alexa Novel Diffusion-Kurtosis-Informed Template Reduces Distortions due to Partial Volume Effects and Improves Statistical between-Group Comparisons | OMICS International| Abstract
ISSN: 2161-0460

Journal of Alzheimers Disease & Parkinsonism
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  • Research Article   
  • J Alzheimers Dis Parkinsonism 2017, Vol 7(6): 393
  • DOI: 10.4172/2161-0460.1000393

Novel Diffusion-Kurtosis-Informed Template Reduces Distortions due to Partial Volume Effects and Improves Statistical between-Group Comparisons

Farida Grinberg1,2*, Ezequiel Farrher1, Xiang Gao1, Kerstin Konrad3,4,5, Irene Neuner1,4,6 and Jon Shah N1,2,4
1Institute of Neuroscience and Medicine-4, , Forschungszentrum Juelich GmbH, 52425 Juelich, Germany
2Department of Neurology, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany
3Child Neuropsychology Section, Department of Child and Adolescent Psychiatry and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany
4JARA - BRAIN - Translational Medicine, , Germany
5Institute of Neuroscience and Medicine-3, , Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
6Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Germany
*Corresponding Author : Farida Grinberg, Institute of Neuroscience and Medicine-4, Forschungszentrum Juelich GmbH, Juelich, 52425, Germany, Tel: +4902461611920, Fax: +4902461612820, Email: [email protected]

Received Date: Oct 10, 2017 / Accepted Date: Oct 23, 2017 / Published Date: Oct 30, 2017

Abstract

Objective: Quantitative diffusion magnetic resonance imaging measures carry information about microstructural properties of the underlying tissue. Proper elucidation of their differences in healthy state and pathology, such as Alzheimer’s or Parkinson’s diseases, requires that these measures must be specific for the tissue or anatomic region of interest. However, they are often subjected to biases caused by partial volume effects and leading to erroneous analyses. The purpose of this work was to develop a novel tool allowing one to eliminate affected voxels from statistical analyses and, thus, improve accuracy of the derived measures and enhance reliability of between-group comparisons.
Methods: In vivo diffusion kurtosis measurements were performed with a whole-body 3T Siemens MAGNETOM scanner for two differently aged groups of healthy volunteers. Mean values of typical diffusion tensor and kurtosis tensor metrics were estimated for 20 white matter anatomic regions. Relative differences between the group mean parameters in percentage and Cohen’s d values, as well as p-values of two-sided t-test analysis were evaluated before and after correction for partial volume effects.
Results: We showed that using the tissue-specific features of diffusion kurtosis distributions allows one to reduce contamination of white matter structures by partial volume effects from neighbouring grey matter regions and cerebrospinal fluid. The performance of the developed method was demonstrated in the semi-automatic atlasbased comparison of two differently aged groups of healthy subjects showing that, after correction, the effect sizes of between-group differences in many regional diffusion indices become larger, whereas p-values of the t-tests decrease.
Conclusion: Our work shows that excluding affected voxels from statistical analyses allows one to reduce confounding effects of mixing tissues and improves between-group comparisons. The proposed method is expected to be especially useful for detection of subtle between-group differences and longitudinal changes in studies of neurodegenerative pathologies and ageing associated with white matter atrophy.

Keywords: Brain; Microstructure; Diffusion kurtosis imaging; Partial volume effects; Between-group comparisons; Atlas-based statistical analysis

Citation: Grinberg F, Farrher E, Gao X, Konrad K, Neuner I, et al. (2017) Novel Diffusion-Kurtosis-Informed Template Reduces Distortions due to Partial Volume Effects and Improves Statistical between-Group Comparisons. J Alzheimers Dis Parkinsonism 7: 393. Doi: 10.4172/2161-0460.1000393

Copyright: © 2017 Grinberg F, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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