Super-resolution PET image reconstruction with sparse representation

被引：0

作者：

Hu, Zhanli ^{[1
]}

Li, Tao ^{[1
]}

Yang, Yongfeng ^{[1
]}

Liu, Xin ^{[1
]}

Zheng, Hairong ^{[1
]}

Liang, Dong ^{[1
]}

机构：

[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Inst Biomed & Hlth Engn, Lauterbur Res Ctr Biomed Imaging, Shenzhen 518055, Peoples R China

来源：

2017 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC) | 2017年

基金：

中国国家自然科学基金;

关键词：

Positron emission tomography (PET); super-resolution; sparse representation; dictionaries;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In the positron emission tomography (PET) field, reconstructed images are often blurry and contain noise. These problems are mainly caused by the low-count problem. As sparse technology becomes more widely used, sparse prediction is increasingly prone to be selected to solve the problem. In this paper, we propose a new sparse prior method to process low resolution PET reconstructed images. In the proposed strategy, two dictionaries (D-1 for low-resolution PET images and D-2 for high-resolution PET images) are trained from numerous real PET image patches. Subsequently, D-1 is used to obtain the sparse representation for each patch of the input PET image. Finally, a high-resolution PET image is generated from this sparse representation using D-2. The results of experiments indicate that the proposed method exhibits stable and superior effects that enhance image resolution, detail recovery. Quantitatively, this method achieves a better performance than traditional methods in terms of root mean square error (RMSE). The proposed strategy provides a new and efficient approach to improve the image quality of reconstructed PET images.

引用

页数：3