Statistical Iterative CBCT Reconstruction Based on Neural Network.
Statistical Iterative CBCT Reconstruction Based on Neural Network.
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基于神经网络的统计迭代CBCT重建
DOI：
10.1109/tmi.2018.2829896
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发表时间：
2018-06
影响因子：
10.6
通讯作者：
Tan S
中科院分区：
文献类型：
--
作者：
Chen B;Xiang K;Gong Z;Wang J;Tan S
锥形束计算机断层扫描（CBCT）在放射治疗中起着重要的作用。特别设计惩罚项的统计迭代重建（SIR）算法为低剂量CBCT成像提供了良好的性能。其中，总变差（TV）惩罚是目前去除噪声和保持边缘的最先进方法，但它的一个众所周知的局限性是它的阶梯效应。最近，人们提出了各种带有高阶微分算子的罚项来代替TV罚项，以避免阶梯效应，但代价是物体边缘会稍微模糊。我们开发了一种新的SIR算法，使用神经网络进行CBCT重建。我们使用数据驱动的方法来学习“潜在正则化项”，而不是手动设计惩罚项。该方法将传统统计迭代框架中惩罚项的设计问题转化为设计和训练适合CBCT重建的神经网络。我们提出了利用迁移学习来克服数据不足的问题，并提出了一种专门针对CBCT迭代重建过程中重建图像的噪声水平和分辨率可能发生变化而设计的迭代去模糊方法。通过对两个物理模型、两个模拟数字模型和患者数据的实验，我们证明了基于网络的SIR在视觉和定量上用于CBCT重建的优异性能。该方法克服了阶梯效应，保留了边缘和强度平滑过渡的区域，提供了高分辨率和低噪声水平的重建结果。
Cone-beam computed tomography (CBCT) plays an important role in radiation therapy. Statistical iterative reconstruction (SIR) algorithms with specially designed penalty terms provide good performance for low-dose CBCT imaging. Among others, the total variation (TV) penalty is the current state-of-the-art in removing noises and preserving edges, but one of its well-known limitations is its staircase effect. Recently, various penalty terms with higher order differential operators were proposed to replace the TV penalty to avoid the staircase effect, at the cost of slightly blurring object edges. We developed a novel SIR algorithm using a neural network for CBCT reconstruction. We used a data-driven method to learn the “potential regularization term” rather than design a penalty term manually. This approach converts the problem of designing a penalty term in the traditional statistical iterative framework to designing and training a suitable neural network for CBCT reconstruction. We proposed using transfer learning to overcome the data deficiency problem and an iterative deblurring approach specially designed for the CBCT iterative reconstruction process during which the noise level and resolution of the reconstructed images may change. Through experiments conducted on two physical phantoms, two simulation digital phantoms, and patient data, we demonstrated the excellent performance of the proposed network-based SIR for CBCT reconstruction, both visually and quantitatively. Our proposed method can overcome the staircase effect, preserve both edges and regions with smooth intensity transition, and provide reconstruction results at high resolution and low noise level.