Ke Fang, Zejun Wang, Zhaoqing Li, Bao Wang, Guangxu Han, Zhaowei Cheng, Zhihong Chen, Chuanjin Lan, Yi Zhang, Peng Zhao, Xinyu Jin, Yingchao Liu, Ruiliang Bai
Quantitative physiological parameters can be obtained from nonlinear pharmacokinetic models, such as the extended Tofts (eTofts) model, applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, the computation of such nonlinear models is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for accelerating the computation of fitting eTofts model without sacrificing agreement with conventional nonlinear-least-square (NLLS) fitting. This was a retrospective study, which included 13 patients with brain glioma for training (75%) and validation (25%), and 11 patients (three glioma, four brain metastases, and four lymphoma) for testing...
December 31, 2020: Journal of Magnetic Resonance Imaging: JMRI