Estimation of pharmacokinetic parameters from DCE-MRI by extracting long and short time-dependent features using an LSTM network.

ABSTRACT:

Purpose

T₁ -weighted dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) is typically quantified by least squares (LS) fitting to a pharmacokinetic (PK) model to yield parameters of microvasculature and perfusion in normal and disease tissues. Such fitting is both time-consuming as well as subject to inaccuracy and instability in parameter estimates. Here, we propose a novel neural network approach to estimate the PK parameters by extracting long and short time-dependent features in DCE-MRI.

Methods

A Long Short-Term Memory (LSTM) network, widely used for processing sequence data, was employed to map DCE-MRI time-series accompanied with an arterial input function to parameters of the extended Tofts model. Head and neck DCE-MRI from 103 patients were use

SUBMITTER: Zou J 

PROVIDER: S-EPMC7429301 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature