Image Fusion for Improving the Spatial Resolution of LA-ICP-MS Imaging

化学 图像融合 图像分辨率 分辨率(逻辑) 融合 计算机视觉 人工智能 图像(数学) 计算机科学 语言学 哲学
作者
Teerapong Jantarat,Jeerapat Doungchawee,Xianzhi Zhang,Vincent M. Rotello,Richard W. Vachet
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:97 (27): 14557-14564 被引量:6
标识
DOI:10.1021/acs.analchem.5c01925
摘要

Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging has been employed to determine elemental distributions in biological tissues and has proven to be valuable for studying nanomaterials used in drug delivery systems. However, in LA-ICP-MS imaging, there are often trade-offs between achieving higher spatial resolution, maintaining sensitivity, and minimizing acquisition time. Using a larger laser spot size retains sensitivity and allows for faster image acquisition, but the resulting poorer resolution is often insufficient for characterizing the distributions of nanomaterials in functional units of organs, such as the kidney, liver, and spleen. In this work, we describe an approach to enhance the spatial resolution of LA-ICP-MS imaging using image fusion through the computational integration of LA-ICP-MS and optical microscopy images. This approach maintains the highly precise and sensitive elemental distributions inherent to LA-ICP-MS while leveraging the high spatial resolution of optical microscopy to obtain more detailed suborgan maps for both biological metals and nanomaterials. Our approaches enable the construction of LA-ICP-MS images with 5 μm resolution without changes to hardware, operational time, or sensitivity. The resulting enhanced resolution yields new insights into nanomaterial excretion through the liver and the role of the immune system in recognizing gold nanoparticles in the spleen. This fusion approach promises to serve as a valuable tool for advancing nanomaterial-based drug delivery systems.
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
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
QQ完成签到,获得积分10
2秒前
3秒前
动听易槐发布了新的文献求助10
4秒前
5秒前
keyan123完成签到,获得积分10
5秒前
ccccc发布了新的文献求助10
5秒前
melon完成签到,获得积分20
5秒前
夏果发布了新的文献求助10
5秒前
风中的小蝴蝶完成签到,获得积分10
6秒前
8秒前
8秒前
朴素冰珍完成签到,获得积分20
11秒前
12秒前
12秒前
奋斗金连完成签到,获得积分10
13秒前
菓小柒完成签到 ,获得积分10
13秒前
小马甲应助undergo采纳,获得10
13秒前
子时过完成签到,获得积分10
13秒前
mihomo6666发布了新的文献求助10
14秒前
烟花应助寂寞不是你的错采纳,获得10
15秒前
lianmeiliu发布了新的文献求助10
16秒前
17秒前
嘟嘟左发布了新的文献求助10
18秒前
宫傲蕾完成签到 ,获得积分10
18秒前
顶顶小明完成签到,获得积分10
18秒前
地中海大哥完成签到,获得积分10
21秒前
nhb0912完成签到,获得积分20
21秒前
XX发布了新的文献求助10
21秒前
George完成签到,获得积分10
21秒前
龙吟完成签到,获得积分10
21秒前
缘202020完成签到,获得积分10
22秒前
22秒前
所所应助大水牛姐姐采纳,获得10
22秒前
小丁发布了新的文献求助10
22秒前
赵敏完成签到,获得积分10
22秒前
23秒前
李健的小迷弟应助Zhu采纳,获得10
23秒前
24秒前
24秒前
lfw发布了新的文献求助10
24秒前
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
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Rosenblum, Global Change Biology 500
CLSI VET01S-2024 Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals (7th Ed) 500
DIPPR Project 801 - Full Version 380
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7767689
求助须知:如何正确求助?哪些是违规求助? 9311208
关于积分的说明 20322344
捐赠科研通 7352659
什么是DOI,文献DOI怎么找? 3315436
关于科研通互助平台的介绍 2464719
邀请新用户注册赠送积分活动 2330065