values of several quantities
are known, to validate that the theoretically derived quantiles agree with
those observed empirically. We included results from residual bootstrap for
comparison and found good agreement. The validation employed several different
models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI)
and Constrained Spherical Deconvolution (CSD). We also used in vivo data to
visualize maps of quantitative features and corresponding uncertainties, and to
show how our approach can be used in a group analysis to downweight subjects
with high uncertainty. In summary, we convert successful linear models for dMRI
signal estimation to probabilistic models, capable of accurate uncertainty
quantification.Comment: Added results from a group analysis and a comparison with residual
bootstra
Some families of generating functions for the multiple orthogonal polynomials associated with modified Bessel K-functions
AbstractThe main object of this paper is to derive several substantially more general families of bilinear, bilateral, and mixed multilateral finite-series relationships and generating functions for the multiple orthogonal polynomials associated with the modified Bessel K-functions also known as Macdonald functions. Some special cases of the above statements are also given
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