Cargando…

Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments

Registration is critical for image‐based treatment planning and image‐guided treatment delivery. Although automatic registration is available, manual, visual‐based image fusion using three orthogonal planar views (3P) is always employed clinically to verify and adjust an automatic registration resul...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Guang, Xie, Huchen, Ning, Holly, Citrin, Deborah, Capala, Jacek, Maass‐Moreno, Roberto, Guion, Peter, Arora, Barbara, Coleman, Norman, Camphausen, Kevin, Miller, Robert W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5722361/
https://www.ncbi.nlm.nih.gov/pubmed/19020479
http://dx.doi.org/10.1120/jacmp.v9i4.2781
_version_ 1783284995601727488
author Li, Guang
Xie, Huchen
Ning, Holly
Citrin, Deborah
Capala, Jacek
Maass‐Moreno, Roberto
Guion, Peter
Arora, Barbara
Coleman, Norman
Camphausen, Kevin
Miller, Robert W.
author_facet Li, Guang
Xie, Huchen
Ning, Holly
Citrin, Deborah
Capala, Jacek
Maass‐Moreno, Roberto
Guion, Peter
Arora, Barbara
Coleman, Norman
Camphausen, Kevin
Miller, Robert W.
author_sort Li, Guang
collection PubMed
description Registration is critical for image‐based treatment planning and image‐guided treatment delivery. Although automatic registration is available, manual, visual‐based image fusion using three orthogonal planar views (3P) is always employed clinically to verify and adjust an automatic registration result. However, the 3P fusion can be time consuming, observer dependent, as well as prone to errors, owing to the incomplete 3‐dimensional (3D) volumetric image representations. It is also limited to single‐pixel precision (the screen resolution). The 3D volumetric image registration (3DVIR) technique was developed to overcome these shortcomings. This technique introduces a 4th dimension in the registration criteria beyond the image volume, offering both visual and quantitative correlation of corresponding anatomic landmarks within the two registration images, facilitating a volumetric image alignment, and minimizing potential registration errors. The 3DVIR combines image classification in real‐time to select and visualize a reliable anatomic landmark, rather than using all voxels for alignment. To determine the detection limit of the visual and quantitative 3DVIR criteria, slightly misaligned images were simulated and presented to eight clinical personnel for interpretation. Both of the criteria produce a detection limit of 0.1 mm and 0.1°. To determine the accuracy of the 3DVIR method, three imaging modalities (CT, MR and PET/CT) were used to acquire multiple phantom images with known spatial shifts. Lateral shifts were applied to these phantoms with displacement intervals of [Formula: see text]. The accuracy of the 3DVIR technique was determined by comparing the image shifts determined through registration to the physical shifts made experimentally. The registration accuracy, together with precision, was found to be: [Formula: see text] for CT/CT images, [Formula: see text] for MR/MR images, and [Formula: see text] for PET/CT images. This accuracy is consistent with the detection limit, suggesting an absence of detectable systematic error. This 3DVIR technique provides a superior alternative to the 3P fusion method for clinical applications. PACS numbers: 87.57.nj, 87.57.nm, 87.57.‐N, 87.57.‐s
format Online
Article
Text
id pubmed-5722361
institution National Center for Biotechnology Information
language English
publishDate 2008
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-57223612018-04-02 Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments Li, Guang Xie, Huchen Ning, Holly Citrin, Deborah Capala, Jacek Maass‐Moreno, Roberto Guion, Peter Arora, Barbara Coleman, Norman Camphausen, Kevin Miller, Robert W. J Appl Clin Med Phys Radiation Oncology Physics Registration is critical for image‐based treatment planning and image‐guided treatment delivery. Although automatic registration is available, manual, visual‐based image fusion using three orthogonal planar views (3P) is always employed clinically to verify and adjust an automatic registration result. However, the 3P fusion can be time consuming, observer dependent, as well as prone to errors, owing to the incomplete 3‐dimensional (3D) volumetric image representations. It is also limited to single‐pixel precision (the screen resolution). The 3D volumetric image registration (3DVIR) technique was developed to overcome these shortcomings. This technique introduces a 4th dimension in the registration criteria beyond the image volume, offering both visual and quantitative correlation of corresponding anatomic landmarks within the two registration images, facilitating a volumetric image alignment, and minimizing potential registration errors. The 3DVIR combines image classification in real‐time to select and visualize a reliable anatomic landmark, rather than using all voxels for alignment. To determine the detection limit of the visual and quantitative 3DVIR criteria, slightly misaligned images were simulated and presented to eight clinical personnel for interpretation. Both of the criteria produce a detection limit of 0.1 mm and 0.1°. To determine the accuracy of the 3DVIR method, three imaging modalities (CT, MR and PET/CT) were used to acquire multiple phantom images with known spatial shifts. Lateral shifts were applied to these phantoms with displacement intervals of [Formula: see text]. The accuracy of the 3DVIR technique was determined by comparing the image shifts determined through registration to the physical shifts made experimentally. The registration accuracy, together with precision, was found to be: [Formula: see text] for CT/CT images, [Formula: see text] for MR/MR images, and [Formula: see text] for PET/CT images. This accuracy is consistent with the detection limit, suggesting an absence of detectable systematic error. This 3DVIR technique provides a superior alternative to the 3P fusion method for clinical applications. PACS numbers: 87.57.nj, 87.57.nm, 87.57.‐N, 87.57.‐s John Wiley and Sons Inc. 2008-07-09 /pmc/articles/PMC5722361/ /pubmed/19020479 http://dx.doi.org/10.1120/jacmp.v9i4.2781 Text en © 2008 The Authors. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Li, Guang
Xie, Huchen
Ning, Holly
Citrin, Deborah
Capala, Jacek
Maass‐Moreno, Roberto
Guion, Peter
Arora, Barbara
Coleman, Norman
Camphausen, Kevin
Miller, Robert W.
Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments
title Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments
title_full Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments
title_fullStr Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments
title_full_unstemmed Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments
title_short Accuracy of 3D volumetric image registration based on CT, MR and PET/CT phantom experiments
title_sort accuracy of 3d volumetric image registration based on ct, mr and pet/ct phantom experiments
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5722361/
https://www.ncbi.nlm.nih.gov/pubmed/19020479
http://dx.doi.org/10.1120/jacmp.v9i4.2781
work_keys_str_mv AT liguang accuracyof3dvolumetricimageregistrationbasedonctmrandpetctphantomexperiments
AT xiehuchen accuracyof3dvolumetricimageregistrationbasedonctmrandpetctphantomexperiments
AT ningholly accuracyof3dvolumetricimageregistrationbasedonctmrandpetctphantomexperiments
AT citrindeborah accuracyof3dvolumetricimageregistrationbasedonctmrandpetctphantomexperiments
AT capalajacek accuracyof3dvolumetricimageregistrationbasedonctmrandpetctphantomexperiments
AT maassmorenoroberto accuracyof3dvolumetricimageregistrationbasedonctmrandpetctphantomexperiments
AT guionpeter accuracyof3dvolumetricimageregistrationbasedonctmrandpetctphantomexperiments
AT arorabarbara accuracyof3dvolumetricimageregistrationbasedonctmrandpetctphantomexperiments
AT colemannorman accuracyof3dvolumetricimageregistrationbasedonctmrandpetctphantomexperiments
AT camphausenkevin accuracyof3dvolumetricimageregistrationbasedonctmrandpetctphantomexperiments
AT millerrobertw accuracyof3dvolumetricimageregistrationbasedonctmrandpetctphantomexperiments