2 research outputs found
Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients
: Earlier work showed that IVIM-NET, 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, and characterizes
its superior performance in pancreatic ductal adenocarcinoma (PDAC) patients.
: 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 , and
the coefficient of variation (CV), respectively. The best performing
network, IVIM-NET was compared to least squares (LS) and a Bayesian
approach at different SNRs. IVIM-NET'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. : In simulations, IVIM-NET outperformed
IVIM-NET in accuracy (NRMSE(D)=0.18 vs 0.20; NMRSE(f)=0.22 vs 0.27;
NMRSE(D*)=0.39 vs 0.39), independence ((D*,f)=0.22 vs 0.74) and
consistency (CV (D)=0.01 vs 0.10; CV (f)=0.02 vs 0.05;
CV (D*)=0.04 vs 0.11). IVIM-NET showed superior performance
to the LS and Bayesian approaches at SNRs<50. In vivo, IVIM-NET
sshowed significantly less noisy parameter maps with lower wSD for D and f than
the alternatives. In the treated cohort, IVIM-NET detected the most
individual patients with significant parameter changes compared to day-to-day
variations. : IVIM-NET is recommended for IVIM
fitting to DWI data
Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients
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 diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NET optim, 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 (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 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-NET optim outperformed IVIM-NET orig 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 0.74), and consistency (CV NET(D) = 0.013 vs 0.104; CV NET(f) = 0.020 vs 0.054; CV NET(D*) = 0.036 vs 0.110). IVIM-NET optim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NET optim showed 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. Conclusion: IVIM-NET optim is recommended for accurate, informative, and consistent IVIM fitting to DWI data