Aggarwal Hemant K, Mani Merry P, Jacob Mathews
University of Iowa, Iowa, USA.
Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:1541-1544. doi: 10.1109/isbi.2019.8759514. Epub 2019 Jul 11.
We propose a model-based deep learning architecture for the correction of phase errors in multishot diffusion-weighted echo-planar MRI images. This work is a generalization of MUSSELS, which is a structured low-rank algorithm. We show that an iterative reweighted least-squares implementation of MUSSELS resembles the model-based deep learning (MoDL) framework. We propose to replace the self-learned linear filter bank in MUSSELS with a convolutional neural network, whose parameters are learned from exemplary data. The proposed algorithm reduces the computational complexity of MUSSELS by several orders of magnitude, while providing comparable image quality.
我们提出了一种基于模型的深度学习架构，用于校正多激发扩散加权回波平面磁共振成像（MRI）图像中的相位误差。这项工作是对MUSSELS的推广，MUSSELS是一种结构化低秩算法。我们表明，MUSSELS的迭代加权最小二乘实现类似于基于模型的深度学习（MoDL）框架。我们建议用卷积神经网络取代MUSSELS中的自学习线性滤波器组，其参数从示例数据中学习。所提出的算法将MUSSELS的计算复杂度降低了几个数量级，同时提供了相当的图像质量。
