%0 Journal Article
%T Automatic Mapping Extraction from Multiecho T2-Star Weighted Magnetic Resonance Images for Improving Morphological Evaluations in Human Brain
%A Shaode Yu
%A Shibin Wu
%A Yaoqin Xie
%J Computational and Mathematical Methods in Medicine
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/202309
%X Mapping extraction is useful in medical image analysis. Similarity coefficient mapping (SCM) replaced signal response to time course in tissue similarity mapping with signal response to TE changes in multiecho T2-star weighted magnetic resonance imaging without contrast agent. Since different tissues are with different sensitivities to reference signals, a new algorithm is proposed by adding a sensitivity index to SCM. It generates two mappings. One measures relative signal strength (SSM) and the other depicts fluctuation magnitude (FMM). Meanwhile, the new method is adaptive to generate a proper reference signal by maximizing the sum of contrast index (CI) from SSM and FMM without manual delineation. Based on four groups of images from multiecho T2-star weighted magnetic resonance imaging, the capacity of SSM and FMM in enhancing image contrast and morphological evaluation is validated. Average contrast improvement index (CII) of SSM is 1.57, 1.38, 1.34, and 1.41. Average CII of FMM is 2.42, 2.30, 2.24, and 2.35. Visual analysis of regions of interest demonstrates that SSM and FMM show better morphological structures than original images, T2-star mapping and SCM. These extracted mappings can be further applied in information fusion, signal investigation, and tissue segmentation. 1. Introduction As a routine examination technique, magnetic resonance imaging (MRI) has been extensively used in clinical diagnosis. Pixel intensities on conventional MR images are dependent on a complex mix of proton density (PD), longitudinal relaxation time (T1), and transverse relaxation time (T2) or T2-star relaxation time based on the initial scan setting [1¨C3]. Many types of MRI have been invented to reflect physical and physiological properties, such as T2-star weighted MRI and susceptibility weighted imaging (SWI) [4, 5]. Among these techniques, T2-star weighted MRI has been widely used to reveal functional and morphological characteristics by taking advantage of differences in tissue properties [6¨C10]. As an essential modality, T2-star weighted MRI is capable of producing a large number of medical images by selecting optimal cross section and imaging parameters for specific emphasis. How to dig out valuable messages from a series of MR images is an important project for various applications. Quantitative MRI (Q-MRI) is one way to extract tissue-intrinsic information from a series of MR images [6¨C18]. Conventional MRI focuses on qualitative visual assessment of anatomy and disease. It interprets anatomic changes when there is visibly detectable difference in signal
%U http://www.hindawi.com/journals/cmmm/2013/202309/