A comparison of parametric and integrative approaches for X‐ray fluorescence analysis applied to a Stroke model. (8th October 2018) - Crawford, Andrew M.; Sylvain, Nicole J.; Hou, Huishu; Hackett, Mark J.; Pushie, M. Jake; Pickering, Ingrid J.; George, Graham N.; Kelly, Michael E.
Abstract : With scientific advances, X‐ray fluorescence imaging has rapidly been adopted in multidisciplinary research investigating disease‐induced changes in the metallome. This, with the apparent lack of prior literature comparing binning (described as a crude but fast approach for visualizing data `on the fly' during collection) with fitting (a refined approach which deconvolutes overlapping emission lines), has created a need to document the differences between both and highlight unfortunate errors and artifacts in interpretation that can arise when the binned answers from data collection are accepted without further refinement by fitting. Abstract : Synchrotron X‐ray fluorescence imaging enables visualization and quantification of microscopic distributions of elements. This versatile technique has matured to the point where it is used in a wide range of research fields. The method can be used to quantitate the levels of different elements in the image on a pixel‐by‐pixel basis. Two approaches to X‐ray fluorescence image analysis are commonly used, namely, (i) integrative analysis, or window binning, which simply sums the numbers of all photons detected within a specific energy region of interest; and (ii) parametric analysis, or fitting, in which emission spectra are represented by the sum of parameters representing a series of peaks and other contributing factors. This paper presents a quantitative comparison between these two methods of image analysis using X‐ray fluorescence imaging of mouse brain‐tissue sections; it is shown that substantial errors can result when data from overlapping emission lines are binned rather than fitted. These differences are explored using two different digital signal processing data‐acquisition systems with different count‐rate and emission‐line resolution characteristics. Irrespective of the digital signal processing electronics, there are substantial differences in quantitation between the two approaches. Binning analyses are thus shown to contain significant errors that not only distort the data but in some cases result in complete reversal of trends between different tissue regions.