摘要
自适应放疗可根据患者解剖和/或生理的变化对放疗计划进行修正。与加速器集成的锥形束CT成像装置是最普遍的在线影像获取设备。但是,由于锥形束CT固有的电子散射,重建影像的电子密度不准确,使得通常采用的基于密度的配准算法配准计划扇形束CT和在线获取的锥形束CT影像时,会产生较大的配准误差。我们通过建模图像变形配准问题为一个求解梯度距离能量泛函的极值问题,然后通过变分法和Gauss-Seidel方法获得一种新型的基于梯度信息的变形配准算法的迭代公式。该方法在迭代过程中同时考虑梯度信息的吻合和变形场的连续性,产生准确光滑的变形场。此算法迭代公式的局部特性,使其便于并行实施。通过OpenCL编程将此算法在图形处理器(GPU)上并行实施,大大缩短了配准时间。利用配准结果结合flood filling和cubic matching算法,可以快速地完成在线器官映射。算法临床数据配准结果表明,本文提出的基于梯度场的配准算法与基于密度的算法相比可以更准确地配准临床锥形束CT和扇形束CT影像。由于配准可以在很短的时间内完成,配准结果可用于在线器官映射和在线重新计划优化。
Because the X-ray scatters, the CT numbers in cone-beam CT cannot exactly correspond to the electron densities. This, therefore, results in registration error when the intensity-based registration algorithm is used to reg- ister planning fan-beam CT and cone-beam CT. In order to reduce the registration error, we have developed an accu- rate gradient-based registration algorithm. The gradient-based deformable registration problem is described as a mini- mization of energy functional. Through the calculus of variations and Gauss-Seidel finite difference method, we de- rived the iterative formula of the deformable registration. The algorithm was implemented by GPU through OpenCL framework, with which the registration time was greatly reduced. Our experimental results showed that the pro- posed gradient-based registration algorithm could register more accurately the clinical cone-beam CT and fan-beam CT images compared with the intensity-based algorithm. The GPU-accelerated algorithm meets the real-time require- ment in the online adaptive radiotherapy.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2012年第3期534-540,共7页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(30970861
30670617)
