中国物理B ›› 2015, Vol. 24 ›› Issue (12): 128711-128711.doi: 10.1088/1674-1056/24/12/128711

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇    下一篇

Linear-fitting-based similarity coefficient map for tissue dissimilarity analysis in T2*-w magnetic resonance imaging

余绍德a b, 伍世宾a b, 王浩宇c, 魏新华d, 陈鑫d, 潘万龙e, Hu Jianif, 谢耀钦a   

  1. a Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
    b University of Chinese Academy of Sciences, Beijing 100049, China;
    c Department of Experimental Pathology, Beijing Institute of Radiation Medicine, Beijing 100049, China;
    d Department of Radiology, Guangzhou First Hospital, Guangzhou 510080, China;
    e Institute of Medical Imaging, North Sichuan Medical University, Nanchong 637007, China;
    f Department of Radiology, Wayne State University, Detroit 48201, USA
  • 收稿日期:2015-04-03 修回日期:2015-08-04 出版日期:2015-12-05 发布日期:2015-12-05
  • 通讯作者: Xie Yao-Qin E-mail:yq.xie@siat.ac.cn
  • 基金资助:
    Project supported in part by the National High Technology Research and Development Program of China (Grant Nos. 2015AA043203 and 2012AA02A604), the National Natural Science Foundation of China (Grant Nos. 81171402, 61471349, and 81501463), the Innovative Research Team Program of Guangdong Province, China (Grant No. 2011S013), the Science and Technological Program for Higher Education, Science and Research, and Health Care Institutions of Guangdong Province, China (Grant No. 2011108101001), the Natural Science Foundation of Guangdong Province, China (Grant No. 2014A030310360), the Fundamental Research Program of Shenzhen City, China (Grant No. JCYJ20140417113430639), and Beijing Center for Mathematics and Information Interdisciplinary Sciences, China.

Linear-fitting-based similarity coefficient map for tissue dissimilarity analysis in T2*-w magnetic resonance imaging

Yu Shao-De (余绍德)a b, Wu Shi-Bin (伍世宾)a b, Wang Hao-Yu (王浩宇)c, Wei Xin-Hua (魏新华)d, Chen Xin (陈鑫)d, Pan Wan-Long (潘万龙)e, Hu Jianif, Xie Yao-Qin (谢耀钦)a   

  1. a Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;
    b University of Chinese Academy of Sciences, Beijing 100049, China;
    c Department of Experimental Pathology, Beijing Institute of Radiation Medicine, Beijing 100049, China;
    d Department of Radiology, Guangzhou First Hospital, Guangzhou 510080, China;
    e Institute of Medical Imaging, North Sichuan Medical University, Nanchong 637007, China;
    f Department of Radiology, Wayne State University, Detroit 48201, USA
  • Received:2015-04-03 Revised:2015-08-04 Online:2015-12-05 Published:2015-12-05
  • Contact: Xie Yao-Qin E-mail:yq.xie@siat.ac.cn
  • Supported by:
    Project supported in part by the National High Technology Research and Development Program of China (Grant Nos. 2015AA043203 and 2012AA02A604), the National Natural Science Foundation of China (Grant Nos. 81171402, 61471349, and 81501463), the Innovative Research Team Program of Guangdong Province, China (Grant No. 2011S013), the Science and Technological Program for Higher Education, Science and Research, and Health Care Institutions of Guangdong Province, China (Grant No. 2011108101001), the Natural Science Foundation of Guangdong Province, China (Grant No. 2014A030310360), the Fundamental Research Program of Shenzhen City, China (Grant No. JCYJ20140417113430639), and Beijing Center for Mathematics and Information Interdisciplinary Sciences, China.

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摘要： Similarity coefficient mapping (SCM) aims to improve the morphological evaluation of T2* weighted magnetic resonance imaging (T2*-w MRI). However, how to interpret the generated SCM map is still pending. Moreover, is it probable to extract tissue dissimilarity messages based on the theory behind SCM? The primary purpose of this paper is to address these two questions. First, the theory of SCM was interpreted from the perspective of linear fitting. Then, a term was embedded for tissue dissimilarity information. Finally, our method was validated with sixteen human brain image series from multi-echo T2*-w MRI. Generated maps were investigated from signal-to-noise ratio (SNR) and perceived visual quality, and then interpreted from intra- and inter-tissue intensity. Experimental results show that both perceptibility of anatomical structures and tissue contrast are improved. More importantly, tissue similarity or dissimilarity can be quantified and cross-validated from pixel intensity analysis. This method benefits image enhancement, tissue classification, malformation detection and morphological evaluation.

关键词: T2*-w magnetic resonance imaging, similarity coefficient map, linear fitting, tissue dissimilarity

Abstract: Similarity coefficient mapping (SCM) aims to improve the morphological evaluation of T2* weighted magnetic resonance imaging (T2*-w MRI). However, how to interpret the generated SCM map is still pending. Moreover, is it probable to extract tissue dissimilarity messages based on the theory behind SCM? The primary purpose of this paper is to address these two questions. First, the theory of SCM was interpreted from the perspective of linear fitting. Then, a term was embedded for tissue dissimilarity information. Finally, our method was validated with sixteen human brain image series from multi-echo T2*-w MRI. Generated maps were investigated from signal-to-noise ratio (SNR) and perceived visual quality, and then interpreted from intra- and inter-tissue intensity. Experimental results show that both perceptibility of anatomical structures and tissue contrast are improved. More importantly, tissue similarity or dissimilarity can be quantified and cross-validated from pixel intensity analysis. This method benefits image enhancement, tissue classification, malformation detection and morphological evaluation.

Key words: T2*-w magnetic resonance imaging, similarity coefficient map, linear fitting, tissue dissimilarity

中图分类号:  (Magnetic resonance imaging)

  • 87.61.-c
87.57.C- (Image quality) 02.10.Ud (Linear algebra)