College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou, China.
Key Laboratory for Electric Drive Control and Intelligent Equipment of Hunan Province, Zhuzhou, China.
J Xray Sci Technol. 2019;27(2):257-272. doi: 10.3233/XST-180419.
The total variation (TV) regularization has been widely used in statistically iterative cone-beam computed tomography (CBCT) reconstruction, showing ability to preserve object edges. However, the TV regularization can also produce staircase effect and tend to over-smooth the reconstructed images due to its piecewise constant assumption. In this study, we proposed to use the structure tensor total variation (STV) that penalizes the eigenvalues of the structure tensor for CBCT reconstruction. The STV penalty extends the TV penalty, with many important properties maintained such as convexity and rotation and translation invariance. The STV penalty utilizes gradient information more effectively and has a stronger ability to capture local image structural variation. The objective function was constructed with the penalized weighted least-square (PWLS) strategy and the gradient descent (GD) method was used to optimize the objective function. Besides, we investigated whether the norms involved in the STV penalty affected the reconstruction performance and found that the l1-norm gave the better performance than the l2-norm and l ∞-norm. We also examined performance of the STV penalties constructed using different kernel functions and found that the STV with the Gaussian kernel had the best performance, and the STVs with Uniform, Logistic, and Sigmoid kernels had similar performance to each other. We evaluated our reconstruction method with the STV penalty on computer simulated phantoms and physical phantoms. The results demonstrated that STV led to better reconstruction performance than TV, both visually and quantitatively. For the Catphan 600 physical phantom, the STV1 penalty was 175% and 623% better than the low-dose FDK and the high-dose FDK, and 14% better than the TV penalty at the matched noise level, according to the average contrast-to-noise ratio (CNR); while for the Compressed Sensing simulation phantom, the peak signal to noise ratio (PSNR) of reconstructed results using STV1, STV2, and STV ∞ were 40.67 dB, 38.72 dB, and 37.40 dB, respectively, all being significantly better than 36.84 dB using TV.
总变差(TV)正则化已广泛应用于统计迭代锥束 CT(CBCT)重建中,可用于保持目标边缘。然而,由于 TV 正则化的分段常数假设,其会产生阶梯效应,并趋于过度平滑重建图像。在这项研究中,我们提出了使用结构张量总变差(STV)对 CBCT 重建进行正则化。STV 惩罚扩展了 TV 惩罚,保留了许多重要性质,如凸性、旋转和平移不变性。STV 惩罚更有效地利用梯度信息,具有更强的捕获局部图像结构变化的能力。目标函数是通过惩罚加权最小二乘(PWLS)策略构建的,梯度下降(GD)方法用于优化目标函数。此外,我们还研究了 STV 惩罚中涉及的范数是否会影响重建性能,并发现 l1-范数的性能优于 l2-范数和 l∞-范数。我们还研究了使用不同核函数构建的 STV 惩罚的性能,发现具有高斯核的 STV 具有最佳性能,而具有均匀、逻辑和 Sigmoid 核的 STV 性能彼此相似。我们在计算机模拟的和物理的体模上使用 STV 惩罚评估了我们的重建方法。结果表明,STV 在视觉和定量方面都比 TV 具有更好的重建性能。对于 Catphan 600 物理体模,在匹配噪声水平下,STV1 惩罚比低剂量 FDK 分别高 175%和 623%,比高剂量 FDK 高 623%,比 TV 惩罚高 14%;而对于压缩感知模拟体模,使用 STV1、STV2 和 STV∞ 的重建结果的峰值信噪比(PSNR)分别为 40.67dB、38.72dB 和 37.40dB,均明显优于使用 TV 的 36.84dB。