IEEE Trans Med Imaging. 2021 Jul;40(7):1792-1804. doi: 10.1109/TMI.2021.3066318. Epub 2021 Jun 30.
Limited view tomographic reconstruction aims to reconstruct a tomographic image from a limited number of projection views arising from sparse view or limited angle acquisitions that reduce radiation dose or shorten scanning time. However, such a reconstruction suffers from severe artifacts due to the incompleteness of sinogram. To derive quality reconstruction, previous methods use UNet-like neural architectures to directly predict the full view reconstruction from limited view data; but these methods leave the deep network architecture issue largely intact and cannot guarantee the consistency between the sinogram of the reconstructed image and the acquired sinogram, leading to a non-ideal reconstruction. In this work, we propose a cascaded residual dense spatial-channel attention network consisting of residual dense spatial-channel attention networks and projection data fidelity layers. We evaluate our methods on two datasets. Our experimental results on AAPM Low Dose CT Grand Challenge datasets demonstrate that our algorithm achieves a consistent and substantial improvement over the existing neural network methods on both limited angle reconstruction and sparse view reconstruction. In addition, our experimental results on Deep Lesion datasets demonstrate that our method is able to generate high-quality reconstruction for 8 major lesion types.
有限视角断层重建旨在从稀疏视角或有限角度采集的有限数量投影视图中重建断层图像，以减少辐射剂量或缩短扫描时间。然而，由于射线照相不完全，这种重建会受到严重的伪影影响。为了获得高质量的重建，以前的方法使用类似于 UNet 的神经网络架构直接从有限视角数据预测全视角重建；但是这些方法在很大程度上保留了深层网络架构问题，并且不能保证重建图像的射线照相与采集的射线照相之间的一致性，导致不理想的重建。在这项工作中，我们提出了一个由残差密集空间通道注意网络组成的级联残差密集空间通道注意网络，以及投影数据保真度层。我们在两个数据集上评估了我们的方法。我们在 AAPM 低剂量 CT 大挑战数据集上的实验结果表明，我们的算法在有限角度重建和稀疏视图重建方面都比现有的神经网络方法有一致且显著的改进。此外，我们在 Deep Lesion 数据集上的实验结果表明，我们的方法能够为 8 种主要病变类型生成高质量的重建。
