{\bf Purpose}: Earlier work showed that IVIM-NET_{orig}, 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 DWI. This study presents  : IVIM-NET_{optim}, overcoming IVIM-NET_{orig . {\bf 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 \rho, and the coefficient of variation (CV_{NET}), respectively. The best performing network, IVIM-NET_{optim} was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NET_{optim}'s performance was evaluated in 23 pancreatic ductal adenocarcinoma (PDAC ) patients. 14 of the patients received no treatment between 2 repeated scan sessions and 9 received chemoradiotherapy between sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed. {\bf Results}: In simulations, IVIM-NET_{optim} outperformed IVIM-NET_{orig} in accuracy (NRMSE(D)= 0.14 vs 0.17 ; NMRSE(f)= 0.26 vs 0.31 ; NMRSE(D*)= 0.46 vs 0.49 ), independence (\rho(D*,f)= 0.32 vs 0.95 ) and consistency (CV_{NET} (D)= 0.028 vs 0.185 ; CV_{NET} (f)= 0.025 vs 0.078 ; CV_{NET} (D*)= 0.075 vs 0.144 ). IVIM-NET_{optim} showed superior performance to the LS and Bayesian approaches at SNRs<50. In vivo, IVIM-NET_{optim} showed less noisy and more detailed parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NET_{optim} detected the most individual patients with significant parameter changes compared to day-to-day variations. {\bf Conclusion}: IVIM-NET_{optim} is recommended for accurate IVIM fitting to DWI data.
{\bf 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-NET_{optim}, and characterizes its superior performance in pancreatic ductal adenocarcinoma (PDAC) patients . {\bf 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 \rho, and the coefficient of variation (CV_{NET}), respectively. The best performing network, IVIM-NET_{optim} was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NET_{optim}'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. {\bf Results}: In simulations, IVIM-NET_{optim} 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 (\rho(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-NET_{optim} showed superior performance to the LS and Bayesian approaches at SNRs<50. In vivo, IVIM-NET_{optim} sshowed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NET_{optim} detected the most individual patients with significant parameter changes compared to day-to-day variations. {\bf Conclusion}: IVIM-NET_{optim} is recommended for IVIM fitting to DWI data.
D
faster and
A
an improved version
R
overcoming IVIM-NET_{orig
and characterizes its superior performance in pancreatic ductal adenocarcinoma (PDAC) patients
R
pancreatic ductal adenocarcinoma (PDAC )
PDAC
D
2 repeated
R
0.14 vs 0.17
0.18 vs 0.20
R
0.26 vs 0.31
0.22 vs 0.27
R
0.46 vs 0.49
0.39 vs 0.39
R
0.32 vs 0.95
0.22 vs 0.74
R
0.028 vs 0.185
0.01 vs 0.10
R
0.025 vs 0.078
0.02 vs 0.05
R
0.075 vs 0.144
0.04 vs 0.11
R
showed less noisy and more detailed
sshowed significantly less noisy
D
accurate