Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI
Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI
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DOI：
10.1109/tmi.2024.3351211
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发表时间：
2024-05-01
影响因子：
10.6
通讯作者：
Zhou，Xin
中科院分区：
文献类型：
--
作者：
Li，Zimeng;Xiao，Sa;Zhou，Xin
超极化惰性气体磁共振成像（MRI）提供了一种观察人肺结构和功能的方法，但成像时间长限制了其广泛的研究和临床应用。深度学习通过从欠采样数据重建图像来加速MRI的巨大潜力。然而，大多数现有的深度卷积神经网络（CNN）直接将平方卷积应用于k空间数据，而没有考虑k空间采样的固有属性，限制了k空间学习效率和图像重建质量。在这项工作中，我们提出了一种编码增强（EN2）复杂CNN，用于高度欠采样的肺部MRI重建。EN 2复CNN采用沿频率或相位编码方向的沿着卷积，类似于k空间采样的机制，以最大化k空间的行或列内的编码相关性和完整性的利用。我们还使用复卷积来从复k空间数据中学习丰富的表示。此外，我们开发了一个功能增强的模块化单元，以进一步提高重建性能。实验表明，我们的方法可以准确地重建超极化129毫米波和1H肺MRI从6倍欠采样的k空间数据，并提供肺功能测量与完全采样图像相比，最小的偏差。这些结果证明了所提出的算法组件的有效性，并表明所提出的方法可用于研究和临床肺部疾病患者护理中的加速肺部MRI。
Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated great potential for accelerating MRI by reconstructing images from undersampled data. However, most existing deep convolutional neural networks (CNN) directly apply square convolution to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. In this work, we propose an encoding enhanced (EN2) complex CNN for highly undersampled pulmonary MRI reconstruction. EN2 complex CNN employs convolution along either the frequency or phase-encoding direction, resembling the mechanisms of k-space sampling, to maximize the utilization of the encoding correlation and integrity within a row or column of k-space. We also employ complex convolution to learn rich representations from the complex k-space data. In addition, we develop a feature-strengthened modularized unit to further boost the reconstruction performance. Experiments demonstrate that our approach can accurately reconstruct hyperpolarized 129Xe and 1H lung MRI from 6-fold undersampled k-space data and provide lung function measurements with minimal biases compared with fully sampled images. These results demonstrate the effectiveness of the proposed algorithmic components and indicate that the proposed approach could be used for accelerated pulmonary MRI in research and clinical lung disease patient care.