Du Xiaogang, Dang Jianwu, Wang Yangping, Wang Song, Lei Tao
School of Electronic & Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
School of Electronic & Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; Lanzhou Yuxin Information Technology Limited Liability Company, Lanzhou 730000, China.
Comput Math Methods Med. 2016;2016:7419307. doi: 10.1155/2016/7419307. Epub 2016 Dec 7.
The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU).
基于B样条自由形式变形（FFD）的非刚性配准算法发挥着关键作用，由于其良好的灵活性和鲁棒性，在医学图像处理中得到了广泛应用。然而，尤其是对于大量医学图像数据，要获得更精确的配准结果需要耗费大量的计算时间。为了解决这个问题，本文提出了一种基于B样条的并行非刚性配准算法。首先，在B样条配准算法中采用对数平方差（LSD）作为相似性度量以提高配准精度。之后，我们创建了一种并行计算策略和查找表（LUT）来降低B样条配准算法的复杂度。结果，由于B样条配准算法采用非线性共轭梯度（NCG）优化方法，包括B样条插值、LSD计算以及LSD的解析梯度计算这三个耗时步骤的计算时间得到了有效减少。在大量医学图像上的配准质量和执行效率的实验结果表明，我们的算法在最佳变形场与真实值之间的差异方面实现了更好的配准精度，并且由于图形处理单元（GPU）强大的并行计算能力，与单线程CPU实现相比加速了17倍。
