Intelligent Parameter Tuning in Optimization-Based Iterative CT Reconstruction via Deep Reinforcement Learning.
Intelligent Parameter Tuning in Optimization-Based Iterative CT Reconstruction via Deep Reinforcement Learning.
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DOI：
10.1109/tmi.2018.2823679
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发表时间：
2018-06
影响因子：
10.6
通讯作者：
Jia X
中科院分区：
文献类型：
--
作者：
Shen C;Gonzalez Y;Chen L;Jiang SB;Jia X
许多图像处理问题可以用公式表示为优化问题。目标函数通常包含几个专门为不同目的设计的项。这些术语前面的参数用于控制它们之间的相对重要性。调整这些参数至关重要，因为解决方案的质量取决于它们的值。调整参数对于人类来说是相对简单的任务，因为人们可以基于解的质量直观地确定参数调整的方向。然而，手动参数调整不仅在许多情况下是乏味的，但变得不切实际的，当一些参数存在于一个问题。为了解决这个问题，本文提出了一种采用深度强化学习来训练系统的方法，该系统可以像人类一样自动调整参数。我们证明了我们的想法，在一个例子中的问题，基于优化的迭代CT重建与逐像素的总变差正则化项。我们建立了一个参数调整策略网络（PTPN），它将CT图像补丁映射到一个输出，该输出指定了补丁中心参数的调整方向和幅度。我们通过端到端的强化学习过程来训练PTPN。我们证明，在训练的PTPN的指导下，重建的CT图像达到类似或优于手动调整参数重建的质量。
A number of image-processing problems can be formulated as optimization problems. The objective function typically contains several terms specifically designed for different purposes. Parameters in front of these terms are used to control the relative importance among them. It is of critical importance to adjust these parameters, as quality of the solution depends on their values. Tuning parameters is a relatively straightforward task for a human, as one can intuitively determine the direction of parameter adjustment based on the solution quality. Yet manual parameter tuning is not only tedious in many cases, but becomes impractical when a number of parameters exist in a problem. Aiming at solving this problem, this paper proposes an approach that employs deep reinforcement learning to train a system that can automatically adjust parameters in a human-like manner. We demonstrate our idea in an example problem of optimization-based iterative CT reconstruction with a pixel-wise total-variation regularization term. We set up a Parameter-Tuning Policy Network (PTPN), which maps a CT image patch to an output that specifies the direction and amplitude by which the parameter at the patch center is adjusted. We train the PTPN via an end-to-end reinforcement learning procedure. We demonstrate that under the guidance of the trained PTPN, reconstructed CT images attain quality similar or better than those reconstructed with manually tuned parameters.