基于鲁棒主成分分析（Robust PCA）的对比度无关分组配准用于定量心脏MRI
Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI
AI总结：
本文提出将基于CNN的分组配准主干集成到rPCA框架中，利用低秩定量映射与稀疏残余运动分解，解决心脏定量MRI中对比度变化及心、呼吸运动导致的配准难题。
摘要：
定量心脏磁共振成像（MRI）正成为心血管疾病日益重要的诊断工具。然而，对定量MRI序列内所有基线图像进行协同配准，对于定量图谱的准确性和精确性至关重要。但是，由于强度和对比度同时发生变化，并叠加心脏运动和呼吸运动，对来自定量心脏MRI序列的所有基线图像进行协同配准仍然是一项非平凡的任务。为应对这一挑战，我们提出了一种基于鲁棒主成分分析（rPCA）的新型运动校正框架，该框架将定量心脏MRI分解为低秩分量和稀疏分量，并将基于CNN的分组配准主干网络集成到rPCA框架中。rPCA的低秩分量对应于定量映射（即变化自由度有限），而稀疏分量对应于残余运动，从而使分组配准问题更易于建模和求解。我们在钆对比剂给药前后，通过改良Look-Locker反转恢复（MOLLI）序列在心脏T1 mapping上评估了所提出的方法。实验表明，与未引入rPCA的基线方法相比，我们的方法有效提升了配准性能，并在域内（对比剂前MOLLI）和域外（对比剂后MOLLI）推理中均降低了定量映射误差。所提出的rPCA框架具有通用性，可与其他配准主干网络集成。
英文摘要：
Quantitative cardiac magnetic resonance imaging (MRI) is an increasingly important diagnostic tool for cardiovascular diseases. Yet, co-registration of all baseline images within the quantitative MRI sequence is essential for the accuracy and precision of quantitative maps. However, co-registering all baseline images from a quantitative cardiac MRI sequence remains a nontrivial task because of the simultaneous changes in intensity and contrast, in combination with cardiac and respiratory motion. To address the challenge, we propose a novel motion correction framework based on robust principle component analysis (rPCA) that decomposes quantitative cardiac MRI into low-rank and sparse components, and we integrate the groupwise CNN-based registration backbone within the rPCA framework. The low-rank component of rPCA corresponds to the quantitative mapping (i.e. limited degree of freedom in variation), while the sparse component corresponds to the residual motion, making it easier to formulate and solve the groupwise registration problem. We evaluated our proposed method on cardiac T1 mapping by the modified Look-Locker inversion recovery (MOLLI) sequence, both before and after the Gadolinium contrast agent administration. Our experiments showed that our method effectively improved registration performance over baseline methods without introducing rPCA, and reduced quantitative mapping error in both in-domain (pre-contrast MOLLI) and out-of-domain (post-contrast MOLLI) inference. The proposed rPCA framework is generic and can be integrated with other registration backbones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
