Affiliations: [a] Institute of Medical Imaging and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| [b] Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China
Correspondence:
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Corresponding authors. Yuanjun Wang, Institute of Medical Imaging and Engineering, University of Shanghai for Science and Technology, Shanghai, China and Yu Liu, Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai JiaoTong University Schoolof Medicine, Shanghai, China. E-mails: yjusst@126.com (Y. Wang) and yliu9y@126.com (Y. Liu).
Abstract: Gradient based image fusion can more effectively incorporate edge details using structure tensor, which is successfully used in 2D image fusion. In this study, we generalized and applied this gradient based image fusion method into 3D for non-small cell lung cancer PET/CT image fusion. According the characteristic of lung PET/CT images, we proposed a novel 3D structure tensor based feature, which can be used to construct a weighted structure tensor containing important local detail of both PET and CT images. The fusion gradient domain is deduced from a rank one tensor, which is the closest approximation of the weighted structure tensor in geometry. Based on the fusion gradient domain, final PET/CT fusion image is obtained by solving a Poisson equation. Comparing with the wavelet transform based fusion result, the average information entropy and average gradient measure of proposed fusion method increase 13.5% and 42.3%, respectively. The experimental results show that the proposed fusion method enables to effectively preserve lung vessel structure and sphere-like lesion detail while produces clear, stable and well consistent fusion images.
