论文标题
加速磁共振成像的卷积框架
Convolutional Framework for Accelerated Magnetic Resonance Imaging
论文作者
论文摘要
磁共振成像（MRI）是一种无创成像技术，在不使用电离辐射的情况下提供精美的软组织对比度。 MRI的临床应用可能会受到长时间的数据获取时间的限制；因此，来自高度不足的K空间数据的MR图像重建一直是研究的活跃领域。许多作品利用了Hankel数据矩阵中的等级缺陷，以恢复未观察到的K空间样本。最终的问题是非凸，因此数值算法的选择会显着影响性能，计算和内存。我们提出了一种简单，可扩展的方法，称为卷积框架（CF）。我们使用来自2D，3D和动态应用的测量数据来证明CF的可行性和多功能性。
Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides exquisite soft-tissue contrast without using ionizing radiation. The clinical application of MRI may be limited by long data acquisition times; therefore, MR image reconstruction from highly undersampled k-space data has been an active area of research. Many works exploit rank deficiency in a Hankel data matrix to recover unobserved k-space samples; the resulting problem is non-convex, so the choice of numerical algorithm can significantly affect performance, computation, and memory. We present a simple, scalable approach called Convolutional Framework (CF). We demonstrate the feasibility and versatility of CF using measured data from 2D, 3D, and dynamic applications.
