A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution

反褶积 迭代重建 计算机科学 人工智能 相似性(几何) 滤波器(信号处理) 计算机视觉 模式识别(心理学) 人工神经网络 图像质量 深度学习 算法 图像(数学)
作者
Zhicheng Zhang,Xiaokun Liang,Xu Dong,Yaoqin Xie,Guohua Cao
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:37 (6): 1407-1417 被引量:235
标识
DOI:10.1109/tmi.2018.2823338
摘要

Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained many remarkable outcomes. In this paper, we propose a new method for sparse-view CT reconstruction based on the DL approach. The method can be divided into two steps. First, filter backprojection (FBP) was used to reconstruct the CT image from sparsely sampled sinogram. Then, the FBP results were fed to a DL neural network, which is a DenseNet and deconvolution-based network (DD-Net). The DD-Net combines the advantages of DenseNet and deconvolution and applies shortcut connections to concatenate DenseNet and deconvolution to accelerate the training speed of the network; all of those operations can greatly increase the depth of network while enhancing the expression ability of the network. After the training, the proposed DD-Net achieved a competitive performance relative to the state-of-the-art methods in terms of streaking artifacts removal and structure preservation. Compared with the other state-of-the-art reconstruction methods, the DD-Net method can increase the structure similarity by up to 18% and reduce the root mean square error by up to 42%. These results indicate that DD-Net has great potential for sparse-view CT image reconstruction.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
白华苍松发布了新的文献求助10
2秒前
小赵完成签到,获得积分20
2秒前
孙宏发布了新的文献求助10
4秒前
樱桃猴子应助李理采纳,获得10
5秒前
充电宝应助李理采纳,获得10
5秒前
5秒前
77777发布了新的文献求助10
6秒前
太叔文博完成签到,获得积分10
6秒前
斯文败类应助CY采纳,获得30
7秒前
辣椒炒肉发布了新的文献求助10
8秒前
热心市民小黄完成签到,获得积分10
8秒前
9秒前
9秒前
茜牙牙牙完成签到,获得积分20
9秒前
啊啊啊发布了新的文献求助10
9秒前
10秒前
orixero应助松松果采纳,获得10
12秒前
谜记完成签到 ,获得积分10
12秒前
13秒前
领导范儿应助茜牙牙牙采纳,获得10
13秒前
pagoda发布了新的文献求助30
14秒前
香蕉觅云应助小鹤采纳,获得10
15秒前
Plemon完成签到,获得积分10
16秒前
HXJ完成签到,获得积分10
17秒前
FashionBoy应助啊啊啊采纳,获得10
18秒前
ZZTT07完成签到,获得积分10
18秒前
漱石枕流完成签到 ,获得积分10
18秒前
英姑应助wear88采纳,获得10
20秒前
岁寒发布了新的文献求助10
20秒前
21秒前
彪壮的梦槐完成签到,获得积分10
21秒前
华仔应助梦想or现实采纳,获得30
25秒前
dyk完成签到,获得积分10
27秒前
HXJ发布了新的文献求助10
28秒前
29秒前
ZZTT07发布了新的文献求助10
30秒前
30秒前
科研通AI2S应助zhangyannini采纳,获得10
30秒前
高分求助中
The Oxford Handbook of Social Cognition (Second Edition, 2024) 1050
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Chen Hansheng: China’s Last Romantic Revolutionary 500
COSMETIC DERMATOLOGY & SKINCARE PRACTICE 388
Case Research: The Case Writing Process 300
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3141752
求助须知:如何正确求助?哪些是违规求助? 2792736
关于积分的说明 7804057
捐赠科研通 2449017
什么是DOI,文献DOI怎么找? 1303050
科研通“疑难数据库(出版商)”最低求助积分说明 626718
版权声明 601260