کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10712590 | 1025211 | 2014 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Automated assessment of the quality of diffusion tensor imaging data using color cast of color-encoded fractional anisotropy images
ترجمه فارسی عنوان
ارزیابی خودکار از کیفیت داده های تصویربرداری از تانسور انتشار با استفاده از رنگ آمیزی تصویر های ناهمواری قطبی کدگذاری شده با رنگ
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
فیزیک ماده چگال
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Magnetic Resonance Imaging - Volume 32, Issue 5, June 2014, Pages 446-456
Journal: Magnetic Resonance Imaging - Volume 32, Issue 5, June 2014, Pages 446-456
نویسندگان
Xiaofu He, Wei Liu, Xuzhou Li, Qingli Li, Feng Liu, Virginia A. Rauh, Dazhi Yin, Ravi Bansal, Yunsuo Duan, Alayar Kangarlu, Bradley S. Peterson, Dongrong Xu,