Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging
The preservation of kidneys using normothermic machine perfusion (NMP) prior to transplantation has the potential for predictive evaluation of organ quality. Investigations concerning the quantitative assessment of physiological tissue parameters and their dependence on organ function lack in this c...
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MDPI AG
2020-11-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/13/11/289 |
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| author | Wenke Markgraf Jannis Lilienthal Philipp Feistel Christine Thiele Hagen Malberg |
| author_facet | Wenke Markgraf Jannis Lilienthal Philipp Feistel Christine Thiele Hagen Malberg |
| author_sort | Wenke Markgraf |
| collection | DOAJ |
| description | The preservation of kidneys using normothermic machine perfusion (NMP) prior to transplantation has the potential for predictive evaluation of organ quality. Investigations concerning the quantitative assessment of physiological tissue parameters and their dependence on organ function lack in this context. In this study, hyperspectral imaging (HSI) in the wavelength range of 500–995 nm was conducted for the determination of tissue water content (TWC) in kidneys. The quantitative relationship between spectral data and the reference TWC values was established by partial least squares regression (PLSR). Different preprocessing methods were applied to investigate their influence on predicting the TWC of kidneys. In the full wavelength range, the best models for absorbance and reflectance spectra provided R<sub>p</sub><sup>2</sup> values of 0.968 and 0.963, as well as root-mean-square error of prediction (RMSEP) values of 2.016 and 2.155, respectively. Considering an optimal wavelength range (800–980 nm), the best model based on reflectance spectra (R<sub>p</sub><sup>2</sup> value of 0.941, RMSEP value of 3.202). Finally, the visualization of TWC distribution in all pixels of kidneys’ HSI image was implemented. The results show the feasibility of HSI for a non-invasively and accurate TWC prediction in kidneys, which could be used in the future to assess the quality of kidneys during the preservation period. |
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| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T14:58:14Z |
| publishDate | 2020-11-01 |
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| series | Algorithms |
| spelling | doaj.art-c1cbe98cb04f41b3b9218a7701522e972023-11-20T20:25:49ZengMDPI AGAlgorithms1999-48932020-11-01131128910.3390/a13110289Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral ImagingWenke Markgraf0Jannis Lilienthal1Philipp Feistel2Christine Thiele3Hagen Malberg4Institute of Biomedical Engineering, Technische Universität Dresden, 01307 Dresden, GermanyInstitute of Biomedical Engineering, Technische Universität Dresden, 01307 Dresden, GermanyInstitute of Biomedical Engineering, Technische Universität Dresden, 01307 Dresden, GermanyInstitute of Biomedical Engineering, Technische Universität Dresden, 01307 Dresden, GermanyInstitute of Biomedical Engineering, Technische Universität Dresden, 01307 Dresden, GermanyThe preservation of kidneys using normothermic machine perfusion (NMP) prior to transplantation has the potential for predictive evaluation of organ quality. Investigations concerning the quantitative assessment of physiological tissue parameters and their dependence on organ function lack in this context. In this study, hyperspectral imaging (HSI) in the wavelength range of 500–995 nm was conducted for the determination of tissue water content (TWC) in kidneys. The quantitative relationship between spectral data and the reference TWC values was established by partial least squares regression (PLSR). Different preprocessing methods were applied to investigate their influence on predicting the TWC of kidneys. In the full wavelength range, the best models for absorbance and reflectance spectra provided R<sub>p</sub><sup>2</sup> values of 0.968 and 0.963, as well as root-mean-square error of prediction (RMSEP) values of 2.016 and 2.155, respectively. Considering an optimal wavelength range (800–980 nm), the best model based on reflectance spectra (R<sub>p</sub><sup>2</sup> value of 0.941, RMSEP value of 3.202). Finally, the visualization of TWC distribution in all pixels of kidneys’ HSI image was implemented. The results show the feasibility of HSI for a non-invasively and accurate TWC prediction in kidneys, which could be used in the future to assess the quality of kidneys during the preservation period.https://www.mdpi.com/1999-4893/13/11/289hyperspectral imagingdata preprocessingmultivariate data analysispartial least squares regressionkidney tissuewater content |
| spellingShingle | Wenke Markgraf Jannis Lilienthal Philipp Feistel Christine Thiele Hagen Malberg Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging Algorithms hyperspectral imaging data preprocessing multivariate data analysis partial least squares regression kidney tissue water content |
| title | Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging |
| title_full | Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging |
| title_fullStr | Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging |
| title_full_unstemmed | Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging |
| title_short | Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging |
| title_sort | algorithm for mapping kidney tissue water content during normothermic machine perfusion using hyperspectral imaging |
| topic | hyperspectral imaging data preprocessing multivariate data analysis partial least squares regression kidney tissue water content |
| url | https://www.mdpi.com/1999-4893/13/11/289 |
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