Enhancement of 4-D Cone-Beam Computed Tomography (4D-CBCT) Using a Dual-Encoder Convolutional Neural Network (DeCNN)

作者：Zhuoran Jiang, Zeyu Zhang, Yushi Chang, Yun Ge, F Yin, Lei Ren · 发表于：IEEE Transactions on Radiation and Plasma Medical Sciences · 年份：2021 · DOI：10.1109/trpms.2021.3133510 · 被引用次数：21 · 研究领域：Medical Imaging Techniques and Applications、Advanced Radiotherapy Techniques、Advanced MRI Techniques and Applications

4D-CBCT is a powerful tool to provide respiration-resolved images for the moving target localization. However, projections in each respiratory phase are intrinsically under-sampled under the clinical scanning time and imaging dose constraints. Images reconstructed by compressed sensing (CS)-based methods suffer from blurred edges. Introducing the average-4D-image constraint to the CS-based reconstruction, such as prior-image-constrained CS (PICCS), can improve the edge sharpness of the stable structures. However, PICCS can lead to motion artifacts in the moving regions. In this study, we proposed a dual-encoder convolutional neural network (DeCNN) to realize the average-image-constrained 4D-CBCT reconstruction. The proposed DeCNN has two parallel encoders to extract features from both the under-sampled target phase images and the average images. The features are then concatenated and fed into the decoder for the high-quality target phase image reconstruction. The reconstructed 4D-CBCT using of the proposed DeCNN from the real lung cancer patient data showed (1) qualitatively, clear and accurate edges for both stable and moving structures; (2) quantitatively, low-intensity errors, high peak signal-to-noise ratio, and high structural similarity compared to the ground truth images; and (3) superior quality to those reconstructed by several other state-of-the-art methods including the back-projection, CS total-variation, PICCS, and the single-encoder CNN. Overall, the proposed De...