合成低场神经MRI去噪方法的比较

Anthony O'Brien, F. Rodriguez y Baena
{"title":"合成低场神经MRI去噪方法的比较","authors":"Anthony O'Brien, F. Rodriguez y Baena","doi":"10.31256/hsmr2023.39","DOIUrl":null,"url":null,"abstract":"Over the past decade, the resurgence of low-field (LF) magnetic resonance imaging (MRI) sensor systems designed to operate up to 1 tesla range has proven well suited to inspire new solutions and design strategies to address frontline medical challenges where environmental factors are most extreme. Examples of successful low-field MRI devices in resource-limited environments include: (1) classification of infant hydrocephalus in Africa and (2) in remote small hospitals where the low-field scanner travels to the patient's bedside to observe volumetric changes in brain structure [1,2]. These low-field MRI design examples have been effective in leveraging MRI information in the setting where it is used with minimal available resources. In developing countries, access to high-field (HF) MRI is limited and requires support and infrastructure to be used. These lower-cost systems can potentially benefit from many developments that have occurred in higher fields, such as signal-to-noise dependence on static magnetic fields and hardware components (i.e., magnet, gradient coils, etc.) [3]. In addition, improvements in machine learning now provide superior noise reduction compared to traditional methods, resulting in improved performance with smaller size and lower power consumption. With improved access to medical imaging equipment, people around the world who cannot afford it due to the high cost of conventional MRIs will be able to obtain high- quality imaging data with improved contrast resolution and acquisition times. SNR (signal-to-noise ratio) is an important measure of the quality of a signal in low-field MRI. The amount of useful information in a signal compared to background noise directly affects the effectiveness of a low-field scan [4].","PeriodicalId":129686,"journal":{"name":"Proceedings of The 15th Hamlyn Symposium on Medical Robotics 2023","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of denoising methods for synthetic low-field neurological MRI\",\"authors\":\"Anthony O'Brien, F. Rodriguez y Baena\",\"doi\":\"10.31256/hsmr2023.39\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Over the past decade, the resurgence of low-field (LF) magnetic resonance imaging (MRI) sensor systems designed to operate up to 1 tesla range has proven well suited to inspire new solutions and design strategies to address frontline medical challenges where environmental factors are most extreme. Examples of successful low-field MRI devices in resource-limited environments include: (1) classification of infant hydrocephalus in Africa and (2) in remote small hospitals where the low-field scanner travels to the patient's bedside to observe volumetric changes in brain structure [1,2]. These low-field MRI design examples have been effective in leveraging MRI information in the setting where it is used with minimal available resources. In developing countries, access to high-field (HF) MRI is limited and requires support and infrastructure to be used. These lower-cost systems can potentially benefit from many developments that have occurred in higher fields, such as signal-to-noise dependence on static magnetic fields and hardware components (i.e., magnet, gradient coils, etc.) [3]. In addition, improvements in machine learning now provide superior noise reduction compared to traditional methods, resulting in improved performance with smaller size and lower power consumption. With improved access to medical imaging equipment, people around the world who cannot afford it due to the high cost of conventional MRIs will be able to obtain high- quality imaging data with improved contrast resolution and acquisition times. SNR (signal-to-noise ratio) is an important measure of the quality of a signal in low-field MRI. The amount of useful information in a signal compared to background noise directly affects the effectiveness of a low-field scan [4].\",\"PeriodicalId\":129686,\"journal\":{\"name\":\"Proceedings of The 15th Hamlyn Symposium on Medical Robotics 2023\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of The 15th Hamlyn Symposium on Medical Robotics 2023\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31256/hsmr2023.39\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of The 15th Hamlyn Symposium on Medical Robotics 2023","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31256/hsmr2023.39","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在过去的十年中,低场(LF)磁共振成像(MRI)传感器系统的重新兴起,其设计工作范围高达1特斯拉,已被证明非常适合激发新的解决方案和设计策略,以应对环境因素最极端的一线医疗挑战。低场MRI设备在资源有限的环境中成功的例子包括:(1)非洲婴儿脑积水的分类;(2)在偏远的小医院,低场扫描仪到达患者床边观察脑结构的体积变化[1,2]。这些低场MRI设计示例在可用资源最少的情况下有效地利用了MRI信息。在发展中国家,获得高场核磁共振成像的机会有限,需要支持和基础设施的使用。这些低成本的系统可能会受益于在更高领域发生的许多发展,例如静态磁场和硬件组件(即磁铁,梯度线圈等)的信噪比依赖[3]。此外,与传统方法相比,机器学习的改进现在提供了更好的降噪效果,从而在更小的尺寸和更低的功耗下提高了性能。随着医疗成像设备的普及,世界各地因传统核磁共振成像的高成本而无法负担的人们将能够获得高质量的成像数据,并提高对比度分辨率和采集时间。在低场磁共振成像中,信噪比(SNR)是衡量信号质量的重要指标。与背景噪声相比,信号中有用信息的数量直接影响低场扫描的有效性[4]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of denoising methods for synthetic low-field neurological MRI
Over the past decade, the resurgence of low-field (LF) magnetic resonance imaging (MRI) sensor systems designed to operate up to 1 tesla range has proven well suited to inspire new solutions and design strategies to address frontline medical challenges where environmental factors are most extreme. Examples of successful low-field MRI devices in resource-limited environments include: (1) classification of infant hydrocephalus in Africa and (2) in remote small hospitals where the low-field scanner travels to the patient's bedside to observe volumetric changes in brain structure [1,2]. These low-field MRI design examples have been effective in leveraging MRI information in the setting where it is used with minimal available resources. In developing countries, access to high-field (HF) MRI is limited and requires support and infrastructure to be used. These lower-cost systems can potentially benefit from many developments that have occurred in higher fields, such as signal-to-noise dependence on static magnetic fields and hardware components (i.e., magnet, gradient coils, etc.) [3]. In addition, improvements in machine learning now provide superior noise reduction compared to traditional methods, resulting in improved performance with smaller size and lower power consumption. With improved access to medical imaging equipment, people around the world who cannot afford it due to the high cost of conventional MRIs will be able to obtain high- quality imaging data with improved contrast resolution and acquisition times. SNR (signal-to-noise ratio) is an important measure of the quality of a signal in low-field MRI. The amount of useful information in a signal compared to background noise directly affects the effectiveness of a low-field scan [4].
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信