Peng Junbo, Wang Tonghe, Xie Huiqiao, Qiu Richard L J, Chang Chih-Wei, Roper Justin, Yu David S, Tang Xiangyang, Yang Xiaofeng
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States of America.
Phys Med Biol. 2025 Jul 11;70(14):145010. doi: 10.1088/1361-6560/ade843.
. Limited-angle dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without hardware modification. However, its clinical implementations are hindered by the challenging image reconstruction from limited-angle projections. While optimization-based and deep learning-based methods have been proposed for image reconstruction, their utilization is limited by the requirement for x-ray spectra measurement or paired datasets for model training. This work aims to facilitate the clinical applications of fast and low-dose DE-CBCT by developing a practical solution for image reconstruction in limited-angle DE-CBCT.. An inter-spectral structural similarity-based regularization was integrated into the iterative image reconstruction in limited-angle DE-CBCT. By enforcing the similarity between the DE images, limited-angle artifacts were efficiently reduced in the reconstructed DECBCT images. The proposed method was evaluated using two physical phantoms and three digital phantoms, demonstrating its efficacy in quantitative DECBCT imaging.. In all the studies, the proposed method achieves accurate image reconstruction without visible residual artifacts from limited-angle DE-CBCT projection data. In the digital phantom studies, the proposed method reduces the mean-absolute-error from 309/290 HU to 14/20 HU, increases the peak signal-to-noise ratio from 40/39 dB to 70/67 dB, and improves the structural similarity index measurement from 0.74/0.72-1.00/1.00.. The proposed method can efficiently reduce limited-angle artifacts during the image reconstruction, enabling quantitative DE-CBCT with comparable data acquisition time and radiation dose to that of a single-energy scan on current onboard scanners without hardware modification. This work is of great clinical significance and can boost the clinical application of DE-CBCT in image-guided radiation therapy and surgical interventions.
有限角度双能（DE）锥束CT（CBCT）被认为是在当前CBCT扫描仪上实现快速低剂量DE成像且无需硬件修改的潜在解决方案。然而，其临床应用受到有限角度投影图像重建难题的阻碍。虽然已提出基于优化和基于深度学习的方法用于图像重建，但它们的应用受到X射线光谱测量要求或模型训练所需配对数据集的限制。这项工作旨在通过开发有限角度DE - CBCT图像重建的实用解决方案，促进快速低剂量DE - CBCT的临床应用。在有限角度DE - CBCT的迭代图像重建中集成了基于光谱间结构相似性的正则化。通过强制DE图像之间的相似性，在重建的DE - CBCT图像中有效减少了有限角度伪影。使用两个物理体模和三个数字体模对所提出的方法进行了评估，证明了其在定量DE - CBCT成像中的有效性。在所有研究中，所提出的方法从有限角度DE - CBCT投影数据实现了准确的图像重建，且无可见残留伪影。在数字体模研究中，所提出的方法将平均绝对误差从309/290 HU降低到14/20 HU，将峰值信噪比从40/39 dB提高到70/67 dB，并将结构相似性指数测量从0.74/0.72提高到1.00/1.00。所提出的方法能够在图像重建过程中有效减少有限角度伪影，在数据采集时间和辐射剂量与当前机载扫描仪的单能扫描相当且无需硬件修改的情况下，实现定量DE - CBCT。这项工作具有重要的临床意义，可推动DE - CBCT在图像引导放射治疗和手术干预中的临床应用。
