A comparison of parametric and integrative approaches for X-ray fluorescence analysis applied to a Stroke model
© International Union of Crystallography, 2018 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 q...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Published: |
Blackwell Publishing
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/70753 |
| _version_ | 1848762292594802688 |
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| author | Crawford, A. Sylvain, N. Hou, H. Hackett, Mark Pushie, M. Pickering, I. George, G. Kelly, M. |
| author_facet | Crawford, A. Sylvain, N. Hou, H. Hackett, Mark Pushie, M. Pickering, I. George, G. Kelly, M. |
| author_sort | Crawford, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © International Union of Crystallography, 2018 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. |
| first_indexed | 2025-11-14T10:45:15Z |
| format | Journal Article |
| id | curtin-20.500.11937-70753 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:45:15Z |
| publishDate | 2018 |
| publisher | Blackwell Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-707532020-06-15T03:47:37Z A comparison of parametric and integrative approaches for X-ray fluorescence analysis applied to a Stroke model Crawford, A. Sylvain, N. Hou, H. Hackett, Mark Pushie, M. Pickering, I. George, G. Kelly, M. © International Union of Crystallography, 2018 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. 2018 Journal Article http://hdl.handle.net/20.500.11937/70753 10.1107/S1600577518010895 Blackwell Publishing restricted |
| spellingShingle | Crawford, A. Sylvain, N. Hou, H. Hackett, Mark Pushie, M. Pickering, I. George, G. Kelly, M. A comparison of parametric and integrative approaches for X-ray fluorescence analysis applied to a Stroke model |
| title | A comparison of parametric and integrative approaches for X-ray fluorescence analysis applied to a Stroke model |
| title_full | A comparison of parametric and integrative approaches for X-ray fluorescence analysis applied to a Stroke model |
| title_fullStr | A comparison of parametric and integrative approaches for X-ray fluorescence analysis applied to a Stroke model |
| title_full_unstemmed | A comparison of parametric and integrative approaches for X-ray fluorescence analysis applied to a Stroke model |
| title_short | A comparison of parametric and integrative approaches for X-ray fluorescence analysis applied to a Stroke model |
| title_sort | comparison of parametric and integrative approaches for x-ray fluorescence analysis appliedâ to a stroke model |
| url | http://hdl.handle.net/20.500.11937/70753 |