Improved unsupervised physics‐informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients

作者：Misha Kaandorp, Sebastiano Barbieri, Remy Klaassen, Hanneke W. M. van Laarhoven, Hans Crezee, Peter Thomas While, Aart Johannes Nederveen, Oliver J. Gurney‐Champion · 发表于：Magnetic Resonance in Medicine · 年份：2021 · DOI：10.1002/mrm.28852 · 被引用次数：15 · 研究领域：Pancreatic and Hepatic Oncology Research、Advanced Radiotherapy Techniques、Medical Imaging Techniques and Applications

Purpose Earlier work showed that IVIM‐NETorig, an unsupervised physics‐informed deep neural network, was faster and more accurate than other state‐of‐the‐art intravoxel‐incoherent motion (IVIM) fitting approaches to diffusion‐weighted imaging (DWI). This study presents a substantially improved version, IVIM‐NEToptim, and characterizes its superior performance in pancreatic cancer patients. Method In simulations (signal‐to‐noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM‐NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root‐mean‐square error (NRMSE), Spearman’s ρ, and the coefficient of variation (CVNET), respectively. The best performing network, IVIM‐NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM‐NEToptim’s performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within‐subject standard deviations (wSD) and treatment‐induced changes were assessed. Results In simulations (SNR = 20), IVIM‐NEToptim outperformed IVIM‐NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs ...