公开可用的线性 MRI 立体定向配准技术比较。
A comparison of publicly available linear MRI stereotaxic registration techniques.
机构信息
NeuroImaging and Surgical Tools Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
NeuroImaging and Surgical Tools Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
出版信息
Neuroimage. 2018 Jul 1;174:191-200. doi: 10.1016/j.neuroimage.2018.03.025. Epub 2018 Mar 13.
INTRODUCTION
Linear registration to a standard space is one of the major steps in processing and analyzing magnetic resonance images (MRIs) of the brain. Here we present an overview of linear stereotaxic MRI registration and compare the performance of 5 publicly available and extensively used linear registration techniques in medical image analysis.
METHODS
A set of 9693 T1-weighted MR images were obtained for testing from 4 datasets: ADNI, PREVENT-AD, PPMI, and HCP, two of which have multi-center and multi-scanner data and three of which have longitudinal data. Each individual native image was linearly registered to the MNI ICBM152 average template using five versions of MRITOTAL from MINC tools, FLIRT from FSL, two versions of Elastix, spm_affreg from SPM, and ANTs linear registration techniques. Quality control (QC) images were generated from the registered volumes and viewed by an expert rater to assess the quality of the registrations. The QC image contained 60 sub-images (20 of each of axial, sagittal, and coronal views at different levels throughout the brain) overlaid with contours of the ICBM152 template, enabling the expert rater to label the registration as acceptable or unacceptable. The performance of the registration techniques was then compared across different datasets. In addition, the effect of image noise, intensity non-uniformity, age, head size, and atrophy on the performance of the techniques was investigated by comparing differences between age, scaling factor, ventricle volume, brain volume, and white matter hyperintensity (WMH) volumes between passed and failed cases for each method.
RESULTS
The average registration failure rate among all datasets was 27.41%, 27.14%, 12.74%, 13.03%, 0.44% for the five versions of MRITOTAL techniques, 8.87% for ANTs, 11.11% for FSL, 12.35% for Elastix Affine, 24.40% for Elastix Similarity, and 30.66% for SPM. There were significant effects of signal to noise ratio, image intensity non-uniformity estimates, as well as age, head size, and atrophy related changes between passed and failed registrations.
CONCLUSION
Our experiments show that the Revised BestLinReg had the best performance among the evaluated registration techniques while all techniques performed worse for images with higher levels of noise and non-uniformity as well as atrophy related changes.
简介
线性配准到标准空间是处理和分析大脑磁共振图像（MRI）的主要步骤之一。在这里，我们概述了线性立体定向 MRI 配准，并比较了 5 种广泛使用的线性配准技术在医学图像分析中的性能。
方法
我们从 4 个数据集（ADNI、PREVENT-AD、PPMI 和 HCP）中获得了一组 9693 个 T1 加权 MRI 测试图像，其中两个数据集具有多中心和多扫描仪数据，三个数据集具有纵向数据。使用 MINC 工具中的 5 个版本的 MRITOTAL、FSL 中的 FLIRT、2 个版本的 Elastix、SPM 中的 spm_affreg 和 ANTs 线性配准技术，将每个个体的原始图像线性配准到 MNI ICBM152 平均模板。从配准后的体积生成质量控制（QC）图像，并由专家评分员查看，以评估配准的质量。QC 图像包含 60 个子图像（每个轴位、矢状位和冠状位各 20 个，分布在大脑的不同水平），并叠加有 ICBM152 模板的轮廓，使专家评分员能够将配准标记为可接受或不可接受。然后比较不同数据集之间的配准技术性能。此外，通过比较每个方法通过和失败案例之间的年龄、缩放因子、脑室体积、脑体积和白质高信号（WMH）体积之间的差异，研究了图像噪声、强度不均匀性、年龄、头部大小和萎缩对技术性能的影响。
结果
所有数据集的平均配准失败率分别为 5 个版本的 MRITOTAL 技术的 27.41%、27.14%、12.74%、13.03%和 0.44%，ANTS 为 8.87%，FSL 为 11.11%，Elastix Affine 为 12.35%，Elastix Similarity 为 24.40%，SPM 为 30.66%。通过和失败的配准之间存在显著的信号噪声比、图像强度不均匀性估计以及年龄、头部大小和萎缩相关变化的影响。
结论
我们的实验表明，在评估的配准技术中，修订后的 BestLinReg 表现最好，而所有技术在噪声和强度不均匀性水平较高以及与萎缩相关的变化时表现更差。
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