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Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy

BACKGROUND: Bronchoscopy serves as direct visualisation of the airway. Virtual bronchoscopy provides similar visual information using a non-invasive imaging procedure(s). Early and accurate image-guided diagnosis requires the possible highest performance, which might be approximated by combining ana...

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Autores principales: Opposits, Gábor, Nagy, Marianna, Barta, Zoltán, Aranyi, Csaba, Szabó, Dániel, Makai, Attila, Varga, Imre, Galuska, László, Trón, Lajos, Balkay, László, Emri, Miklós
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8313651/
https://www.ncbi.nlm.nih.gov/pubmed/34312736
http://dx.doi.org/10.1186/s13550-021-00810-w
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author Opposits, Gábor
Nagy, Marianna
Barta, Zoltán
Aranyi, Csaba
Szabó, Dániel
Makai, Attila
Varga, Imre
Galuska, László
Trón, Lajos
Balkay, László
Emri, Miklós
author_facet Opposits, Gábor
Nagy, Marianna
Barta, Zoltán
Aranyi, Csaba
Szabó, Dániel
Makai, Attila
Varga, Imre
Galuska, László
Trón, Lajos
Balkay, László
Emri, Miklós
author_sort Opposits, Gábor
collection PubMed
description BACKGROUND: Bronchoscopy serves as direct visualisation of the airway. Virtual bronchoscopy provides similar visual information using a non-invasive imaging procedure(s). Early and accurate image-guided diagnosis requires the possible highest performance, which might be approximated by combining anatomical and functional imaging. This communication describes an advanced functional virtual bronchoscopic (fVB) method based on the registration of PET images to high-resolution diagnostic CT images instead of low-dose CT images of lower resolution obtained from PET/CT scans. PET/CT and diagnostic CT data were collected from 22 oncological patients to develop a computer-aided high-precision fVB. Registration of segmented images was performed using elastix. RESULTS: For virtual bronchoscopy, we used an in-house developed segmentation method. The quality of low- and high-dose CT image registrations was characterised by expert’s scoring the spatial distance of manually paired corresponding points and by eight voxel intensity-based (dis)similarity parameters. The distribution of (dis)similarity parameter correlating best with anatomic scoring was bootstrapped, and 95% confidence intervals were calculated separately for acceptable and insufficient registrations. We showed that mutual information (MI) of the eight investigated (dis)similarity parameters displayed the closest correlation with the anatomy-based distance metrics used to characterise the quality of image registrations. The 95% confidence intervals of the bootstrapped MI distribution were [0.15, 0.22] and [0.28, 0.37] for insufficient and acceptable registrations, respectively. In case of any new patient, a calculated MI value of registered low- and high-dose CT image pair within the [0.28, 0.37] or the [0.15, 0.22] interval would suggest acceptance or rejection, respectively, serving as an aid for the radiologist. CONCLUSION: A computer-aided solution was proposed in order to reduce reliance on radiologist’s contribution for the approval of acceptable image registrations.
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spelling pubmed-83136512021-08-16 Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy Opposits, Gábor Nagy, Marianna Barta, Zoltán Aranyi, Csaba Szabó, Dániel Makai, Attila Varga, Imre Galuska, László Trón, Lajos Balkay, László Emri, Miklós EJNMMI Res Original Research BACKGROUND: Bronchoscopy serves as direct visualisation of the airway. Virtual bronchoscopy provides similar visual information using a non-invasive imaging procedure(s). Early and accurate image-guided diagnosis requires the possible highest performance, which might be approximated by combining anatomical and functional imaging. This communication describes an advanced functional virtual bronchoscopic (fVB) method based on the registration of PET images to high-resolution diagnostic CT images instead of low-dose CT images of lower resolution obtained from PET/CT scans. PET/CT and diagnostic CT data were collected from 22 oncological patients to develop a computer-aided high-precision fVB. Registration of segmented images was performed using elastix. RESULTS: For virtual bronchoscopy, we used an in-house developed segmentation method. The quality of low- and high-dose CT image registrations was characterised by expert’s scoring the spatial distance of manually paired corresponding points and by eight voxel intensity-based (dis)similarity parameters. The distribution of (dis)similarity parameter correlating best with anatomic scoring was bootstrapped, and 95% confidence intervals were calculated separately for acceptable and insufficient registrations. We showed that mutual information (MI) of the eight investigated (dis)similarity parameters displayed the closest correlation with the anatomy-based distance metrics used to characterise the quality of image registrations. The 95% confidence intervals of the bootstrapped MI distribution were [0.15, 0.22] and [0.28, 0.37] for insufficient and acceptable registrations, respectively. In case of any new patient, a calculated MI value of registered low- and high-dose CT image pair within the [0.28, 0.37] or the [0.15, 0.22] interval would suggest acceptance or rejection, respectively, serving as an aid for the radiologist. CONCLUSION: A computer-aided solution was proposed in order to reduce reliance on radiologist’s contribution for the approval of acceptable image registrations. Springer Berlin Heidelberg 2021-07-26 /pmc/articles/PMC8313651/ /pubmed/34312736 http://dx.doi.org/10.1186/s13550-021-00810-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Research
Opposits, Gábor
Nagy, Marianna
Barta, Zoltán
Aranyi, Csaba
Szabó, Dániel
Makai, Attila
Varga, Imre
Galuska, László
Trón, Lajos
Balkay, László
Emri, Miklós
Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy
title Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy
title_full Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy
title_fullStr Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy
title_full_unstemmed Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy
title_short Automated procedure assessing the accuracy of HRCT–PET registration applied in functional virtual bronchoscopy
title_sort automated procedure assessing the accuracy of hrct–pet registration applied in functional virtual bronchoscopy
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8313651/
https://www.ncbi.nlm.nih.gov/pubmed/34312736
http://dx.doi.org/10.1186/s13550-021-00810-w
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