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arXiv · 2610.28873

Improved three component IVIM fitting using physics-informed neural network with multimodal spatial prior

Nhat Hoang·Abrar Faiyaz·Emmanuel A. Mensah·Md Nasir Uddin·Jianhui Zhong·Giovanni Schifitto
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Abstract

Purpose: To mitigate parameter degeneracy and improve robustness of IVIM fitting, a multimodal physics-informed neural network was developed to leverage anatomical information through the combined use of cross attention, selective spatial gating, and anatomy-weighted spatial prior. Methods: T1w and FLAIR anatomical images were co-registered to IVIM signal in native space and segmented by tissue type. The proposed Physics-Anchored Cross-attention Embedding network (PACE) encoded IVIM signals and their anatomical features in parallel branches, then integrated them via cross attention and spatial gating. An anatomy-weighted spatial prior further constrained signal reconstruction using anatomically similar neighboring voxels. PACE and its prior-guided variant were compared with two baseline deep neural networks (DNN and IVIM3C), least squares, non-negative least squares, and maximum a posteriori fitting models. All models were evaluated on signal and parameter reconstruction accuracy and lesion contrast fidelity using data generated from subjects drawn from the University of Rochester COVID cohort. Results: PACE variants demonstrated lower RMSE for all IVIM parameters except for Dmv. The anatomy-weighted spatial prior further reduced RMSE across all five parameters relative to the default PACE, with modest reductions in lesion contrast accuracy for fmv and fint. For in-vivo evaluation, PACE with spatial prior also showed the best signal reconstruction across all noise levels while maintaining good to excellent ICC. Conclusion: Multimodal anatomical information improved IVIM fitting accuracy and robustness, with selective spatial integration via cross attention, gating, and anatomy-weighted spatial prior. Keywords: intravoxel incoherent motion, physics-informed deep learning, diffusion MRI, multimodality fusion, attention mechanism

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Nhat Hoang, Abrar Faiyaz, Emmanuel A. Mensah, Md Nasir Uddin, Jianhui Zhong, Giovanni Schifitto. 2026-10-20. Improved three component IVIM fitting using physics-informed neural network with multimodal spatial prior. https://arxiv.org/abs/2610.28873

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