Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction

2026-08-18

AI Summary

本文提出一种基于多维基元框架的方法，用于动态对比增强MRI重建，解决了高质量时空重建的问题，无需大量训练数据。

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. We 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. We show that this architecture achieves performance competitive with conventional reconstruction methods, both in reconstruction quality and in the accuracy of extracted aorta and kidney enhancement curves. The modular tier design extends naturally to additional dynamic factors and higher acceleration rates. Code available at https://github.com/compai-lab/ 2026-GaborDCE-spieker.

Problem

• dynamic contrast-enhanced MRI
• spatiotemporal reconstructions
• undersampling rates
• primitive based framework

Innovation

• Primitive Representation Learning
• Dynamic Contrast-Enhanced MRI
• Spatiotemporal Reconstruction
• Temporal Basis Functions
• High Undersampling Rates