  ${\bf Purpose}$: 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.
