Local texture descriptors for the assessment of differences in diffusion magnetic resonance imaging of the brain
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Descriptors extracted from magnetic resonance imaging (MRI) of the brain can be employed to locate and characterize a wide range of pathologies. Scalar measures are typically derived within a single-voxel unit, but neighborhood-based texture measures can also be applied. In this work, we propose a new set of descriptors to compute local texture characteristics from scalar measures of diffusion tensor imaging (DTI), such as mean and radial diffusivity, and fractional anisotropy.
We employ weighted rotational invariant local operators, namely standard deviation, inter-quartile range, coefficient of variation, quartile coefficient of variation and skewness. Sensitivity and specificity of those texture descriptors were analyzed with tract-based spatial statistics of the white matter on a diffusion MRI group study of elderly healthy controls, patients with mild cognitive impairment (MCI), and mild or moderate Alzheimer’s disease (AD). In addition, robustness against noise has been assessed with a realistic diffusion-weighted imaging phantom and the contamination of the local neighborhood with gray matter has been measured.
The new texture operators showed an increased ability for finding formerly undetected differences between groups compared to conventional DTI methods. In particular, the coefficient of variation, quartile coefficient of variation, standard deviation and inter-quartile range of the mean and radial diffusivity detected significant differences even between previously not significantly discernible groups, such as MCI versus moderate AD and mild versus moderate AD. The analysis provided evidence of low contamination of the local neighborhood with gray matter and high robustness against noise.
The local operators applied here enhance the identification and localization of areas of the brain where cognitive impairment takes place and thus indicate them as promising extensions in diffusion MRI group studies.
KeywordsLocal texture Diffusion tensor imaging Alzheimer’s disease White matter
The authors acknowledge the company QDiagnóstica, Valladolid, Spain, whose facility has been used for data acquisition purposes and thank Dr. Miguel Angel Tola-Arribas for his valuable help with the recruitment and diagnosis of patients.
Funding Felix Sebastian Leo Thomsen received a doctoral fellowship from Consejo Nacional de Investigaciones Científicas y Técnicas of Argentina (CONICET). This work was partially funded by the Universidad Nacional del Sur (PGI 24/K061). Rodrigo de Luis García has received research grants from Ministerio de Ciencia e Innovación of Spain (TEC 2013-44194-P), Fondo de Investigaciones Sanitarias (PI 11-01492) the Consejería de Sanidad de Castilla y León (BIO/VA30/14).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animals right
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
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