Do CNNs Solve the CT Inverse Problem?
Do CNNs Solve the CT Inverse Problem?
复制标题
卷积神经网络解决了CT逆问题吗？
DOI：
10.1109/tbme.2020.3020741
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发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Pan X
中科院分区：
文献类型：
--
作者：
Sidky EY;Lorente I;Brankov JG;Pan X
这项工作研究了文献中提出的主张，即与稀疏视图计算机断层扫描（CT）中的图像重建相关的逆问题可以用卷积神经网络（CNN）解决。训练和测试图像/数据对生成在一个专用的乳腺CT模拟稀疏视图采样，使用两个不同的对象模型。测试训练后的CNN，看看图像是否可以从相应的稀疏视图数据中准确恢复。作为参考，通过使用约束全变差（TV）最小化（TVmin）来重建相同的稀疏视图CT数据，其利用梯度幅度图像（GMI）中的稀疏性。使用CNN获得的图像与生成数据的图像之间存在显著差异。TVmin能够准确地重建测试图像。我们发现，稀疏视图CT逆问题无法解决特定的出版CNN为基础的方法，我们选择和特定的对象模型，我们测试。CNN无法解决与稀疏视图CT相关的逆问题，对于所提出的模拟的特定条件，这使得人们对使用CNN和深度学习解决医学成像中的逆问题提出了类似的不受支持的要求。
This work examines the claim made in the literature that the inverse problem associated with image reconstruction in sparse-view computed tomography (CT) can be solved with a convolutional neural network (CNN). Training and testing image/data pairs are generated in a dedicated breast CT simulation for sparse-view sampling, using two different object models. The trained CNN is tested to see if images can be accurately recovered from their corresponding sparse-view data. For reference, the same sparse-view CT data is reconstructed by the use of constrained total-variation (TV) minimization (TVmin), which exploits sparsity in the gradient magnitude image (GMI). There is a significant discrepancy between the image obtained with the CNN and the image that generated the data. TVmin is able to accurately reconstruct the test images. We find that the sparse-view CT inverse problem cannot be solved for the particular published CNN-based methodology that we chose and the particular object model that we tested. The inability of the CNN to solve the inverse problem associated with sparse-view CT, for the specific conditions of the presented simulation, draws into question similar unsupported claims being made for the use of CNNs and deep-learning to solve inverse problems in medical imaging.