University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Stochastic Gradient Langevin dynamics for joint parameterization of tracer kinetic models, input functions, and T1 relaxation-times from undersampled k-space DCE-MRI

Dikaios, Nikolaos (2020) Stochastic Gradient Langevin dynamics for joint parameterization of tracer kinetic models, input functions, and T1 relaxation-times from undersampled k-space DCE-MRI Medical Image Analysis, 62, 101690.

Full text not available from this repository.

Abstract

Dynamic Contrast Enhanced (DCE) Magnetic Resonance Imaging (MRI) is an important diagnostic technique that can quantify the structure and function of microvasculature processes, using T1 relaxation times and tracer kinetic maps. However, a series of methodological limitations affect both the accuracy and standardisation of the quantified maps, and consequently their diagnostic ability. The main methodological challenge in the quantification of tracer kinetics is a multi-parameter optimization, with correlated parameters that have different scales, which results in local minima particularly when measurements are highly undersampled. This work suggests a novel data driven optimization scheme, based on a variation of the Stochastic Gradient Langevin dynamics (SGLD) Markov chain Monte Carlo algorithm, which combines stochastic gradient descent and Langevin dynamics. The proposed SGDL algorithm avoided local minima and accurately quantified proton density, T1 relaxation times and tracer kinetics. Joint direct parameterization significantly benefited the quantification of proton density, T1 relaxation times, and the selection of a suitable tracer kinetic model per tissue type. Model based arterial and portal vein input functions were automatically determined during the joint direct parameterization. Observations made on simulated fully and highly undersampled liver DCE MRI data were confirmed on acquired clinical data.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Dikaios, Nikolaosn.dikaios@surrey.ac.uk
Date : 16 March 2020
DOI : 10.1016/j.media.2020.101690
Copyright Disclaimer : © 2020 Elsevier B.V. All rights reserved.
Uncontrolled Keywords : Stochastic optimization Model-based reconstruction Dynamic contrast enhanced magnetic resonance imaging
Depositing User : James Marshall
Date Deposited : 10 Apr 2020 17:08
Last Modified : 10 Apr 2020 17:08
URI: http://epubs.surrey.ac.uk/id/eprint/854144

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800