Linear-fitting-based similarity coefficient map for tissue dissimilarity analysis in -w magnetic resonance imaging *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. (20th October 2015) - Yu 余, Shao-De 绍德; Wu 伍, Shi-Bin 世宾; Wang 王, Hao-Yu 浩宇; Wei 魏, Xin-Hua 新华; Chen 陈, Xin 鑫; Pan 潘, Wan-Long 万龙; Hu, Jiani; Xie 谢, Yao-Qin 耀钦
Abstract: Similarity coefficient mapping (SCM) aims to improve the morphological evaluation of weighted magnetic resonance imaging 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 . 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.