Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.
Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.
复制标题
DOI：
10.1002/mp.12671
复制
发表时间：
2018-01
期刊：
影响因子：
3.8
通讯作者：
Yin FF
中科院分区：
文献类型：
--
作者：
Gao H;Zhang Y;Ren L;Yin FF
这项工作的目的是生成电影CT图像(即，高时间分辨率的4D图像)基于一种新的主成分重建(PCR)技术和运动学习从2D透视训练图像。在提出的PCR方法中，矩阵分解被用作4D图像的显式低阶正则化，该4D图像被表示为空间主分量和时间运动系数的乘积。聚合酶链式反应的关键假设是，4D图像的时间系数可以由从2D透视训练投影学习的时间系数合理地近似。为此，我们可以在固定的门架角度上获得几个呼吸周期的透视训练投影，这些投影没有由于门架旋转而导致的几何失真，即基于透视的运动学习。这样的训练投影可以提供呼吸运动的有效表征。可以从这些训练投影中提取时间系数，并将其用作PCR的先验，即使来自训练投影的主分量对于要重建的这些4D图像肯定是不同的。为此，使用用于投影入库的相同的实时呼吸位置间隔将训练数据与重建数据同步。在图像重建方面，通过先验的时间系数，使PCR的数据保真度从非线性变为线性，从而使该方法具有较强的鲁棒性和较高的求解效率。该算法是一个关于空间主成分的线性数据保真度和对4D图像相位施加时空全变差正则化的凸优化问题。在乘子交替方向法的基础上，提出了一种基于乘子交替方向法的PCR求解算法。利用NVIDIA CUDA工具箱在GPU上实现了完全并行化，每次重建大约需要几分钟。利用仿真和实验数据对所提出的方法进行了验证，并与目前最先进的方法，即PICCS方法进行了比较。结果表明，应用聚合酶链式反应(PCR)技术进行CINE CBCT重建是可行的，并显著提高了PICC的重建质量。该方法利用透视训练投影对时间运动系数进行先验估计，可以准确地重建空间主成分，然后生成时间运动系数和空间主成分乘积的电影CT图像。
This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images. In the proposed PCR method, the matrix factorization is utilized as an explicit low-rank regularization of 4D images that are represented as a product of spatial principal components and temporal motion coefficients. The key hypothesis of PCR is that temporal coefficients from 4D images can be reasonably approximated by temporal coefficients learned from 2D fluoroscopic training projections. For this purpose, we can acquire fluoroscopic training projections for a few breathing periods at fixed gantry angles that are free from geometric distortion due to gantry rotation, that is, fluoroscopy-based motion learning. Such training projections can provide an effective characterization of the breathing motion. The temporal coefficients can be extracted from these training projections and used as priors for PCR, even though principal components from training projections are certainly not the same for these 4D images to be reconstructed. For this purpose, training data are synchronized with reconstruction data using identical real-time breathing position intervals for projection binning. In terms of image reconstruction, with a priori temporal coefficients, the data fidelity for PCR changes from nonlinear to linear, and consequently, the PCR method is robust and can be solved efficiently. PCR is formulated as a convex optimization problem with the sum of linear data fidelity with respect to spatial principal components and spatiotemporal total variation regularization imposed on 4D image phases. The solution algorithm of PCR is developed based on alternating direction method of multipliers. The implementation is fully parallelized on GPU with NVIDIA CUDA toolbox and each reconstruction takes about a few minutes. The proposed PCR method is validated and compared with a state-of-art method, that is, PICCS, using both simulation and experimental data with the on-board cone-beam CT setting. The results demonstrated the feasibility of PCR for cine CBCT and significantly improved reconstruction quality of PCR from PICCS for cine CBCT. With a priori estimated temporal motion coefficients using fluoroscopic training projections, the PCR method can accurately reconstruct spatial principal components, and then generate cine CT images as a product of temporal motion coefficients and spatial principal components.
登录
查看更多内容
DOI：
10.1109/tbme.2013.2266096
发表时间：
2013-11
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Christodoulou AG;Zhang H;Zhao B;Hitchens TK;Ho C;Liang ZP
通讯作者：
Liang ZP
影响因子：
3.5
作者：
Dietrich, Lars;Jetter, Siri;Oelfke, Uwe
通讯作者：
Oelfke, Uwe
影响因子：
3.8
作者：
Gao, Hao;Qi, X. Sharon;Low, Daniel A.
通讯作者：
Low, Daniel A.
影响因子：
3.5
作者：
Ritschl, Ludwig;Sawall, Stefan;Kachelriess, Marc
通讯作者：
Kachelriess, Marc
影响因子：
3.8
作者：
Yan, Hao;Zhen, Xin;Jia, Xun
通讯作者：
Jia, Xun