[论文] Primitive Representation Learning for Unsupervised Dynamic Contrast En...

论文概要

研究领域: CV 作者: Veronika Spieker, Wenqi Huang, Cemre Ariyurek, Liam Timms, Daniel Rueckert, Onur Afacan, Julia A. Schnabel, Sila Kurugol 发布时间: 2026-08-18 arXiv: 2608.18055

中文摘要

动态对比增强MRI的可靠定量分析需要高质量时空重建，特别是在高欠采样率下。使用高斯和Gabor基元的扫描特定重建已显示出有希望的结果，无需大型训练数据集，但尚未解决动态对比的额外维度。我们提出了一种多维的、基于基元的动态对比增强MRI重建框架，将底层解剖结构、动态对比增强和残余运动解耦到单独的时间基函数中，从而实现对表示的几何解释。我们表明，这种架构在重建质量和提取的主动脉和肾脏增强曲线准确性方面达到了与传统重建方法相当的性能。模块化层级设计自然地扩展到额外的动态因素和更高的加速率。代码可在 https://github.com/compai-lab/2026-GaborDCE-spieker 获取。

原文摘要

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质量和提...

--- *自动采集于 2026-08-20*

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