医学影像领域新突破：用原始表示学习重建动态对比增强MRI，无需大量训练数据。
研究人员提出了一种基于原始表示的多维框架用于动态对比增强MRI重建。该框架将解剖结构、动态对比增强和残余运动分离为不同的时间基函数，实现了几何解释。在主动脉和肾脏增强曲线提取准确性方面，该方法与传统重建方法性能相当。代码已在GitHub开源，项目名为2026-GaborDCE-spieker。
Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction
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.