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Impact of PET - CT motion correction in minimizing the gross tumor volume in non-small cell lung cancer

OBJECTIVE(S): To investigate the impact of respiratory motion on localization, and quantification of lung lesions for the Gross Tumor Volume utilizing a fully automated Auto3Dreg program and dynamic NURBS-based cardiac-torso digitized phantom (NCAT). METHODS: Respiratory motion may result in more th...

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Autores principales: Masoomi, Michael A, McLean, Anne H, Bouchareb, Yassine, Ryder, Will, Robinson, Andy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Asia Oceania Journal of Nuclear Medicine & Biology 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927049/
https://www.ncbi.nlm.nih.gov/pubmed/27408848
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author Masoomi, Michael A
McLean, Anne H
Bouchareb, Yassine
Ryder, Will
Robinson, Andy
author_facet Masoomi, Michael A
McLean, Anne H
Bouchareb, Yassine
Ryder, Will
Robinson, Andy
author_sort Masoomi, Michael A
collection PubMed
description OBJECTIVE(S): To investigate the impact of respiratory motion on localization, and quantification of lung lesions for the Gross Tumor Volume utilizing a fully automated Auto3Dreg program and dynamic NURBS-based cardiac-torso digitized phantom (NCAT). METHODS: Respiratory motion may result in more than 30% underestimation of the SUV values of lung, liver and kidney tumor lesions. The motion correction technique adopted in this study was an image-based motion correction approach using, a voxel-intensity-based and a multi-resolution multi-optimization (MRMO) algorithm. The NCAT phantom was used to generate CT attenuation maps and activity distribution volumes for the lung regions. All the generated frames were co-registered to a reference frame using a time efficient scheme. Quantitative assessment including Region of Interest (ROI), image fidelity and image correlation techniques, as well as semi-quantitative line profile analysis and qualitatively overlaying non-motion and motion corrected image frames were performed. RESULTS: The largest motion was observed in the Z-direction. The greatest translation was for the frame 3, end inspiration, and the smallest for the frame 5 which was closet frame to the reference frame at 67% expiration. Visual assessment of the lesion sizes, 20-60mm at 3 different locations, apex, mid and base of lung showed noticeable improvement for all the foci and their locations. The maximum improvements for the image fidelity were from 0.395 to 0.930 within the lesion volume of interest. The greatest improvement in activity concentration underestimation was 7.7% below the true activity for the 20 mm lesion in comparison to 34.4% below, prior to correction. The discrepancies in activity underestimation were reduced with increasing the lesion sizes. Overlaying activity distribution on the attenuation map showed improved localization of the PET metabolic information to the anatomical CT images. CONCLUSION: The respiratory motion correction for the lung lesions has led to an improvement in the lesion size, localization and activity quantification with a potential application in reducing the size of the PET GTV for radiotherapy treatment planning applications and hence improving the accuracy of the regime in treatment of lung cancer.
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spelling pubmed-49270492016-07-12 Impact of PET - CT motion correction in minimizing the gross tumor volume in non-small cell lung cancer Masoomi, Michael A McLean, Anne H Bouchareb, Yassine Ryder, Will Robinson, Andy Asia Ocean J Nucl Med Biol Original Article OBJECTIVE(S): To investigate the impact of respiratory motion on localization, and quantification of lung lesions for the Gross Tumor Volume utilizing a fully automated Auto3Dreg program and dynamic NURBS-based cardiac-torso digitized phantom (NCAT). METHODS: Respiratory motion may result in more than 30% underestimation of the SUV values of lung, liver and kidney tumor lesions. The motion correction technique adopted in this study was an image-based motion correction approach using, a voxel-intensity-based and a multi-resolution multi-optimization (MRMO) algorithm. The NCAT phantom was used to generate CT attenuation maps and activity distribution volumes for the lung regions. All the generated frames were co-registered to a reference frame using a time efficient scheme. Quantitative assessment including Region of Interest (ROI), image fidelity and image correlation techniques, as well as semi-quantitative line profile analysis and qualitatively overlaying non-motion and motion corrected image frames were performed. RESULTS: The largest motion was observed in the Z-direction. The greatest translation was for the frame 3, end inspiration, and the smallest for the frame 5 which was closet frame to the reference frame at 67% expiration. Visual assessment of the lesion sizes, 20-60mm at 3 different locations, apex, mid and base of lung showed noticeable improvement for all the foci and their locations. The maximum improvements for the image fidelity were from 0.395 to 0.930 within the lesion volume of interest. The greatest improvement in activity concentration underestimation was 7.7% below the true activity for the 20 mm lesion in comparison to 34.4% below, prior to correction. The discrepancies in activity underestimation were reduced with increasing the lesion sizes. Overlaying activity distribution on the attenuation map showed improved localization of the PET metabolic information to the anatomical CT images. CONCLUSION: The respiratory motion correction for the lung lesions has led to an improvement in the lesion size, localization and activity quantification with a potential application in reducing the size of the PET GTV for radiotherapy treatment planning applications and hence improving the accuracy of the regime in treatment of lung cancer. Asia Oceania Journal of Nuclear Medicine & Biology 2013 /pmc/articles/PMC4927049/ /pubmed/27408848 Text en Copyright: © mums.ac.ir http://creativecommons.org/licenses/by/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Masoomi, Michael A
McLean, Anne H
Bouchareb, Yassine
Ryder, Will
Robinson, Andy
Impact of PET - CT motion correction in minimizing the gross tumor volume in non-small cell lung cancer
title Impact of PET - CT motion correction in minimizing the gross tumor volume in non-small cell lung cancer
title_full Impact of PET - CT motion correction in minimizing the gross tumor volume in non-small cell lung cancer
title_fullStr Impact of PET - CT motion correction in minimizing the gross tumor volume in non-small cell lung cancer
title_full_unstemmed Impact of PET - CT motion correction in minimizing the gross tumor volume in non-small cell lung cancer
title_short Impact of PET - CT motion correction in minimizing the gross tumor volume in non-small cell lung cancer
title_sort impact of pet - ct motion correction in minimizing the gross tumor volume in non-small cell lung cancer
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927049/
https://www.ncbi.nlm.nih.gov/pubmed/27408848
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