论文标题
数据一致的CT重建来自数据不足的数据
Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images
论文作者
论文摘要
来自数据不足的图像重建在计算机断层扫描(CT)中是常见的,例如,来自截短数据,有限角度数据和稀疏视图数据的图像重建。深度学习在这一领域取得了令人印象深刻的成果。但是,由于以下两个挑战,深度学习方法的鲁棒性仍然是临床应用的关注: b)深度学习模型对噪声敏感。因此,仅神经网络处理的图像质量仅是不足的。在这项工作中,我们通过显示假阴性和假阳性病变病例来研究CT图像重建中深度学习的鲁棒性。由于具有不正确结构的基于学习的图像可能与测量的投影数据不一致,因此我们提出了一个一致的数据重建方法(DCR)方法来改善其图像质量,这结合了压缩感测和深度学习的优势:首先,先验图像是通过深度学习而产生的。之后,未测量的投影数据被先前图像的正向投影所构成。最后,应用具有重新加权的总变化正则化的迭代重建,将数据一致性整合到了测量数据的数据一致性,并为缺失的数据提供了学到的先验信息。该方法的疗效分别在锥形束CT中,分别具有截短的数据,有限角度的数据和稀疏视图数据。例如,对于截短的数据,DCR在嘈杂的情况下,DCR达到了24 HU的平均根平方误差,在视野视野内为0.999的平均结构相似性指数为0.999,而最先进的U-NET方法分别实现了这两个计数的55 HU和0.995。
Image reconstruction from insufficient data is common in computed tomography (CT), e.g., image reconstruction from truncated data, limited-angle data and sparse-view data. Deep learning has achieved impressive results in this field. However, the robustness of deep learning methods is still a concern for clinical applications due to the following two challenges: a) With limited access to sufficient training data, a learned deep learning model may not generalize well to unseen data; b) Deep learning models are sensitive to noise. Therefore, the quality of images processed by neural networks only may be inadequate. In this work, we investigate the robustness of deep learning in CT image reconstruction by showing false negative and false positive lesion cases. Since learning-based images with incorrect structures are likely not consistent with measured projection data, we propose a data consistent reconstruction (DCR) method to improve their image quality, which combines the advantages of compressed sensing and deep learning: First, a prior image is generated by deep learning. Afterwards, unmeasured projection data are inpainted by forward projection of the prior image. Finally, iterative reconstruction with reweighted total variation regularization is applied, integrating data consistency for measured data and learned prior information for missing data. The efficacy of the proposed method is demonstrated in cone-beam CT with truncated data, limited-angle data and sparse-view data, respectively. For example, for truncated data, DCR achieves a mean root-mean-square error of 24 HU and a mean structure similarity index of 0.999 inside the field-of-view for different patients in the noisy case, while the state-of-the-art U-Net method achieves 55 HU and 0.995 respectively for these two metrics.