Dual- and multi-energy CT: approach to functional imaging

The energy spectrum of X-ray photons after passage through an absorber contains information about its elemental composition. Thus, tissue characterisation becomes feasible provided that absorption characteristics can be measured or differentiated. Dual-energy CT uses two X-ray spectra enabling material differentiation by analysing material-dependent photo-electric and Compton effects. Elemental concentrations can thereby be determined using three-material decomposition algorithms. In comparison to dual-energy CT used in clinical practice, recently developed energy-sensitive photon-counting detectors sample the material-specific attenuation curves at multiple energy levels and within narrow energy bands; the latter allows the detection of element-specific, k-edge discontinuities of the photo-electric cross section. Multi-energy CT imaging therefore is able to concurrently identify multiple materials with increased accuracy. These specific data on material distribution provide information beyond morphological CT, and approach functional imaging. This article reviews the principles of dual- and multi-energy CT imaging, hardware approaches and clinical applications

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior