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.

翻译：以次空间限制的图像重建的非卡泰西亚取样是一种流行的动态磁共振法,但缓慢的迭代重建限制了它的临床应用。数据一致性(DC)深层学习可以以良好的图像质量加速重建,但还没有为非卡提亚空间成像制定出非卡提亚空间成像。在这项研究中,我们建议为快速、准确、动态的MR图像重建建立一个非卡提亚空深层亚空间学习框架。开发和评价了四种新的DC配方:两种梯度平整法,一种直接解决的方法,一种共生梯度法。我们应用了一个有和没有DC层的U-Net模型来重建心脏MyMultasking的T1加权图像(一种先进的多维成像法),将我们的结果与迭代再版参考进行比较。实验结果表明,拟议的框架大大提高了没有DC的U-Net模型的重建精确度,同时大大加快常规迭代重建的重建工作。