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Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization
PURPOSE: We have developed a multi-modal imaging approach for SIRT, combining (99m)Tc-MAA SPECT/CT and/or (90)Y PET, (18)F-FDG PET/CT, and contrast-enhanced CBCT for voxel-based dosimetry, as a tool for treatment planning and verification. For radiation dose prediction calculations, a segmentation o...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382918/ https://www.ncbi.nlm.nih.gov/pubmed/30788640 http://dx.doi.org/10.1186/s13550-019-0485-x |
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author | Jafargholi Rangraz, Esmaeel Coudyzer, Walter Maleux, Geert Baete, Kristof Deroose, Christophe M. Nuyts, Johan |
author_facet | Jafargholi Rangraz, Esmaeel Coudyzer, Walter Maleux, Geert Baete, Kristof Deroose, Christophe M. Nuyts, Johan |
author_sort | Jafargholi Rangraz, Esmaeel |
collection | PubMed |
description | PURPOSE: We have developed a multi-modal imaging approach for SIRT, combining (99m)Tc-MAA SPECT/CT and/or (90)Y PET, (18)F-FDG PET/CT, and contrast-enhanced CBCT for voxel-based dosimetry, as a tool for treatment planning and verification. For radiation dose prediction calculations, a segmentation of the total liver volume and of the liver perfusion territories is required. METHOD: In this paper, we proposed a procedure for multi-modal image analysis to assist SIRT treatment planning. The pre-treatment (18)F-FDG PET/CT, (99m)Tc-MAA SPECT/CT, and contrast-enhanced CBCT images were registered to a common space using an initial rigid, followed by a deformable registration. The registration was scored by an expert using Likert scores. The total liver was segmented semi-automatically based on the PET/CT and SPECT/CT images, and the liver perfusion territories were determined based on the CBCT images. The segmentations of the liver and liver lobes were compared to the manual segmentations by an expert on a CT image. RESULT: Our methodology showed that multi-modal image analysis can be used for determination of the liver and perfusion territories using CBCT in SIRT using all pre-treatment studies. The results for image registration showed acceptable alignment with limited impact on dosimetry. The image registration performs well according to the expert reviewer (scored as perfect or with little misalignment in 94% of the cases). The semi-automatic liver segmentation agreed well with manual liver segmentation (dice coefficient of 0.92 and an average Hausdorff distance of 3.04 mm). The results showed disagreement between lobe segmentation using CBCT images compared to lobe segmentation based on CT images (average Hausdorff distance of 14.18 mm), with a high impact on the dosimetry (differences up to 9 Gy for right and 21 Gy for the left liver lobe). CONCLUSION: This methodology can be used for pre-treatment dosimetry and for SIRT planning including the determination of the activity that should be administered to achieve the therapeutical goal. The inclusion of perfusion CBCT enables perfusion-based definition of the liver lobes, which was shown to be markedly different from the anatomical definition in some of the patients. |
format | Online Article Text |
id | pubmed-6382918 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-63829182019-03-12 Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization Jafargholi Rangraz, Esmaeel Coudyzer, Walter Maleux, Geert Baete, Kristof Deroose, Christophe M. Nuyts, Johan EJNMMI Res Original Research PURPOSE: We have developed a multi-modal imaging approach for SIRT, combining (99m)Tc-MAA SPECT/CT and/or (90)Y PET, (18)F-FDG PET/CT, and contrast-enhanced CBCT for voxel-based dosimetry, as a tool for treatment planning and verification. For radiation dose prediction calculations, a segmentation of the total liver volume and of the liver perfusion territories is required. METHOD: In this paper, we proposed a procedure for multi-modal image analysis to assist SIRT treatment planning. The pre-treatment (18)F-FDG PET/CT, (99m)Tc-MAA SPECT/CT, and contrast-enhanced CBCT images were registered to a common space using an initial rigid, followed by a deformable registration. The registration was scored by an expert using Likert scores. The total liver was segmented semi-automatically based on the PET/CT and SPECT/CT images, and the liver perfusion territories were determined based on the CBCT images. The segmentations of the liver and liver lobes were compared to the manual segmentations by an expert on a CT image. RESULT: Our methodology showed that multi-modal image analysis can be used for determination of the liver and perfusion territories using CBCT in SIRT using all pre-treatment studies. The results for image registration showed acceptable alignment with limited impact on dosimetry. The image registration performs well according to the expert reviewer (scored as perfect or with little misalignment in 94% of the cases). The semi-automatic liver segmentation agreed well with manual liver segmentation (dice coefficient of 0.92 and an average Hausdorff distance of 3.04 mm). The results showed disagreement between lobe segmentation using CBCT images compared to lobe segmentation based on CT images (average Hausdorff distance of 14.18 mm), with a high impact on the dosimetry (differences up to 9 Gy for right and 21 Gy for the left liver lobe). CONCLUSION: This methodology can be used for pre-treatment dosimetry and for SIRT planning including the determination of the activity that should be administered to achieve the therapeutical goal. The inclusion of perfusion CBCT enables perfusion-based definition of the liver lobes, which was shown to be markedly different from the anatomical definition in some of the patients. Springer Berlin Heidelberg 2019-02-20 /pmc/articles/PMC6382918/ /pubmed/30788640 http://dx.doi.org/10.1186/s13550-019-0485-x Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Research Jafargholi Rangraz, Esmaeel Coudyzer, Walter Maleux, Geert Baete, Kristof Deroose, Christophe M. Nuyts, Johan Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization |
title | Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization |
title_full | Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization |
title_fullStr | Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization |
title_full_unstemmed | Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization |
title_short | Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization |
title_sort | multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382918/ https://www.ncbi.nlm.nih.gov/pubmed/30788640 http://dx.doi.org/10.1186/s13550-019-0485-x |
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