基于非局部自相似图像块字典学习的伪CT图像预测

Pseudo CT Estimation by Non-local Self-similar Image Patch Based Dictionary Learning

随着PET/CT技术的日益发展,其被广泛应用于现代放射治疗。但在采集数据过程中,对人体放射时间较长,辐射当量较大,增加了患者的痛苦,因此人们希望减少CT扫描中X射线的辐射。为解决这一问题,本文提出基于非局部自相似图像块字典学习的伪CT图像预测方法。首先,对训练CT与MRI图像进行图像分块,通过块匹配算法聚类CT图像块,并提取CT与MRI图像块的多尺度特征。其次,通过字典学习,获得MRI图像与CT图像的映射关系矩阵,并对CT图像块进行预测。最后,通过重构算法,从目标MRI图像中得到预测CT图像。仿真实验证明了提出算法相对基于图谱集算法的有效性,以及在现代放射治疗中利用MRI图像替代CT图像的应用前景。

Positron emission tomography computed tomography（ PET/CT） is increasingly being used in modern radiotherapy（ RT） treatment in combination with computed tomography（ CT）. Due to the high dose of radiation exposure in CT scan,there is always a great desire to reduce the amount of the radiation dose in x-ray computed tomography（ CT） because of the health risks. In this paper,we investigate the potential of non-local self-similar patch-based dictionary learning based pseudo CT estimating method for tackling this challenging task. Firstly,the method partitions training CT and MRI images into a set of patches. For each patch,voxel multi-scale features are extracted and a block-matching method is applied to cluster the CT image patches. Then by dictionary learning,we obtain the MRI-CT mapping matrix and predict CT patches as a structured output. Finally,by reconstruction method,we output the estimated CT image. Experimental results show that our method perform better than existing atlas-based method and showed a promising potential for RT of brain based only on MRI.