Key Lab of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi'an, China.
J Xray Sci Technol. 2018;26(2):227-240. doi: 10.3233/XST-17285.
This study aims to investigate and test a new image reconstruction algorithm applying to the low-signal projections to generate high quality images by reducing the artifacts and noise in the cone-beam computed tomography (CBCT). For the low-signal and noisy projections, a multiple sampling method is first utilized in projection domain to suppress environmental noise, which guarantees the accuracy of the data for reconstruction, simultaneously. Next, a fuzzy entropy based method with block matching 3D (BM3D) filtering algorithm is employed to improve the image quality to reduce artifacts and noise in image domain. Then, simulation studies on polychromatic spectrum were performed to evaluate the performance of the proposed new algorithm. Study results demonstrated significant improvement in the signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images reconstructed using the new algorithm. SNRs and CNRs of the new images were averagely 40% and 20% higher than those of the previous images reconstructed using the traditional algorithms, respectively. As a result, since the new image reconstruction algorithm effectively reduced the artifacts and noise, and produced images with better contour and grayscale distribution, it has the potential to improve image quality using the original CBCT data with the low and missing signals.
本研究旨在调查和测试一种新的图像重建算法，应用于低信号投影，通过减少锥束 CT（CBCT）中的伪影和噪声来生成高质量的图像。对于低信号和噪声投影，首先在投影域中利用多次采样方法来抑制环境噪声，同时保证重建数据的准确性。接下来，采用基于模糊熵的方法和块匹配 3D（BM3D）滤波算法来提高图像质量，以减少图像域中的伪影和噪声。然后，对多光谱进行了模拟研究，以评估所提出的新算法的性能。研究结果表明，使用新算法重建的图像的信噪比（SNR）和对比噪声比（CNR）有了显著提高。使用新算法重建的新图像的 SNR 和 CNR 平均比使用传统算法重建的先前图像分别提高了 40%和 20%。因此，由于新的图像重建算法有效地减少了伪影和噪声，并产生了具有更好轮廓和灰度分布的图像，因此有可能使用低信号和缺失信号的原始 CBCT 数据来提高图像质量。
