Papers › Improved unsupervised physics-informed deep learning for intravoxel incoherent motion...
Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients
Misha P. T. Kaandorp, Sebastiano Barbieri, Remy Klaassen, Hanneke W. M. van Laarhoven, Hans Crezee, Peter T. While, Aart J. Nederveen, Oliver J. Gurney-Champion
Purpose: Earlier work showed that IVIM-NET_(orig), an unsupervised physics-informed deep neural network, was more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to DWI. This study presents an improved version: IVIM-NETₒₚₜᵢₘ, and characterizes its superior performance in pancreatic ductal adenocarcinoma (PDAC) patients. Method: In simulations (SNR=20), the accuracy, independence and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, # hidden layers, dropout, batch normalization, learning rate), by calculating the NRMSE, Spearman's ρ, and the coefficient of variation (CV_(NET)), 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 23 PDAC patients. 14 of the patients received no treatment between scan sessions and 9 received chemoradiotherapy between sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed. Results: In simulations, IVIM-NETₒₚₜᵢₘ outperformed IVIM-NET_(orig) in accuracy (NRMSE(D)=0.18 vs 0.20; NMRSE(f)=0.22 vs 0.27; NMRSE(D*)=0.39 vs 0.39), independence (ρ(D*,f)=0.22 vs 0.74) and consistency (CV_(NET) (D)=0.01 vs 0.10; CV_(NET) (f)=0.02 vs 0.05; CV_(NET) (D*)=0.04 vs 0.11). IVIM-NETₒₚₜᵢₘ showed superior performance to the LS and Bayesian approaches at SNRs<50. In vivo, IVIM-NETₒₚₜᵢₘ sshowed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NETₒₚₜᵢₘ detected the most individual patients with significant parameter changes compared to day-to-day variations. Conclusion: IVIM-NETₒₚₜᵢₘ is recommended for IVIM fitting to DWI data.
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