Shalchian Bahareh, Rajabi Hossein, Soltanian-zadeh Hamid
Medical Physics Department, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Hell J Nucl Med. 2009 Sep-Dec;12(3):238-43.
While information about anatomy is available in CT images, information about physiology and metabolism is available in PET images. To integrate both information, the two images are fused. Image fusion methods include simple methods like pixel averaging and sophisticated methods like wavelet transformation. An advantage of using wavelet transformation is that it preserves significant parts of each image. After creating lesions of 10, 8, 6 mm in a NURBS (non-uniform rational B-splines) based cardiac torso (NCAT) phantom, PET images were simulated using SimSET simulator. Attenuation maps of the activity phantom were used as CT images. Each of the PET and CT images was divided into an approximation image and three detailed images by the wavelet transform. The corresponding transformed images generated from the PET and CT images were fused in nine different ways to generate composite images, which were compared to the original images. The basis of comparison is the lesion-to-tissue contrast in the fused image in comparison to the lesion-to-tissue contrast in the original PET and CT images. Our results showed that except for one method, the lesion-to-tissue contrast in the fused image was higher than that of the CT images. In the first six methods, the lesion-to-tissue contrast in the fused image was less than the contrast, in the PET image. In the other three methods, the contrast in the fused image was higher than in the PET image. This was true in cases of 10, 8, 6 mm lesions. In conclusion, we have show that the approximation image produced a better ultimate image and that the lesion-to-tissue contrast in the fused image was also better than that of the original PET and CT images. This is because the approximation image is comprised of fundamental information of the signal (low frequency) that directly affects the image contrast.
虽然CT图像中可获取解剖学信息，但PET图像中可获取生理学和代谢信息。为整合这两种信息，需对这两种图像进行融合。图像融合方法包括像素平均等简单方法以及小波变换等复杂方法。使用小波变换的一个优点是它能保留每张图像的重要部分。在基于非均匀有理B样条曲线（NURBS）的心脏躯干（NCAT）模型中创建10毫米、8毫米、6毫米的病变后，使用SimSET模拟器模拟PET图像。将活动模型的衰减图用作CT图像。通过小波变换将PET图像和CT图像各自分为一个近似图像和三个细节图像。由PET图像和CT图像生成的相应变换图像以九种不同方式进行融合以生成合成图像，并与原始图像进行比较。比较的依据是融合图像中病变与组织的对比度与原始PET图像和CT图像中病变与组织的对比度。我们的结果表明，除一种方法外，融合图像中病变与组织的对比度高于CT图像。在前六种方法中，融合图像中病变与组织的对比度低于PET图像中的对比度。在其他三种方法中，融合图像中的对比度高于PET图像中的对比度。在10毫米、8毫米、6毫米病变的情况下均如此。总之，我们已表明近似图像产生了更好的最终图像，且融合图像中病变与组织的对比度也优于原始PET图像和CT图像。这是因为近似图像由直接影响图像对比度的信号基本信息（低频）组成。
