论文标题
数据一致的非科学家深度子空间学习，以进行有效的动态MR图像重建
Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction
论文作者
论文摘要
通过子空间约束的图像重建是一种流行的动态MRI方法，但迭代性重建缓慢限制了其临床应用。数据一致性（DC）的深度学习可以以良好的图像质量加速重建，但尚未用于非艺术亚空间成像。在这项研究中，我们提出了一个直流非 - 卡契式深度子空间学习框架，以进行快速，准确的动态MR图像重建。开发和评估了四种新型的直流公式：两种梯度不错的方法，一种直接解决的方法和一种共轭梯度方法。我们应用了带有和没有直流图的U-NET模型，以重建心脏MR多任务的T1加权图像（一种先进的多维成像方法），将我们的结果与迭代重建的参考进行了比较。实验结果表明，所提出的框架显着提高了U-NET模型的重建精度，而无需DC，同时显着加速了对常规迭代重建的重建。
Non-Cartesian sampling with subspace-constrained image reconstruction is a popular approach to dynamic MRI, but slow iterative reconstruction limits its clinical application. Data-consistent (DC) deep learning can accelerate reconstruction with good image quality, but has not been formulated for non-Cartesian subspace imaging. In this study, we propose a DC non-Cartesian deep subspace learning framework for fast, accurate dynamic MR image reconstruction. Four novel DC formulations are developed and evaluated: two gradient decent approaches, a directly solved approach, and a conjugate gradient approach. We applied a U-Net model with and without DC layers to reconstruct T1-weighted images for cardiac MR Multitasking (an advanced multidimensional imaging method), comparing our results to the iteratively reconstructed reference. Experimental results show that the proposed framework significantly improves reconstruction accuracy over the U-Net model without DC, while significantly accelerating the reconstruction over conventional iterative reconstruction.
