摘要：

在心肌灌注核磁共振(MR)图像中，病人的呼吸和心跳会使心脏的位置和形状发生改变，因此需要对心脏核磁共振(CMR)时间序列图像中的心肌图像位置进行运动补偿。针对医学图像特征较少的问题，利用马尔科夫随机场(MRF)模型，提出一种基于图像配准的心脏运动补偿算法。根据心动周期不同时间点图像像素块的邻域和灰度信息，计算心脏的运动向量，将最相似的像素块平移到图像的相近位置，对心跳产生的位移进行补偿。由于MRF模型的计算量较大，将CPU算法和GPU算法相结合，计算耗时部分使用GPU并行实现，以提高程序的运行速度。实验结果表明，该方法能有效地对心肌灌注MR图像中心脏的位移和弹性形变进行补偿，结合GPU算法能使运动补偿算法的计算性能提高400%，图像配准时间仅为CPU算法的1/3。

关键词: 心肌灌注核磁共振图像, 马尔科夫随机场, 位移补偿, 配准, 并行算法, 优化算法

Abstract:

In the myocardial perfusion image, the location and shape of the heart change with respiration and heartbeat. Therefore, it is necessary to compensate the movement of positions of myocardial in Cardiac Magnetic Resonance(CMR). To address the poor feature problems in medical image, this paper introduces Markov Random Field(MRF) to tackle this problem and to assess the cardiac movement. According to the neighbor information and intensity information of image pixel blocks in the sequence of cardiac cycle images, the motion vectors can be calculated and the most similar pixel blocks are placed to almost the same position to compensate cardiac movement. Due to complexity of the calculation in MRF, some GPU based methods are introduced to improve computing performance of the whole algorithm. Experimental results demonstrate that the method can effectively correct the movement and deformation of the myocardial perfusion image. The calculation performance increases 400%, the calculation time is one third of the CPU based methods after applying GPU.

Key words: myocardial perfusion Magnetic Resonance(MR) image, Markov Random Field(MRF), displacement compensation, registration, parallel algorithm, optimization algorithm

中图分类号: 

• TP391