PDF(Pubmed)
Abstract：
This work aims to improve limited-angle (LA) cone beam computed tomography (CBCT) by developing deep learning (DL) methods for real clinical CBCT projection data, which is the first feasibility study of clinical-projection-data-based LA-CBCT, to the best of our knowledge. In radiation therapy (RT), CBCT is routinely used as the on-board imaging modality for patient setup. Compared to diagnostic CT, CBCT has a long acquisition time, e.g., 60 seconds for a full 360° rotation, which is subject to the motion artifact. Therefore, the LA-CBCT, if achievable, is of the great interest for the purpose of RT, for its proportionally reduced scanning time in addition to the radiation dose. However, LA-CBCT suffers from severe wedge artifacts and image distortions. Targeting at real clinical projection data, we have explored various DL methods such as image/data/hybrid-domain methods and finally developed a so-called Structure-Enhanced Attention Network (SEA-Net) method that has the best image quality from clinical projection data among the DL methods we have implemented. Specifically, the proposed SEA-Net employs a specialized structure enhancement sub-network to promote texture preservation. Based on the observation that the distribution of wedge artifacts in reconstruction images is non-uniform, the spatial attention module is utilized to emphasize the relevant regions while ignores the irrelevant ones, which leads to more accurate texture restoration.
摘要：
这项工作旨在通过开发用于实际临床CBCT投影数据的深度学习（DL）方法来改善有限角度（LA）锥形束计算机断层扫描（CBCT），这是第一个基于临床投影数据的LA-CBCT的可行性研究，据我们所知.在放射治疗（RT）中，CBCT通常用作患者设置的机载成像模态。与诊断性CT相比，CBCT具有较长的采集时间,例如，一个完整的360°旋转60秒，受到运动伪影的影响。因此,LA-CBCT,如果可以实现，对RT的目的非常感兴趣，除了辐射剂量外，它还按比例减少了扫描时间。然而,LA-CBCT遭受严重的楔形伪影和图像失真。针对真实的临床预测数据，我们已经探索了各种DL方法，例如图像/数据/混合域方法，并最终开发了一种所谓的结构增强注意力网络（SEA-Net）方法，该方法在我们实施的DL方法中具有来自临床投影数据的最佳图像质量。具体来说,提出的SEA-Net采用专门的结构增强子网络来促进纹理保存。观察到重建图像中楔形伪影的分布是不均匀的,空间注意模块用于强调相关区域，而忽略不相关区域，这导致更准确的纹理恢复。
