心肌灌注核磁共振图像中心脏的位置和形状会随着病人的呼吸和心跳而发生改变。同时,灌注图像的灰度也会随着造影剂在心腔中的流动而不断变化。这使得传统单纯依靠互信息、互相关等信息的配准算法很难在心肌灌注核磁共振图像中得到应用。使用马尔可夫随机场(MRF)模型对图像进行非刚性配准。在马尔可夫能量函数中对图像块进行归一化,以消除两幅图像的灰度差异对配准的影响。为了消除序列图像配准中产生的积累误差,对心肌灌注MR序列中的每幅图像计算对应的伪真实(PGT)图像,并将这幅伪真实图像作为配准模板。通过将心肌灌注MR序列中的每幅图像与其对应的伪真实图像进行配准,避免了序列图像配准中积累误差的产生。实验结果表明,该方法有效地矫正了心肌灌注MR图像中心脏的位移和弹性形变。

In the myocardial perfusion image, the location and shape of the heart changes with respiration and heartbeat. Meanwhile, the image intensity also changes as the contrast agent flowing in the heart chambers. It is difficult to apply the traditional registration methods, such as cross correlation and mutual information to the registration of myocardial perfusion images. In this paper, we introduce Markov random fields(MRF)to tackle this problem. In the Markov energy function, the image block is normalized to get rid of the intensity change effect. In order to remove the accumulated error, which is common in registration of image series, a pseudo ground-truth image is calculated as template for each of the myocardial perfusion images. The accumulated error can be removed by warping each myocardial perfusion image to its corresponding pseudo ground-truth image. Experimental results demonstrate that our method can effectively correct the displacement and deformation of the myocardial perfusion image.