scholarly journals Gaussian Local Phase Approximation in a Cylindrical Tissue Model

2021 ◽  
Vol 9 ◽  
Author(s):  
Lukas T. Rotkopf ◽  
Eckhard Wehrse ◽  
Heinz-Peter Schlemmer ◽  
Christian H. Ziener

In NMR or MRI, the measured signal is a function of the accumulated magnetization phase inside the measurement voxel, which itself depends on microstructural tissue parameters. Usually the phase distribution is assumed to be Gaussian and higher-order moments are neglected. Under this assumption, only the x-component of the total magnetization can be described correctly, and information about the local magnetization and the y-component of the total magnetization is lost. The Gaussian Local Phase (GLP) approximation overcomes these limitations by considering the distribution of the local phase in terms of a cumulant expansion. We derive the cumulants for a cylindrical muscle tissue model and show that an efficient numerical implementation of these terms is possible by writing their definitions as matrix differential equations. We demonstrate that the GLP approximation with two cumulants included has a better fit to the true magnetization than all the other options considered. It is able to capture both oscillatory and dampening behavior for different diffusion strengths. In addition, the introduced method can possibly be extended for models for which no explicit analytical solution for the magnetization behavior exists, such as spherical magnetic perturbers.

1972 ◽  
Vol 94 (2) ◽  
pp. 577-581 ◽  
Author(s):  
R. C. Winfrey

Techniques for the solution of linear matrix differential equations have previously been applied to the dynamic analysis of a mechanism. However, because the mechanism changes geometry as it rotates, a large number of solutions are necessary to predict the mechanism’s elastic behavior for even a few revolutions. Also, a designer is frequently concerned with the elastic behavior of only one point on the mechanism and has no practical interest in a complete solution. For these reasons, a method is given here for reducing the total number of coordinates to one coordinate at the point of design interest. A considerable saving in computational time is obtained since the dynamic solution involves one degree of freedom instead of many. Further, since any solution will make use of some limiting assumptions, results here indicate that, for design purposes, reducing the coordinates does not significantly affect comparable accuracy.


Axioms ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 51 ◽  
Author(s):  
Carmela Scalone ◽  
Nicola Guglielmi

In this article we present and discuss a two step methodology to find the closest low rank completion of a sparse large matrix. Given a large sparse matrix M, the method consists of fixing the rank to r and then looking for the closest rank-r matrix X to M, where the distance is measured in the Frobenius norm. A key element in the solution of this matrix nearness problem consists of the use of a constrained gradient system of matrix differential equations. The obtained results, compared to those obtained by different approaches show that the method has a correct behaviour and is competitive with the ones available in the literature.


Sign in / Sign up

Export Citation Format

Share Document