Yang Chunlin, Wu Pengwei, Gong Shutao, Wang Jing, Lyu Qihui, Tang Xiangyang, Niu Tianye
Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, People's Republic of China. Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang 310009, People's Republic of China.
Phys Med Biol. 2017 Oct 27;62(22):8495-8520. doi: 10.1088/1361-6560/aa8e62.
Recent advances in total variation (TV) technology enable accurate CT image reconstruction from highly under-sampled and noisy projection data. The standard iterative reconstruction algorithms, which work well in conventional CT imaging, fail to perform as expected in cone beam CT (CBCT) applications, wherein the non-ideal physics issues, including scatter and beam hardening, are more severe. These physics issues result in large areas of shading artifacts and cause deterioration to the piecewise constant property assumed in reconstructed images. To overcome this obstacle, we incorporate a shading correction scheme into low-dose CBCT reconstruction and propose a clinically acceptable and stable three-dimensional iterative reconstruction method that is referred to as the shading correction assisted iterative reconstruction. In the proposed method, we modify the TV regularization term by adding a shading compensation image to the reconstructed image to compensate for the shading artifacts while leaving the data fidelity term intact. This compensation image is generated empirically, using image segmentation and low-pass filtering, and updated in the iterative process whenever necessary. When the compensation image is determined, the objective function is minimized using the fast iterative shrinkage-thresholding algorithm accelerated on a graphic processing unit. The proposed method is evaluated using CBCT projection data of the Catphan 600 phantom and two pelvis patients. Compared with the iterative reconstruction without shading correction, the proposed method reduces the overall CT number error from around 200 HU to be around 25 HU and increases the spatial uniformity by a factor of 20 percent, given the same number of sparsely sampled projections. A clinically acceptable and stable iterative reconstruction algorithm for CBCT is proposed in this paper. Differing from the existing algorithms, this algorithm incorporates a shading correction scheme into the low-dose CBCT reconstruction and achieves more stable optimization path and more clinically acceptable reconstructed image. The method proposed by us does not rely on prior information and thus is practically attractive to the applications of low-dose CBCT imaging in the clinic.
全变差（TV）技术的最新进展使得能够从高度欠采样和有噪声的投影数据中精确重建CT图像。标准的迭代重建算法在传统CT成像中效果良好，但在锥束CT（CBCT）应用中却未能达到预期效果，因为在CBCT中，包括散射和束硬化在内的非理想物理问题更为严重。这些物理问题会导致大面积的阴影伪影，并使重建图像中假设的分段恒定特性恶化。为了克服这一障碍，我们将阴影校正方案纳入低剂量CBCT重建中，并提出了一种临床可接受且稳定的三维迭代重建方法，即阴影校正辅助迭代重建。在所提出的方法中，我们通过在重建图像中添加阴影补偿图像来修改TV正则化项，以补偿阴影伪影，同时保持数据保真项不变。该补偿图像通过图像分割和低通滤波凭经验生成，并在迭代过程中必要时进行更新。当确定补偿图像后，使用在图形处理单元上加速的快速迭代收缩阈值算法将目标函数最小化。我们使用Catphan 600体模和两名骨盆患者的CBCT投影数据对所提出的方法进行了评估。与未进行阴影校正的迭代重建相比，在相同数量的稀疏采样投影下，所提出的方法将整体CT数值误差从约200 HU降低到约25 HU，并将空间均匀性提高了20%。本文提出了一种临床可接受且稳定的CBCT迭代重建算法。与现有算法不同，该算法将阴影校正方案纳入低剂量CBCT重建中，实现了更稳定的优化路径和更符合临床要求的重建图像。我们提出的方法不依赖先验信息，因此在临床低剂量CBCT成像应用中具有实际吸引力。
