CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction
CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction
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DOI：
10.1109/tmi.2018.2832656
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发表时间：
2018-06-01
影响因子：
10.6
通讯作者：
Unser, Michael
中科院分区：
文献类型：
--
作者：
Gupta, Harshit;Jin, Kyong Hwan;Unser, Michael
我们提出了一种新的图像重建方法，用卷积神经网络（CNN）代替投影梯度下降（PGD）中的投影器。最近，cnn作为图像到图像的回归量被成功地用于解决成像中的逆问题。然而，与现有的迭代图像重建算法不同，这些基于cnn的方法通常缺乏反馈机制来强制重建图像与测量值一致。我们提出了一个宽松版本的PGD，其中梯度下降强制测量一致性，而CNN递归地将解决方案投影到更接近所需重建图像的空间。证明了该算法是保证收敛的，并在一定条件下收敛于非凸逆问题的局部极小值。最后，我们提出了一个简单的方案来训练CNN像投影仪一样工作。我们在稀疏视图计算机断层扫描重建上的实验表明，与基于总变分的正则化、字典学习和最先进的基于深度学习的直接重建技术相比，该方法有所改进。
We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained as image-to-image regressors have been successfully used to solve inverse problems in imaging. However, unlike existing iterative image reconstruction algorithms, these CNN-based approaches usually lack a feedback mechanism to enforce that the reconstructed image is consistent with the measurements. We propose a relaxed version of PGD wherein gradient descent enforces measurement consistency, while a CNN recursively projects the solution closer to the space of desired reconstruction images. We show that this algorithm is guaranteed to converge and, under certain conditions, converges to a local minimum of a non-convex inverse problem. Finally, we propose a simple scheme to train the CNN to act like a projector. Our experiments on sparse-view computed-tomography reconstruction show an improvement over total variation-based regularization, dictionary learning, and a state-of-the-art deep learning-based direct reconstruction technique.