Deep reconstruction model for dynamic PET images.

	Deep reconstruction model for dynamic PET images.
	J Cui; X Liu; Y Wang; H Liu
刊名	PLoS One
	2017
文献子类	期刊论文
英文摘要	Accurate and robust tomographic reconstruction from dynamic positron emission tomography (PET) acquired data is a difficult problem. Conventional methods, such as the maximum likelihood expectation maximization (MLEM) algorithm for reconstructing the activity distribution-based on individual frames, may lead to inaccurate results due to the checkerboard effect and limitation of photon counts. In this paper, we propose a stacked sparse auto-encoder based reconstruction framework for dynamic PET imaging. The dynamic reconstruction problem is formulated in a deep learning representation, where the encoding layers extract the prototype features, such as edges, so that, in the decoding layers, the reconstructed results are obtained through a combination of those features. The qualitative and quantitative results of the procedure, including the data based on a Monte Carlo simulation and real patient data demonstrates the effectiveness of our method.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12625]
专题	深圳先进技术研究院_数字所
作者单位	PLoS One
推荐引用方式 GB/T 7714	J Cui,X Liu,Y Wang,et al. Deep reconstruction model for dynamic PET images.[J]. PLoS One,2017.
APA	J Cui,X Liu,Y Wang,&H Liu.(2017).Deep reconstruction model for dynamic PET images..PLoS One.
MLA	J Cui,et al."Deep reconstruction model for dynamic PET images.".PLoS One (2017).