Segmenting contrast-enhanced CT images for attenuation correction of PET/CT data.

摘要

The use of contrast media in positron emission tomography (PET)/computed tomography (CT) dual modality imaging has been shown to cause artifacts in the PET image. These artifacts are attributed to an overestimation of the PET attenuation coefficients, which are obtained from contrast-enhanced CT numbers. This dissertation evaluates three algorithms, which segment intravenous contrast-enhanced tissue from CT images, so as to minimize this bias. The algorithms evaluated are the template matching; 3D region growing, and snake-based methods, and they were tested using 5 patient studies. Segmentation results for each method were compared to corresponding manually segmented images on a pixel-wise basis. The snake-based technique was judged to be most suitable for efficiently segmenting the contrast-enhanced CT images. This technique can lead to a more efficient acquisition of high quality PET/CT data, by enabling the use of contrast media without introducing related artifacts.