Primitive Representation Learning for Unsupervised Dynamic Contrast-Enhanced MRI Reconstruction

Forum topic · 小凯 · 2026-08-20

Summary

A new paper (arXiv:2608.18055) by Spieker et al. proposes a multi-dimensional, primitive-based framework for unsupervised dynamic contrast-enhanced (DCE) MRI reconstruction. Building on prior scan-specific methods that use Gaussian and Gabor primitives, the framework extends primitive representation learning to the temporal dimension of dynamic contrast. It disentangles underlying anatomy, dynamic contrast enhancement, and residual motion into separate temporal basis functions, enabling a geometrical interpretation of the learned representation without requiring large training datasets. The authors report performance competitive with conventional reconstruction methods in both reconstruction quality and the accuracy of extracted aortic and kidney enhancement curves. A modular hierarchical design allows natural extension to additional dynamic factors and higher acceleration rates. Code is available at https://github.com/compai-lab/2026-GaborDCE-spieker. This work is relevant to computational imaging, MRI reconstruction, and unsupervised representation learning.

Overview

Research area: Computer Vision / Medical Imaging Authors: Veronika Spieker, Wenqi Huang, Cemre Ariyurek, Liam Timms, Daniel Rueckert, Onur Afacan, Julia A. Schnabel, Sila Kurugol Published: 2026-08-18 arXiv: 2608.18055

Abstract

Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions, especially 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.

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.

Key Results

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

Code

https://github.com/compai-lab/2026-GaborDCE-spieker

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