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.
Highlights
Diffusion tensor imaging (DTI) [1] and diffusion kurtosis imaging (DKI) [2] metrics are influenced by cellular tissue properties and tend to alter significantly during the life span [3,4] or as a result of various pathologies, including schizophrenia [5] and neurodegenerative diseases, such as mild cognitive impairment [6], Alzheimer’s, Parkinson’s and multiple sclerosis [7,8,9,10,11,12,13]
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
Our work shows that excluding affected voxels from statistical analyses allows one to reduce confounding effects of mixing tissues and improves between-group comparisons
Summary
Diffusion tensor imaging (DTI) [1] and diffusion kurtosis imaging (DKI) [2] metrics are influenced by cellular tissue properties and tend to alter significantly during the life span [3,4] or as a result of various pathologies, including schizophrenia [5] and neurodegenerative diseases, such as mild cognitive impairment [6], Alzheimer’s, Parkinson’s and multiple sclerosis [7,8,9,10,11,12,13] They are often used in between-group comparisons of patients versus control groups and for studying the correlations with age or cognitive performance. Problems might arise due to alignment inaccuracies, arbitrary smoothing and clustering procedures, as well as due to requirements for multiple-comparison corrections providing challenges for valid statistical inferences [21]
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