Objectives: We present a new low-dose CT reconstruction method using sub-pixel and anisotropic diffusion.
Methods: The sub-pixel intensity values and their second-order differences were obtained using linear interpolation techniques, and the new gradient information was then embedded into an anisotropic diffusion process, which was introduced into a penalty-weighted least squares model to reduce the noise in low-dose CT projection data. The high-quality CT image was finally reconstructed using the classical filtered back-projection (FBP) algorithm from the estimated data.
Results: In the Shepp-Logan phantom experiments, the structural similarity (SSIM) index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 28.13%, 5.49%, and 0.91%, the feature similarity (FSIM) index was increased by 21.08%, 1.78%, and 1.36%, and the root mean square error (RMSE) was reduced by 69.59%, 18.96%, and 3.90%, respectively. In the digital XCAT phantom experiments, the SSIM index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 14.24%, 1.43% and 7.89%, the FSIM index was increased by 9.61%, 1.78% and 5.66%, and the RMSE was reduced by 26.88%, 9.41% and 18.39%, respectively. In clinical experiments, the SSIM index of the image reconstructed using the proposed algorithm was increased by 19.24%, 15.63% and 3.68%, the FSIM index was increased by 4.30%, 2.92% and 0.43%, and the RMSE was reduced by 44.60%, 36.84% and 15.22% in comparison with FBP, PWLS-Gibbs and PWLS-TV algorithms, respectively.
Conclusions: The proposed method can effectively reduce the noises and artifacts while maintaining the structural details in low-dose CT images.
目的: 提出一种基于亚像素各项异性扩散的低剂量CT重建方法。方法: 通过线性插值技术计算亚像素单元强度值及其二阶差分后，将计算得到的新的梯度信息引入到各项异性扩散过程中，并结合惩罚加权最小二乘模型对低剂量CT投影数据进行滤波，最后使用滤波反投影算法将恢复后的投影数据重建出CT图像。结果: 在Shepp-Logan体模实验中，与FBP、PWLS-Gibbs和PWLS-TV方法相比，新方法滤波后重建的CT图像在结构相似指数上分别提升了28.13%、5.49%和0.91%，在特征相似指数上分别提升了21.08%、1.78%和1.36%，并且在均方根误差上分别降低了69.59%、18.96%和3.90%。在XCAT体模实验中，与FBP、PWLS-Gibbs和PWLS-TV方法相比，新方法在结构相似指数上分别提高了14.24%、1.43%及7.89%，在特征相似指数上分别提高了9.61%、1.78%及5.66%，同时在均方根误差上分别降低了26.88%、9.41%及18.39%。在临床数据实验中，与FBP、PWLS-Gibbs和PWLS-TV方法重建的CT图像相比，新方法在结构相似指数上分别提升了19.24%、15.63%和3.68%，在特征相似指数上分别提升了4.30%、2.92%和0.43%，同时在均方根误差上分别降低了44.60%、36.84%和15.22%，并且在峰值信噪比上提升至28.39。结论: 本文提出的新方法可以有效去除低剂量CT图像的噪声和伪影，并可以保持结构细节信息。.
Keywords: anisotropic diffusion; image reconstruction; low-dose computed tomography; sub-pixel.