亲爱的研友该休息了!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!身体可是革命的本钱,早点休息,好梦!

NIR-II/NIR-I Fluorescence Molecular Tomography of Heterogeneous Mice Based on Gaussian Weighted Neighborhood Fused Lasso Method

近红外光谱 高斯分布 光学相干层析成像 计算机科学 漫反射光学成像 材料科学 生物医学工程 光学 断层摄影术 迭代重建 人工智能 生物系统 物理 量子力学 医学 生物
作者
Meishan Cai,Zeyu Zhang,Xiaojing Shi,Zhenhua Hu,Jie Tian
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:39 (6): 2213-2222 被引量:25
标识
DOI:10.1109/tmi.2020.2964853
摘要

Fluorescence molecular tomography (FMT), which can visualize the distribution of fluorescence biomarkers, has become a novel three-dimensional noninvasive imaging technique for in vivo studies such as tumor detection and lymph node location. However, it remains a challenging problem to achieve satisfactory reconstruction performance of conventional FMT in the first near-infrared window (NIR-I, 700-900nm) because of the severe scattering of NIR-I light. In this study, a promising FMT method for heterogeneous mice was proposed to improve the reconstruction accuracy using the second near-infrared window (NIR-II, 1000-1700nm), where the light scattering significantly reduced compared with NIR-I. The optical properties of NIR-II were analyzed to construct the forward model for NIR-II FMT. Furthermore, to raise the accuracy of solution of the inverse problem, we proposed a novel Gaussian weighted neighborhood fused Lasso (GWNFL) method. Numerical simulation was performed to demonstrate the outperformance of GWNFL compared with other algorithms. Besides, a novel NIR-II/NIR-I dual-modality FMT system was developed to contrast the in vivo reconstruction performance between NIR-II FMT and NIR-I FMT. To compare the reconstruction performance of NIR-II FMT with traditional NIR-I FMT, numerical simulations and in vivo experiments were conducted. Both the simulation and in vivo results showed that NIR-II FMT outperformed NIR-I FMT in terms of location accuracy and spatial overlap index. It is believed that this study could promote the development and biomedical application of NIR-II FMT in the future.

科研通智能强力驱动
Strongly Powered by AbleSci AI
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
Leo完成签到,获得积分10
4秒前
4秒前
ethanyangzzz发布了新的文献求助10
10秒前
爱笑的芝麻完成签到,获得积分10
14秒前
20秒前
Lucas应助ethanyangzzz采纳,获得10
22秒前
刘金金发布了新的文献求助10
27秒前
34秒前
Umair发布了新的文献求助10
39秒前
louis861933完成签到,获得积分10
40秒前
苗条的奇异果完成签到,获得积分10
42秒前
dontcrybaby完成签到 ,获得积分10
52秒前
Jasper应助张张采纳,获得10
1分钟前
年轻静蕾完成签到,获得积分10
1分钟前
美味SCI歌单完成签到,获得积分10
1分钟前
天天快乐应助张张采纳,获得10
1分钟前
迷你的蜜粉完成签到,获得积分10
1分钟前
1分钟前
天才幸运鱼完成签到,获得积分10
1分钟前
颜瑞发布了新的文献求助10
1分钟前
科研通AI6.2应助小璐璐呀采纳,获得10
1分钟前
闪闪书蕾完成签到,获得积分10
1分钟前
hgvj应助科研通管家采纳,获得200
1分钟前
共享精神应助科研通管家采纳,获得10
1分钟前
复杂的醉山完成签到,获得积分10
2分钟前
wwb0501完成签到,获得积分10
2分钟前
wtt完成签到,获得积分10
2分钟前
2分钟前
笑点低的丹蝶完成签到,获得积分10
2分钟前
Zhu发布了新的文献求助30
2分钟前
3分钟前
Umair完成签到,获得积分10
3分钟前
酒窝发布了新的文献求助10
3分钟前
动听一德完成签到,获得积分10
3分钟前
3分钟前
我是老大应助酒窝采纳,获得10
3分钟前
阔达的芹菜完成签到,获得积分10
3分钟前
白灼虾完成签到 ,获得积分10
3分钟前
3分钟前
朴素的山蝶完成签到,获得积分10
3分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Principles of town planning: translating concepts to applications 1000
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The role of consumer psychology in the marketing strategies of pop mart in Thailand 500
核安全综合知识2024版 500
Photothermal Science and Techniques 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7720439
求助须知:如何正确求助?哪些是违规求助? 9274138
关于积分的说明 20100590
捐赠科研通 7296765
什么是DOI,文献DOI怎么找? 3300175
关于科研通互助平台的介绍 2454014
邀请新用户注册赠送积分活动 2307644