Cargando…
Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging
Purpose. The purpose of this study was to investigate statistical differences with MR perfusion imaging features that reflect the dynamics of Gadolinium-uptake in MS lesions using dynamic texture parameter analysis (DTPA). Methods. We investigated 51 MS lesions (25 enhancing, 26 nonenhancing lesions...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4738712/ https://www.ncbi.nlm.nih.gov/pubmed/26885524 http://dx.doi.org/10.1155/2016/9578139 |
_version_ | 1782413650185682944 |
---|---|
author | Verma, Rajeev K. Slotboom, Johannes Locher, Cäcilia Heldner, Mirjam R. Weisstanner, Christian Abela, Eugenio Kellner-Weldon, Frauke Zbinden, Martin Kamm, Christian P. Wiest, Roland |
author_facet | Verma, Rajeev K. Slotboom, Johannes Locher, Cäcilia Heldner, Mirjam R. Weisstanner, Christian Abela, Eugenio Kellner-Weldon, Frauke Zbinden, Martin Kamm, Christian P. Wiest, Roland |
author_sort | Verma, Rajeev K. |
collection | PubMed |
description | Purpose. The purpose of this study was to investigate statistical differences with MR perfusion imaging features that reflect the dynamics of Gadolinium-uptake in MS lesions using dynamic texture parameter analysis (DTPA). Methods. We investigated 51 MS lesions (25 enhancing, 26 nonenhancing lesions) of 12 patients. Enhancing lesions (n = 25) were prestratified into enhancing lesions with increased permeability (EL+; n = 11) and enhancing lesions with subtle permeability (EL−; n = 14). Histogram-based feature maps were computed from the raw DSC-image time series and the corresponding texture parameters were analyzed during the inflow, outflow, and reperfusion time intervals. Results. Significant differences (p < 0.05) were found between EL+ and EL− and between EL+ and nonenhancing inactive lesions (NEL). Main effects between EL+ versus EL− and EL+ versus NEL were observed during reperfusion (mainly in mean and standard deviation (SD): EL+ versus EL− and EL+ versus NEL), while EL− and NEL differed only in their SD during outflow. Conclusion. DTPA allows grading enhancing MS lesions according to their perfusion characteristics. Texture parameters of EL− were similar to NEL, while EL+ differed significantly from EL− and NEL. Dynamic texture analysis may thus be further investigated as noninvasive endogenous marker of lesion formation and restoration. |
format | Online Article Text |
id | pubmed-4738712 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-47387122016-02-16 Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging Verma, Rajeev K. Slotboom, Johannes Locher, Cäcilia Heldner, Mirjam R. Weisstanner, Christian Abela, Eugenio Kellner-Weldon, Frauke Zbinden, Martin Kamm, Christian P. Wiest, Roland Biomed Res Int Research Article Purpose. The purpose of this study was to investigate statistical differences with MR perfusion imaging features that reflect the dynamics of Gadolinium-uptake in MS lesions using dynamic texture parameter analysis (DTPA). Methods. We investigated 51 MS lesions (25 enhancing, 26 nonenhancing lesions) of 12 patients. Enhancing lesions (n = 25) were prestratified into enhancing lesions with increased permeability (EL+; n = 11) and enhancing lesions with subtle permeability (EL−; n = 14). Histogram-based feature maps were computed from the raw DSC-image time series and the corresponding texture parameters were analyzed during the inflow, outflow, and reperfusion time intervals. Results. Significant differences (p < 0.05) were found between EL+ and EL− and between EL+ and nonenhancing inactive lesions (NEL). Main effects between EL+ versus EL− and EL+ versus NEL were observed during reperfusion (mainly in mean and standard deviation (SD): EL+ versus EL− and EL+ versus NEL), while EL− and NEL differed only in their SD during outflow. Conclusion. DTPA allows grading enhancing MS lesions according to their perfusion characteristics. Texture parameters of EL− were similar to NEL, while EL+ differed significantly from EL− and NEL. Dynamic texture analysis may thus be further investigated as noninvasive endogenous marker of lesion formation and restoration. Hindawi Publishing Corporation 2016 2016-01-13 /pmc/articles/PMC4738712/ /pubmed/26885524 http://dx.doi.org/10.1155/2016/9578139 Text en Copyright © 2016 Rajeev K. Verma et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Verma, Rajeev K. Slotboom, Johannes Locher, Cäcilia Heldner, Mirjam R. Weisstanner, Christian Abela, Eugenio Kellner-Weldon, Frauke Zbinden, Martin Kamm, Christian P. Wiest, Roland Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging |
title | Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging |
title_full | Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging |
title_fullStr | Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging |
title_full_unstemmed | Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging |
title_short | Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging |
title_sort | characterization of enhancing ms lesions by dynamic texture parameter analysis of dynamic susceptibility perfusion imaging |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4738712/ https://www.ncbi.nlm.nih.gov/pubmed/26885524 http://dx.doi.org/10.1155/2016/9578139 |
work_keys_str_mv | AT vermarajeevk characterizationofenhancingmslesionsbydynamictextureparameteranalysisofdynamicsusceptibilityperfusionimaging AT slotboomjohannes characterizationofenhancingmslesionsbydynamictextureparameteranalysisofdynamicsusceptibilityperfusionimaging AT lochercacilia characterizationofenhancingmslesionsbydynamictextureparameteranalysisofdynamicsusceptibilityperfusionimaging AT heldnermirjamr characterizationofenhancingmslesionsbydynamictextureparameteranalysisofdynamicsusceptibilityperfusionimaging AT weisstannerchristian characterizationofenhancingmslesionsbydynamictextureparameteranalysisofdynamicsusceptibilityperfusionimaging AT abelaeugenio characterizationofenhancingmslesionsbydynamictextureparameteranalysisofdynamicsusceptibilityperfusionimaging AT kellnerweldonfrauke characterizationofenhancingmslesionsbydynamictextureparameteranalysisofdynamicsusceptibilityperfusionimaging AT zbindenmartin characterizationofenhancingmslesionsbydynamictextureparameteranalysisofdynamicsusceptibilityperfusionimaging AT kammchristianp characterizationofenhancingmslesionsbydynamictextureparameteranalysisofdynamicsusceptibilityperfusionimaging AT wiestroland characterizationofenhancingmslesionsbydynamictextureparameteranalysisofdynamicsusceptibilityperfusionimaging |