基于可变步长逆向组合算法的弥散张量图像配准

弥散张量成像（Diffusion Tensor Magnetic Resonance Imaging，DT-MRI）是近年来新兴的一种核磁共振成像技术，作为一种无创伤的描述大脑结构的新方法，弥散张量成像在临床诊断中发挥着重要的作用。由于不同个体或者同一个体不同状态下获取的张量数据展开的多幅图像之间存在一定偏差，需要将这些图像对齐，即配准。逆向组合算法是一种很好的配准方法，但是当模板图像和目标图像之间存在多种几何变化时，算法的收敛速度往往很慢。提出了一种可变步长的逆向组合算法，通过自适应改变原始算法的步长达到加快算法收敛速度的目的。实验表明，该算法在保持原始算法精度的基础上能够加快收敛速度，并且对图像的几何变换有很好的鲁棒性。

DT-MRI image registration based on variable step-size inverse compositional algorithm

Diffusion Tensor Magnetic Resonance Imaging（DT-MRI） is a rising Magnetic Resonance Imaging（MRI） technology in recent years.As a non-invasive method to describe complex inner structure of human brain,it plays an important role in clinical diagnostics.Because the unfolding images from the tensor data of different people or the same people at different times are different,these images should be aligned into a unified framework.Inverse compositional algorithm is a power method for image registration.However,the inverse compositional algorithm has some flaws.The convergence rate will be slow when the error image between template image and target image is large.This paper proposes a variable step-size algorithm based on the inverse compositional algorithm,which can raise the convergence rate by changing the step size adaptively.Experimental results show that this improved algorithm is faster than the inverse compositional algorithm and robust to the geometry variation.