![]() Yang, Xue Beason-Held, Lori Resnick, Susan M. Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, region of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrics. Recently, biological parametric mapping has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. Numerically solve and graphically display tangent lines and integrals. New data plotting and curve fitting features. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. a powerful, easy-to-use, equation plotter with numerical and calculus features: Graph Cartesian functions, relations, and inequalities, plus polar, parametric, and ordinary differential equations. Graphmatica for Windows and Macs is distributed free of charge for evaluation purposes. ![]() It runs on Microsoft Windows (all versions), Mac OS X 10.5 and higher, and iOS 5.0 and higher. Please take the time to browse through the help file before you start using Graphmatica, or you may never notice some of its more subtle advanced features. Graphmatica is a graphing program created by Keith Hertzer, 1 a graduate of the University of California, Berkeley. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. This will install the program, create icons, and set up your registry so that Graphmatica starts up when you double-click on a. To enable widespread application of this approach, we introduce robust regression and robust inference in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. Machine learning-based dual-energy CT parametric mapping Graphmatica 2.0 e software# ![]() Su, Kuan-Hao Kuo, Jung-Wen Jordan, David W. Van Hedent, Steven Klahr, Paul Wei, Zhouping Helo, Rose Al Liang, Fan Qian, Pengjiang Pereira, Gisele C. The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (Ï e), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. ![]()
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