SIINR: Structurally Informed Implicit Neural Representations for super-resolution with uncertainty quantification of clinical quality diffusion MRI datasets

2026-07-22 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

SIINR (Structurally Informed Implicit Neural Representations) is a novel framework designed for super-resolution of clinical diffusion Magnetic Resonance Imaging (dMRI) datasets, simultaneously quantifying uncertainty in its reconstructed outputs. Clinical dMRI often suffers from low out-of-plane resolution, degrading structural information. SIINR addresses this by combining a supervised 3D U-net as a prior with a self-supervised implicit neural representation (INR). This INR fuses the high-resolution prior and original low-resolution data, enabling joint modeling across spatial and angular domains, enforcing data consistency, and providing analytic approximate posterior distributions for uncertainty quantification. Validated on diverse open-access dMRI datasets, SIINR outperforms standard interpolation methods in quantitative error metrics and anatomical fidelity. Its effectiveness is further demonstrated in clinical cases, including subjects with multiple sclerosis and brain lesions, where it propagates intensity changes and flags uncertain regions.

Key takeaway

For neuroimaging scientists analyzing clinical dMRI data, SIINR offers a principled approach to overcome low out-of-plane resolution. You can achieve enhanced anatomical fidelity and more reliable derived metrics by applying this framework. It allows you to quantify reconstruction uncertainty, which is crucial for robust interpretation, especially in challenging cases like multiple sclerosis or brain lesions. Consider integrating SIINR to improve diagnostic confidence and support advanced dMRI analysis.

Key insights

SIINR enhances clinical dMRI super-resolution by fusing U-net priors with INRs, quantifying uncertainty for robust interpretation.

Principles

• Combine supervised priors with self-supervised INRs.
• Jointly model spatial and angular dMRI domains.
• Quantify uncertainty for robust interpretation.

Method

SIINR uses a supervised 3D U-net as a prior, fused with a self-supervised implicit neural representation (INR) to process low-resolution dMRI data. The INR enables joint spatial/angular modeling and provides posterior distributions for uncertainty.

In practice

• Enhance low-resolution clinical dMRI scans.
• Identify uncertain regions in brain lesions.
• Adapt to various dMRI upsampling ratios.

Topics

• Diffusion MRI
• Super-resolution
• Implicit Neural Representations
• Uncertainty Quantification
• Clinical Neuroimaging
• 3D U-net

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.