当前位置: X-MOL 学术Med Phys › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Full-count PET recovery from low-count image using a dilated convolutional neural network.
Medical Physics ( IF 3.2 ) Pub Date : 2020-07-20 , DOI: 10.1002/mp.14402
Karl Spuhler 1 , Mario Serrano-Sosa 1 , Renee Cattell 1 , Christine DeLorenzo 1, 2 , Chuan Huang 1, 2, 3
Affiliation  

Positron emission tomography (PET) is an essential technique in many clinical applications that allows for quantitative imaging at the molecular level. This study aims to develop a denoising method using a novel dilated convolutional neural network (CNN) to recover full‐count images from low‐count images.

中文翻译:

使用膨胀卷积神经网络从低计数图像中恢复全计数PET。

正电子发射断层扫描(PET)是许多临床应用中必不可少的技术,可在分子水平进行定量成像。这项研究旨在开发一种使用新型膨胀卷积神经网络(CNN)的降噪方法,以从低计数图像中恢复全计数图像。
更新日期:2020-07-20
down
wechat
bug