已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

阈值 迭代重建 压缩传感 算法 迭代法 欠采样 图像质量 计算机科学 人工智能 工件(错误) 计算机视觉 图像(数学) 数学
作者
Sana Elahi,Muhammad Kaleem,Hammad Omer
出处
期刊:Journal of Magnetic Resonance [Elsevier BV]
卷期号:286: 91-98 被引量:18
标识
DOI:10.1016/j.jmr.2017.11.008
摘要

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k-space. This paper introduces an improved iterative algorithm based on p-thresholding technique for CS-MRI image reconstruction. The use of p-thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p-thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p-thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary’s Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.
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
PDF的下载单位、IP信息已删除 (2025-6-4)

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
1秒前
guo完成签到 ,获得积分10
2秒前
2秒前
1234发布了新的文献求助10
2秒前
5秒前
VIAI发布了新的文献求助10
6秒前
负责怀莲发布了新的文献求助10
8秒前
热心语柔完成签到 ,获得积分10
8秒前
多年以后发布了新的文献求助10
10秒前
10秒前
科研通AI5应助科研通管家采纳,获得10
10秒前
JamesPei应助科研通管家采纳,获得10
11秒前
11秒前
科研通AI5应助科研通管家采纳,获得10
11秒前
11秒前
李健应助科研通管家采纳,获得10
11秒前
11秒前
pan完成签到,获得积分10
12秒前
olivia完成签到,获得积分10
13秒前
缓慢安阳发布了新的文献求助10
15秒前
18秒前
19秒前
Rondab应助图南采纳,获得30
20秒前
1234发布了新的文献求助10
21秒前
深情安青应助小木安华采纳,获得10
22秒前
福大命大关注了科研通微信公众号
23秒前
淡定海亦发布了新的文献求助10
24秒前
暴躁的大侠完成签到,获得积分10
26秒前
26秒前
丘比特应助11采纳,获得10
27秒前
酷波er应助西瓜采纳,获得10
29秒前
淡定海亦完成签到,获得积分10
29秒前
超级飞侠完成签到,获得积分10
30秒前
傻丢完成签到 ,获得积分10
30秒前
30秒前
黎乐荷发布了新的文献求助10
32秒前
完美蚂蚁发布了新的文献求助10
32秒前
33秒前
34秒前
35秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
Aktuelle Entwicklungen in der linguistischen Forschung 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 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 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989918
求助须知:如何正确求助?哪些是违规求助? 3532013
关于积分的说明 11255831
捐赠科研通 3270829
什么是DOI,文献DOI怎么找? 1805053
邀请新用户注册赠送积分活动 882233
科研通“疑难数据库(出版商)”最低求助积分说明 809216