Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
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发表时间：
2016-07
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通讯作者：
Hanming Zhang;Liang Li;Kai Qiao;Linyuan Wang;Bin Yan;Lei Li;Guoen Hu
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作者：
Hanming Zhang;Liang Li;Kai Qiao;Linyuan Wang;Bin Yan;Lei Li;Guoen Hu
有限角度问题是X射线计算机层析成像(CT)领域的一个具有挑战性的问题。利用附加先验的迭代重建方法可以抑制伪影和改善图像质量，但遗憾的是需要增加计算时间。一种有趣的方法是抑制由实用的滤波反投影(FBP)方法重建的图像中的伪影。弗里克尔和昆托已经证明，FBP结果中的条纹伪影是可以表征的。这表明FBP方法产生的伪影在静止的有限角度扫描构型中具有特定而相似的特征。基于这一认识，本工作旨在开发一种用于有限角度层析成像的FBP重建特定伪影的提取和抑制方法。提出了一种基于深度卷积神经网络的数据驱动学习方法。学习FBP图像和无伪影图像之间的端到端映射，通过非线性映射来提取和抑制包含伪影的隐含特征。实验结果的定性和定量评估表明，该方法在有限角度层析成像的伪影去除和细节恢复方面具有稳定和预期的性能。该策略为提高有限投影数据重建结果的图像质量提供了一种简单有效的方法。
Limited angle problem is a challenging issue in x-ray computed tomography (CT) field. Iterative reconstruction methods that utilize the additional prior can suppress artifacts and improve image quality, but unfortunately require increased computation time. An interesting way is to restrain the artifacts in the images reconstructed from the practical filtered back projection (FBP) method. Frikel and Quinto have proved that the streak artifacts in FBP results could be characterized. It indicates that the artifacts created by FBP method have specific and similar characteristics in a stationary limited-angle scanning configuration. Based on this understanding, this work aims at developing a method to extract and suppress specific artifacts of FBP reconstructions for limited-angle tomography. A data-driven learning-based method is proposed based on a deep convolutional neural network. An end-to-end mapping between the FBP and artifact-free images is learned and the implicit features involving artifacts will be extracted and suppressed via nonlinear mapping. The qualitative and quantitative evaluations of experimental results indicate that the proposed method show a stable and prospective performance on artifacts reduction and detail recovery for limited angle tomography. The presented strategy provides a simple and efficient approach for improving image quality of the reconstruction results from limited projection data.