优化仿射空间归一化的三步 "蛮力 "法

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
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引用次数: 0

摘要

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