Compressed Sensing: From Research to Clinical Practice with Deep Neural Networks.
Compressed Sensing: From Research to Clinical Practice with Deep Neural Networks.
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DOI：
10.1109/msp.2019.2950433
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发表时间：
2020-01
影响因子：
14.9
通讯作者：
Vasanawala SS
中科院分区：
文献类型：
--
作者：
Sandino CM;Cheng JY;Chen F;Mardani M;Pauly JM;Vasanawala SS
压缩感知 (CS) 重建方法利用底层信号中的稀疏结构从高度欠采样的测量中恢复高分辨率图像。当应用于磁共振成像 (MRI) 时，CS 有可能显着缩短 MRI 扫描时间、提高诊断价值并改善整体患者体验。然而，CS 有几个缺点限制了其临床转化，例如：1）由于不准确的稀疏建模假设而产生的伪影，2）每个临床应用需要大量的参数调整，以及 3）临床上不可行的重建时间。最近，计算机科学已扩展到纳入深度神经网络，作为从历史考试数据中学习复杂图像先验的一种方式。这些技术通常被称为展开神经网络，已被证明是解决稀疏 CS 挑战的一种引人注目且实用的方法。在本教程中，我们将回顾经典的压缩感知公式，并概述将该公式转换为基于深度学习的重建框架所需的步骤。 Python 中的补充开源代码将用于通过开放数据库演示这种方法。此外，我们将讨论在临床环境中应用展开神经网络的注意事项。
Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying signals to recover high-resolution images from highly undersampled measurements. When applied to magnetic resonance imaging (MRI), CS has the potential to dramatically shorten MRI scan times, increase diagnostic value, and improve overall patient experience. However, CS has several shortcomings which limit its clinical translation such as: 1) artifacts arising from inaccurate sparse modelling assumptions, 2) extensive parameter tuning required for each clinical application, and 3) clinically infeasible reconstruction times. Recently, CS has been extended to incorporate deep neural networks as a way of learning complex image priors from historical exam data. Commonly referred to as unrolled neural networks, these techniques have proven to be a compelling and practical approach to address the challenges of sparse CS. In this tutorial, we will review the classical compressed sensing formulation and outline steps needed to transform this formulation into a deep learning-based reconstruction framework. Supplementary open source code in Python will be used to demonstrate this approach with open databases. Further, we will discuss considerations in applying unrolled neural networks in the clinical setting.