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

arXiv: https://arxiv.org/abs/2011.01689 · HTML: https://mlsanity.com/paper/2011.01689/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 16 of 16)

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- [SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency](https://mlsanity.com/paper/2206.03820/) (2022)
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- [IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data](https://mlsanity.com/paper/2401.07126/) (2024)
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- [Accelerating MRI Uncertainty Estimation with Mask-based Bayesian Neural Network](https://mlsanity.com/paper/2407.05521/) (2024)
- [SCREENER: A general framework for task-specific experiment design in quantitative MRI](https://mlsanity.com/paper/2408.11834/) (2024)
- [A Comprehensive Framework for Uncertainty Quantification of Voxel-wise Supervised Models in IVIM MRI](https://mlsanity.com/paper/2508.04588/) (2025)
- [Protocol generalisation for brain tissue microstructure estimation via hypernetwork-controlled geometric deep learning](https://mlsanity.com/paper/2608.02053/) (2026)

## Abstract (arXiv)

bf Purpose: Earlier work showed that IVIM-NETorig, 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. 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 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 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-NEToptim outperformed IVIM-NETorig 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 (CVNET (D)=0.01 vs 0.10; CVNET (f)=0.02 vs 0.05; CVNET (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. bf Conclusion: IVIM-NEToptim is recommended for IVIM fitting to DWI data.