Spatially regularized estimation of the tissue homogeneity model parameters in DCE‐MRI using proximal minimization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F19%3A00507670" target="_blank" >RIV/68081731:_____/19:00507670 - isvavai.cz</a>

Alternative codes found

RIV/67985556:_____/19:00507670 RIV/00216305:26220/19:PU130915

Result on the web

<a href="https://onlinelibrary.wiley.com/doi/10.1002/mrm.27874" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/mrm.27874</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/mrm.27874" target="_blank" >10.1002/mrm.27874</a>

Result language

angličtina

Original language name

Spatially regularized estimation of the tissue homogeneity model parameters in DCE‐MRI using proximal minimization

Original language description

The Tofts and the extended Tofts models are the pharmacokinetic models commonly used in dynamic contrast‐enhanced MRI (DCE‐MRI) perfusion analysis, although they do not provide two important biological markers, namely, the plasmaflow and the permeability‐surface area product. Estimates of such markers are possible using advanced pharmacokinetic models describing the vascular distribution phase, such as the tissue homogeneity model. However, the disadvantage of theadvanced models lies in biased and uncertain estimates, especially when the estimates are computed voxelwise. The goal of this work is to improve the reliability of the estimates by including information from neighboring voxels. Information from the neighboring voxels is incorporated in the estimation process through spatial regularization in the form of total variation. The spatial regularization is applied on five maps of perfusion parameters estimated using the tissue homogeneity model. Since the total variation is not differentiable, two proximal techniques of convex optimization are used to solve the problem numerically. The proposed algorithm helps to reduce noise in the estimated perfusionparameter maps together with improving accuracy of the estimates. These conclusions are proved using a numerical phantom. In addition, experiments on real datanshow improved spatial consistency and readability of perfusion maps without considerable lowering of the quality of fit. The reliability of the DCE‐MRI perfusion analysis using the tissue homogeneity model can be improved by employing spatial regularization. The proposed utilization of modern optimization techniques implies only slightly higher computational costs compared to the standard approach without spatial regularization.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20601 - Medical engineering

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Magnetic Resonance in Medicine

ISSN

0740-3194

e-ISSN

1522-2594

Volume of the periodical

82

Issue of the periodical within the volume

6

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

2257-2272

UT code for WoS article

000483797700024

EID of the result in the Scopus database

2-s2.0-85071714198