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

• Published in: Medical physics (Lancaster) Vol. 47; no. 8; pp. 3447 - 3457
• Main Authors: Zou, Jiaren, Balter, James M., Cao, Yue
• Format: Journal Article
• Language: English
• Published: United States 01.08.2020
• Subjects: Algorithms; contrast agent; Contrast Media; DCE‐MRI; Humans; Least-Squares Analysis; long‐short‐term memory; machine learning; Magnetic Resonance Imaging; Neural Networks, Computer; pharmacokinetic model; temporal correlation; pharmacokinetic model; temporal correlation; DCE-MRI; long-short-term memory; machine learning; contrast agent

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...