A three-step, “brute-force” approach toward optimized affine spatial normalization

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-07-08 DOI:10.3389/fncom.2024.1367148
Marko Wilke
{"title":"A three-step, “brute-force” approach toward optimized affine spatial normalization","authors":"Marko Wilke","doi":"10.3389/fncom.2024.1367148","DOIUrl":null,"url":null,"abstract":"The first step in spatial normalization of magnetic resonance (MR) images commonly is an affine transformation, which may be vulnerable to image imperfections (such as inhomogeneities or “unusual” heads). Additionally, common software solutions use internal starting estimates to allow for a more efficient computation, which may pose a problem in datasets not conforming to these assumptions (such as those from children). In this technical note, three main questions were addressed: one, does the affine spatial normalization step implemented in SPM12 benefit from an initial inhomogeneity correction. Two, does using a complexity-reduced image version improve robustness when matching “unusual” images. And three, can a blind “brute-force” application of a wide range of parameter combinations improve the affine fit for unusual datasets in particular. A large database of 2081 image datasets was used, covering the full age range from birth to old age. All analyses were performed in Matlab. Results demonstrate that an initial removal of image inhomogeneities improved the affine fit particularly when more inhomogeneity was present. Further, using a complexity-reduced input image also improved the affine fit and was beneficial in younger children in particular. Finally, blindly exploring a very wide parameter space resulted in a better fit for the vast majority of subjects, but again particularly so in infants and young children. In summary, the suggested modifications were shown to improve the affine transformation in the large majority of datasets in general, and in children in particular. The changes can easily be implemented into SPM12.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"46 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Computational Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fncom.2024.1367148","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The first step in spatial normalization of magnetic resonance (MR) images commonly is an affine transformation, which may be vulnerable to image imperfections (such as inhomogeneities or “unusual” heads). Additionally, common software solutions use internal starting estimates to allow for a more efficient computation, which may pose a problem in datasets not conforming to these assumptions (such as those from children). In this technical note, three main questions were addressed: one, does the affine spatial normalization step implemented in SPM12 benefit from an initial inhomogeneity correction. Two, does using a complexity-reduced image version improve robustness when matching “unusual” images. And three, can a blind “brute-force” application of a wide range of parameter combinations improve the affine fit for unusual datasets in particular. A large database of 2081 image datasets was used, covering the full age range from birth to old age. All analyses were performed in Matlab. Results demonstrate that an initial removal of image inhomogeneities improved the affine fit particularly when more inhomogeneity was present. Further, using a complexity-reduced input image also improved the affine fit and was beneficial in younger children in particular. Finally, blindly exploring a very wide parameter space resulted in a better fit for the vast majority of subjects, but again particularly so in infants and young children. In summary, the suggested modifications were shown to improve the affine transformation in the large majority of datasets in general, and in children in particular. The changes can easily be implemented into SPM12.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
优化仿射空间归一化的三步 "蛮力 "法
磁共振(MR)图像空间归一化的第一步通常是仿射变换,这可能容易受到图像缺陷(如不均匀或 "异常 "头部)的影响。此外,常见的软件解决方案使用内部起始估计值来提高计算效率,这可能会给不符合这些假设的数据集(如来自儿童的数据集)带来问题。在本技术说明中,主要讨论了三个问题:其一,SPM12 中实施的仿射空间归一化步骤是否受益于初始不均匀性校正。其二,在匹配 "异常 "图像时,使用复杂度降低的图像版本是否能提高鲁棒性。第三,盲目 "强制 "应用各种参数组合是否能改善仿射拟合,尤其是针对不寻常数据集的拟合。我们使用了一个包含 2081 个图像数据集的大型数据库,涵盖了从出生到老年的所有年龄段。所有分析均在 Matlab 中进行。结果表明,初步去除图像不均匀性后,仿射拟合效果有所改善,尤其是在存在较多不均匀性的情况下。此外,使用复杂度降低的输入图像也能改善仿射拟合效果,尤其是对年龄较小的儿童。最后,盲目探索一个非常宽的参数空间对绝大多数受试者都有更好的拟合效果,尤其是对婴幼儿。总之,建议的修改在绝大多数数据集,尤其是儿童数据集上都能改善仿射变换。这些修改可以很容易地应用到 SPM12 中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
期刊最新文献
Sex differences in brain MRI using deep learning toward fairer healthcare outcomes. Editorial: Computational modeling and machine learning methods in neurodevelopment and neurodegeneration: from basic research to clinical applications. Simulated synapse loss induces depression-like behaviors in deep reinforcement learning. Systematic review of cognitive impairment in drivers through mental workload using physiological measures of heart rate variability. Facial emotion recognition using deep quantum and advanced transfer learning mechanism.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1