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Magn Reson Imaging. 2014 Jun;32(5):446-56. doi: 10.1016/j.mri.2014.01.013. Epub 2014 Jan 28.

Automated assessment of the quality of diffusion tensor imaging data using color cast of color-encoded fractional anisotropy images.

Author information

1
Center for Developmental Neuropsychiatry, Department of Psychiatry, Columbia University & New York State Psychiatric Institute, New York, NY 10032, USA.
2
Key laboratory of Brain Functional Genomics (MOE & STCSM), Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, Shanghai, 20062, China.
3
MRI Unit, Department of Psychiatry, Columbia University & New York State Psychiatric Institute, New York, NY 10032, USA.
4
Columbia Center for Children's Environmental Health, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
5
Center for Developmental Neuropsychiatry, Department of Psychiatry, Columbia University & New York State Psychiatric Institute, New York, NY 10032, USA; Key laboratory of Brain Functional Genomics (MOE & STCSM), Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, Shanghai, 20062, China.
6
Center for Developmental Neuropsychiatry, Department of Psychiatry, Columbia University & New York State Psychiatric Institute, New York, NY 10032, USA. Electronic address: dx2103@columbia.edu.

Abstract

Diffusion tensor imaging (DTI) data often suffer from artifacts caused by motion. These artifacts are especially severe in DTI data from infants, and implementing tight quality controls is therefore imperative for DTI studies of infants. Currently, routine procedures for quality assurance of DTI data involve the slice-wise visual inspection of color-encoded, fractional anisotropy (CFA) images. Such procedures often yield inconsistent results across different data sets, across different operators who are examining those data sets, and sometimes even across time when the same operator inspects the same data set on two different occasions. We propose a more consistent, reliable, and effective method to evaluate the quality of CFA images automatically using their color cast, which is calculated on the distribution statistics of the 2D histogram in the color space as defined by the International Commission on Illumination (CIE) on lightness and a and b (LAB) for the color-opponent dimensions (also known as the CIELAB color space) of the images. Experimental results using DTI data acquired from neonates verified that this proposed method is rapid and accurate. The method thus provides a new tool for real-time quality assurance for DTI data.

KEYWORDS:

CIELAB color space; Color-encoded fractional anisotropy (CFA); Diffusion tensor imaging (DTI); Quality assessment

PMID:
24637081
PMCID:
PMC4169879
DOI:
10.1016/j.mri.2014.01.013
[Indexed for MEDLINE]
Free PMC Article

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