Primitive Representation Learning for Unsupervised Dynamic Contrast-Enhanced MRI Reconstruction

Forum topic · 小凯 · 2026-08-20

Summary

This paper (arXiv:2608.18055) proposes a multi-dimensional, primitive-based framework for unsupervised reconstruction of dynamic contrast-enhanced (DCE) MRI. Building on prior scan-specific methods that use Gaussian and Gabor primitives—effective without large training datasets—the authors extend the approach to handle the additional temporal dimension of dynamic contrast. The framework disentangles underlying anatomy, dynamic contrast enhancement, and residual motion into separate temporal basis functions, enabling a geometrically interpretable representation. Experiments show the architecture achieves performance competitive with conventional reconstruction methods, both in reconstruction quality and in the accuracy of extracted aortic and renal enhancement curves. Its modular hierarchical design naturally extends to additional dynamic factors and higher acceleration rates. Code is available at https://github.com/compai-lab/2026-GaborDCE-spieker. Authors: Veronika Spieker, Wenqi Huang, Cemre Ariyurek, Liam Timms, Daniel Rueckert, Onur Afacan, Julia A. Schnabel, Sila Kurugol.

Paper Overview

Field: Computer Vision / Medical Imaging
Authors: Veronika Spieker, Wenqi Huang, Cemre Ariyurek, Liam Timms, Daniel Rueckert, Onur Afacan, Julia A. Schnabel, Sila Kurugol
arXiv: 2608.18055
Code: https://github.com/compai-lab/2026-GaborDCE-spieker

Abstract

Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan-specific reconstructions using Gaussian and Gabor primitives have shown promising results without the need for large training datasets, but have not addressed the additional dimension of dynamic contrast.

Contribution

The authors propose a multi-dimensional, primitive-based framework for dynamic contrast-enhanced MRI reconstruction that disentangles the underlying anatomy, the dynamic contrast enhancement, and residual motion into separate temporal basis functions, thereby enabling a geometrical interpretation of the representation.

Results

The architecture achieves performance competitive with conventional reconstruction methods, both in reconstruction quality and in the accuracy of extracted aortic and renal enhancement curves.
The modular hierarchical design naturally extends to additional dynamic factors and higher acceleration rates.

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*Auto-collected on 2026-08-20.*