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Background and objective: Phase difference enhanced imaging (PADRE), a new phase-related MRI technique, can enhance both paramagnetic and diamagnetic substances, and select which phases to be enhanced. Utilizing these characteristics, we developed color map of PADRE (Color PADRE), which enables simultaneous visualization of myelin-rich structures and veins. Our aim was to determine whether Color PADRE is sufficient to delineate the characteristics of non-gadolinium-enhancing T2-hyperintense regions related with metastatic tumors (MTs), diffuse astrocytomas (DAs) and glioblastomas (GBs), and whether it can contribute to the differentiation of MTs from GBs. Methods: Color PADRE images of 11 patients with MTs, nine with DAs and 17 with GBs were created by combining tissue-enhanced, vessel-enhanced and magnitude images of PADRE, and then retrospectively reviewed. First, predominant visibility of superficial white matter and deep medullary veins within non-gadolinium-enhancing T2-hyperintense regions were compared among the three groups. Then, the discriminatory power to differentiate MTs from GBs was assessed using receiver operating characteristic analysis. Results: The degree of visibility of superficial white matter was significantly better in MTs than in GBs (p = 0.017), better in GBs than in DAs (p = 0.014), and better in MTs than in DAs (p = 0.0021). On the contrary, the difference in the visibility of deep medullary veins was not significant (p = 0.065). The area under the receiver operating characteristic curve to discriminate MTs from GBs was 0.76 with a sensitivity of 80% and specificity of 64%. Conclusion: Visibility of superficial white matter on Color PADRE reflects inferred differences in the proportion of vasogenic edema and tumoral infiltration within non-gadolinium-enhancing T2-hyperintense regions of MTs, DAs and GBs. Evaluation of peritumoral areas on Color PADRE can help to distinguish MTs from GBs.
Pubmed ID: 30298047
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
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