![]() Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. The maps could be used for material identification and radiation dose calculation. 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. Van Hedent, Steven Klahr, Paul Wei, Zhouping Helo, Rose Al Liang, Fan Qian, Pengjiang Pereira, Gisele C. Su, Kuan-Hao Kuo, Jung-Wen Jordan, David W. Machine learning-based dual-energy CT parametric mapping The robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. 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. 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 approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. 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. 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. ![]() Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Yang, Xue Beason-Held, Lori Resnick, Susan M. ![]() Robust biological parametric mapping: an improved technique for multimodal brain image analysis
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