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Medicine

解剖性脑 MRI 的全脑分割及变化点分析--Premanifest 亨廷顿病的应用

Published: June 09, 2018
doi:
10.3791/57256
, , , , ,
1The Russell H. Morgan Department of Radiology and Radiological Science,Johns Hopkins University School of Medicine, 2Center for Imaging Science,Johns Hopkins University, 3Institute for Computational Medicine,Johns Hopkins University, 4Department of Applied Mathematics and Statistics,Johns Hopkins University, 5Division of Neurobiology, Departments of Psychiatry, Neurology, Neuroscience and Pharmacology, and Program in Cellular and Molecular Medicine,Johns Hopkins University School of Medicine, 6F.M. Kirby Research Center for Functional Brain Imaging,Kennedy Krieger Institute, 7Department of Biomedical Engineering,Johns Hopkins University

Summary

本文介绍了一种容积 MRI 数据分析的统计模型, 它确定了 premanifest 亨廷顿病脑萎缩开始时的 “变化点”。利用基于地图集的 T1-weighted 图像分割管道获得的脑容量, 实现了变化点的全脑映射。

Abstract

最近的 MRI 研究进展提供了多种有用的标记来识别神经退行性疾病。在亨廷顿氏病 (HD), 区域脑萎缩开始前许多年在马达发病之前 (在 “premanifest” 期间), 但区域萎缩的时空格局在脑子没有被充分地描绘。在这里, 我们展示了一个在线云计算平台, “MRICloud”, 它提供了基于地图集的全脑分割的 T1-weighted 图像在多个粒度水平, 从而使我们能够访问的区域特征的大脑解剖学。然后, 我们描述了一个回归模型, 它检测出统计学意义上的拐点, 区域脑萎缩开始明显,即“改变点”, 关于疾病进展指数。我们使用 CAG 年龄的产品 (CAP) 评分指数的疾病进展, HD 患者。因此, 从分割管道的容积测量的变化点分析, 提供了关于大脑结构萎缩的顺序和模式的重要信息。本文从一个大的多中心预测-hd 研究中, 阐述了这些技术对 premanifest HD T1-weighted MRI 数据的应用。这种设计可能在一系列神经退行性疾病中有广泛的应用, 以研究脑解剖学的动态变化。

Introduction

磁共振成像 (MRI) 大大提高了我们的能力, 检查大脑解剖和功能的神经退行性疾病1,2,3。T1-weighted 结构 MRI 是常规临床实践中应用最广泛的影像学工具之一, 用于评估脑解剖学及相关病理学。对高分辨率 T1-weighted 图像进行定量分析, 为测量脑变性过程中的解剖变化提供了有用的标志。特别是, 基于分割的量化方法有效地将图像维数从体素水平 ((106)) 降低到解剖结构水平 ((102)), 用于高通量神经信息学4,5. 利用基于 atlas 的方法6、7、8、9将预定义的解剖标签从地图集映射到患者图像, 可以实现自动脑分割。.在基于地图集的方法中, 多地图集算法10、11、12、13、14具有较高的分割精度和鲁棒性。我们集团开发了一个全自动 T1 多地图集分割管线, 具有先进的 diffeomorphic 图像配准算法15, 多阿特拉斯融合方法16,17, 丰富的多地图集库18. 自2015年以来, 该管道已分布在一个云计算平台上, MRICloud19, 并已用于研究神经退行性疾病, 如阿尔茨海默病 (AD)20、21、初级渐进性失语症22, 亨廷顿氏病23。

一旦高分辨率图像被分割成脑结构, 区域特征, 如体积, 可以用来建立数学模型来表征神经解剖学的变化。本文以纵向和/或横断面 MRI 资料为基础, 对时间顺序进行了分析, 并对其进行了统计学意义上的变化。这一统计模型是第一次开发, 以量化基于形状的 diffeomorphometry 年龄在 AD 患者21,24;它后来被改编为研究亨廷顿氏病 (HD) 的大脑结构变化, 以及描述新生儿大脑的发育变化25。在 HD 患者中, 对 cag 年龄产品 (CAP) 评分的变化点进行了界定, 以此作为HTT 26中 cag 扩张程度的指标。众所周知, 巴萎缩是 HD 最早的标志之一, 其次是苍白27。然而, 纹状体的变化与大脑中其他的灰质和白质结构有关, 仍然不清楚。这种关系对我们了解疾病进展至关重要。对所有脑结构的容积变化进行改变点分析, 可能会提供 HD premanifest 阶段脑萎缩的系统信息。

