Solomon Eddy, Bae Jonghyun, Moy Linda, Heacock Laura, Feng Li, Kim Sungheon Gene
Department of Radiology, Weill Cornell Medical College, New York, NY, United States.
Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University, New York, NY, United States.
Res Sq. 2025 Feb 27:rs.3.rs-5448452. doi: 10.21203/rs.3.rs-5448452/v1.
MRI is the most effective method for screening high-risk breast cancer patients. While current exams primarily rely on the qualitative evaluation of morphological features before and after contrast administration and less on contrast kinetic information, the latest developments in acquisition protocols aim to combine both. However, balancing between spatial and temporal resolution poses a significant challenge in dynamic MRI. Here, we propose a radial MRI reconstruction framework for Dynamic Contrast Enhanced (DCE) imaging, which offers a joint solution to existing spatial and temporal MRI limitations. It leverages a locally low-rank (LLR) subspace model to represent spatially localized dynamics based on tissue information. Our framework demonstrated substantial improvement in CNR, noise reduction and enables a flexible temporal resolution, ranging from a few seconds to 1-second, aided by a neural network, resulting in images with reduced undersampling penalties. Finally, our reconstruction framework also shows potential benefits for head and neck, and brain MRI applications, making it a viable alternative for a range of DCE-MRI exams.
磁共振成像（MRI）是筛查高危乳腺癌患者的最有效方法。虽然目前的检查主要依赖于对比剂注射前后形态学特征的定性评估，而较少依赖对比剂动力学信息，但采集协议的最新进展旨在将两者结合起来。然而，在动态MRI中平衡空间和时间分辨率是一项重大挑战。在此，我们提出了一种用于动态对比增强（DCE）成像的径向MRI重建框架，该框架为现有的空间和时间MRI局限性提供了联合解决方案。它利用局部低秩（LLR）子空间模型基于组织信息来表示空间局部动态。我们的框架在对比噪声比（CNR）、降噪方面有显著改善，并借助神经网络实现了从几秒到1秒的灵活时间分辨率，从而减少了欠采样惩罚的图像。最后，我们的重建框架对头颈部和脑MRI应用也显示出潜在益处，使其成为一系列DCE-MRI检查的可行替代方案。
