Shidahara Miho, Ikoma Yoko, Kershaw Jeff, Kimura Yuichi, Naganawa Mika, Watabe Hiroshi
Biophysics Group, Molecular Imaging Center, National Institute of Radiological Sciences, 4-9-1 Anagawa, Chiba 263-8555, Japan.
Ann Nucl Med. 2007 Sep;21(7):379-86. doi: 10.1007/s12149-007-0044-9. Epub 2007 Sep 25.
Physiological functions (e.g., cerebral blood flow, glucose metabolism, and neuroreceptor binding) can be investigated as parameters estimated by kinetic modeling using dynamic positron emission tomography (PET) images. Imaging of these physiological parameters, called parametric imaging, can locate the regional distribution of functionalities. However, the most serious technical issue affecting parametric imaging is noise in dynamic PET data. This review describes wavelet denoising of dynamic PET images for improving image quality in estimated parametric images. Wavelet denoising provides significantly improved quality directly to dynamic PET images and indirectly to estimated parametric images. The application of wavelet denoising to radio-ligand and kinetic analysis is still in the development stage, but even so, it is thought that wavelet techniques will have a substantial impact on nuclear medicine in the near future.
生理功能(如脑血流量、葡萄糖代谢和神经受体结合)可以作为通过使用动态正电子发射断层扫描(PET)图像的动力学建模估计的参数来进行研究。对这些生理参数的成像,即参数成像,可以定位功能的区域分布。然而,影响参数成像的最严重技术问题是动态PET数据中的噪声。本综述描述了动态PET图像的小波去噪,以提高估计的参数图像的质量。小波去噪直接为动态PET图像提供显著提高的质量,并间接为估计的参数图像提供显著提高的质量。小波去噪在放射性配体和动力学分析中的应用仍处于发展阶段,但即便如此,人们认为小波技术在不久的将来将对核医学产生重大影响。