PET Image Improvement using the Patch Confidence K-nearest Neighbors Filter

被引：0

作者：

Yu, Sicong ^{[1
]}

Muhammed, Hamed Hamid ^{[1
]}

机构：

[1] Royal Inst Technol KTH, Sch Technol & Hlth STH, Stockholm, Sweden

来源：

2014 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) | 2014年

关键词：

D O I：

暂无

中图分类号：

R-058 [];

学科分类号：

摘要：

In Positron Emission Tomography (PET), the resulted images are highly deteriorated by noise. In this study, we propose a new denoising framework using the Patch Confidence K-Nearest Neighbors Filter (PCKNN) to reduce noise in the sinogram before forwarding it to the reconstruction procedure. This method has been evaluated on a simulated PET image of a phantom, and the performance has been compared with several conventional methods in the literature. The results have shown that the PET image quality can be substantially improved in term of increased signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).

引用

页码：306 / 309

页数：4

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