在这里, 我们演示了使用 MRICloud (www.mricloud.org) 进行全脑分割的过程, 以及对 premanifest HD 主题中的容量数据进行更改点分析的步骤。MRI 数据从一大群多中心预测-hd 研究28,29与大约400控制和 premanifest hd 主题收集。基于地图集的分割和变化点分析的结合, 带来了关于大脑结构变化的时空顺序和整个大脑疾病进展模式的独特信息。这些技术可能适用于一系列神经退行性疾病, 各种生物标志物可以映射脑变性。

Protocol

1. 基于 Atlas 的全脑分割 数据准备 转换三维 (3D) T1-weighted 图像, 通常获得与 MPRAGE (磁化准备快速梯度回声) 序列, 从供应商特定的 DICOM (数字成像和通信) 格式, 以分析格式。请注意, 云计算需要将用户的数据传输到远程群集。根据《健康保险可移植性和责任法》 (HIPPA), 从图像文件中删除患者的个人身份信息。注: MRICloud 提供了一个 DICOM 到分析转换器 (https://braingps.mriclou…

Representative Results

使用 1.1-1.3 中描述的过程, 可以从 MRICloud 中获得全脑分割图。在当前版本的阿特拉斯 (V9B), 283 包裹被分割在最佳的粒度 (等级 5), 可以分组到不同的粒度级别, e., 从半球到小叶和包裹, 根据具体的本体定义。图 3显示了两种类型的多级分割在五级, 在轴向和日冕视图。例如, 在粗糙级别, I 型分段定义了前脑、间脑、中脑、后脑和 myelencephalon 的经?…

Discussion

如本文所示, 使用我们的在线平台 MRICloud 可以方便地实现脑 MRI 的全脑分割。T1-weighted MRI 为基础的容积标记已表明是强健和敏感的一系列神经退行性疾病1,2,3。容积测量用于各种下游分析, 如数学建模、特征选择和分类分析, 以协助临床诊断和预后。脑容量的变化点分析可以定量描述疾病进展过程中的脑萎缩。这种统计分析采用…

Disclosures

The authors have nothing to disclose.

Acknowledgements

我们感谢预测高清调查员, 特别是 Pauslen 博士和爱荷华州大学的简·约翰逊博士, 他们慷慨地分享了 MRI 数据, 并对数据分析和结果进行了建设性的讨论。

这项工作由 NIH 赠款 R21 NS098018、P50 NS16375、NS40068、R01 NS086888、R01 NS084957、P41 EB015909、P41 EB015909、R01 EB000975、R01 EB008171 和 U01 NS082085 支持。

Materials

MATLAB Mathworks N/A Version 2015b and above
Dell Workstation Dell Dell Precision T5500 (Intel Xeon CPU)
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References

  1. Paulsen, J. S., et al. Clinical and Biomarker Changes in Premanifest Huntington Disease Show Trial Feasibility: A Decade of the PREDICT-HD Study. Front Aging Neurosci. 6, 78 (2014).
  2. Jack, C. R., et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 9 (1), 119-128 (2010).
  3. Laakso, M. P., et al. Hippocampal volumes in Alzheimer’s disease, Parkinson’s disease with and without dementia, and in vascular dementia: An MRI study. Neurology. 46 (3), 678-681 (1996).
  4. Miller, M. I., Faria, A. V., Oishi, K., Mori, S. High-throughput neuro-imaging informatics. Front Neuroinform. 7, 31 (2013).
  5. Mori, S., Oishi, K., Faria, A. V., Miller, M. I. Atlas-based neuroinformatics via MRI: harnessing information from past clinical cases and quantitative image analysis for patient care. Annu Rev Biomed Eng. 15, 71-92 (2013).
  6. Klein, A., Mensh, B., Ghosh, S., Tourville, J., Hirsch, J. Mindboggle: automated brain labeling with multiple atlases. BMC Med Imaging. 5, 7 (2005).
  7. Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., Hammers, A. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage. 33 (1), 115-126 (2006).
  8. Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de-Solorzano, C. Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans Med Imaging. 28 (8), 1266-1277 (2009).
  9. Lotjonen, J. M. P., et al. Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage. 49 (3), 2352-2365 (2010).
  10. Rohlfing, T., Brandt, R., Menzel, R., Maurer, C. R. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocol microscopy images of bee brains. Neuroimage. 21 (4), 1428-1442 (2004).
  11. Fischl, B., et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron. 33 (3), 341-355 (2002).
  12. Collins, D. L., Holmes, C. J., Peters, T. M., Evans, A. C. Automatic 3-D model-based neuroanatomical segmentation. Human Brain Mapping. 3 (3), 190-208 (1995).
  13. Dawant, B. M., et al. Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, methodology and validation on normal subjects. Ieee Transactions on Medical Imaging. 18 (10), 909-916 (1999).
  14. Wu, G., et al. A generative probability model of joint label fusion for multi-atlas based brain segmentation. Med Image Anal. 18 (6), 881-890 (2014).
  15. Miller, M. I., Trouve, A., Younes, Y. Diffeomorphometry and geodesic positioning systems for human anatomy. Technology. 2, (2013).
  16. Tang, X., et al. Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model. PLoS One. 8 (6), e65591 (2013).
  17. Wang, H., et al. Multi-Atlas Segmentation with Joint Label Fusion. IEEE Trans Pattern Anal Mach Intell. 35 (3), 611-623 (2013).
  18. Wu, D., et al. Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI. Neuroimage. 125, 120-130 (2015).
  19. Mori, S., et al. MRICloud: Delivering High-Throughput MRI Neuroinformatics as Cloud-Based Software as a Service. Computing in Science & Engineering. 18 (5), 21-35 (2016).
  20. Wu, D., Ceritoglu, C., Miller, M. I., Mori, S. Direct estimation of patient attributes from anatomical MRI based on multi-atlas voting. Neuroimage-Clinical. 12, 570-581 (2016).
  21. Miller, M. I., et al. Network Neurodegeneration in Alzheimer’s Disease via MRI Based Shape Diffeomorphometry and High-Field Atlasing. Front Bioeng Biotechnol. 3, 54 (2015).
  22. Faria, A. V., et al. Content-based image retrieval for brain MRI: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis. Neuroimage Clin. 7, 367-376 (2015).
  23. Wu, D., et al. Mapping the order and pattern of brain structural MRI changes using change-point analysis in premanifest Huntington’s disease. Hum Brain Mapp. , (2017).
  24. Younes, L., Albert, M., Miller, M. I., Team, B. R. Inferring changepoint times of medial temporal lobe morphometric change in preclinical Alzheimer’s disease. Neuroimage Clin. 5, 178-187 (2014).
  25. Wu, D., et al. Mapping the critical gestational age at birth that alters brain development in preterm-born infants using multi-modal MRI. NeuroImage. 149, 33-43 (2017).
  26. Zhang, Y., et al. Indexing disease progression at study entry with individuals at-risk for Huntington disease. Am J Med Genet B Neuropsychiatr Genet. 156b (7), 751-763 (2011).
  27. Vonsattel, J. P., et al. Neuropathological classification of Huntington’s disease. J Neuropathol Exp Neurol. 44 (6), 559-577 (1985).
  28. Paulsen, J. S., et al. Detection of Huntington’s disease decades before diagnosis: the Predict-HD study. J Neurol Neurosurg Psychiatry. 79 (8), 874-880 (2008).
  29. Paulsen, J. S., et al. Prediction of manifest Huntington’s disease with clinical and imaging measures: a prospective observational study. Lancet Neurol. 13 (12), 1193-1201 (2014).
  30. Djamanakova, A., et al. Tools for multiple granularity analysis of brain MRI data for individualized image analysis. Neuroimage. 101, 168-176 (2014).
  31. . A package for T1 volumetric analysis of MRIcloud output. R package version 0.0.1 Available from: https://github.com/bcaffo/MRIcloudT1volumetrics (2017)
  32. Aljabar, P., Heckemann, R. A., Hammers, A., Hajnal, J. V., Rueckert, D. Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage. 46 (3), 726-738 (2009).
  33. Huntington Study Group. Unified Huntington’s Disease Rating Scale: reliability and consistency. Mov Disord. 11 (2), 136-142 (1996).
  34. Liang, Z., et al. Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool. PLoS One. 10 (7), e0133533 (2015).

Tags

Brain MRIWhole-brain SegmentationChange-point AnalysisHuntington’s DiseaseNeurodegenerative DiseaseVolumetric AnalysisMRICloudMultiAtlas-based SegmentationQuantitative AnalysisBrain Atrophy
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Wu, D., Faria, A. V., Younes, L., Ross, C. A., Mori, S., Miller, M. I. Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington’s Disease. J. Vis. Exp. (136), e57256, doi:10.3791/57256 (2018).
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