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Practical quantification of image registration accuracy following the AAPM TG‐132 report framework
The AAPM TG 132 Report enumerates important steps for validation of the medical image registration process. While the Report outlines the general goals and criteria for the tests, specific implementation may be obscure to the wider clinical audience. We endeavored to provide a detailed step‐by‐step...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6036411/ https://www.ncbi.nlm.nih.gov/pubmed/29882231 http://dx.doi.org/10.1002/acm2.12348 |
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author | Latifi, Kujtim Caudell, Jimmy Zhang, Geoffrey Hunt, Dylan Moros, Eduardo G. Feygelman, Vladimir |
author_facet | Latifi, Kujtim Caudell, Jimmy Zhang, Geoffrey Hunt, Dylan Moros, Eduardo G. Feygelman, Vladimir |
author_sort | Latifi, Kujtim |
collection | PubMed |
description | The AAPM TG 132 Report enumerates important steps for validation of the medical image registration process. While the Report outlines the general goals and criteria for the tests, specific implementation may be obscure to the wider clinical audience. We endeavored to provide a detailed step‐by‐step description of the quantitative tests’ execution, applied as an example to a commercial software package (Mirada Medical, Oxford, UK), while striving for simplicity and utilization of readily available software. We demonstrated how the rigid registration data could be easily extracted from the DICOM registration object and used, following some simple matrix math, to quantify accuracy of rigid translations and rotations. The options for validating deformable image registration (DIR) were enumerated, and it was shown that the most practically viable ones are comparison of propagated internal landmark points on the published datasets, or of segmented contours that can be generated locally. The multimodal rigid registration in our example did not always result in the desired registration error below ½ voxel size, but was considered acceptable with the maximum errors under 1.3 mm and 1°. The DIR target registration errors in the thorax based on internal landmarks were far in excess of the Report recommendations of 2 mm average and 5 mm maximum. On the other hand, evaluation of the DIR major organs’ contours propagation demonstrated good agreement for lung and abdomen (Dice Similarity Coefficients, DSC, averaged over all cases and structures of 0.92 ± 0.05 and 0.91 ± 0.06, respectively), and fair agreement for Head and Neck (average DSC = 0.73 ± 0.14). The average for head and neck is reduced by small volume structures such as pharyngeal constrictor muscles. Even these relatively simple tests show that commercial registration algorithms cannot be automatically assumed sufficiently accurate for all applications. Formalized task‐specific accuracy quantification should be expected from the vendors. |
format | Online Article Text |
id | pubmed-6036411 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-60364112018-07-12 Practical quantification of image registration accuracy following the AAPM TG‐132 report framework Latifi, Kujtim Caudell, Jimmy Zhang, Geoffrey Hunt, Dylan Moros, Eduardo G. Feygelman, Vladimir J Appl Clin Med Phys Radiation Oncology Physics The AAPM TG 132 Report enumerates important steps for validation of the medical image registration process. While the Report outlines the general goals and criteria for the tests, specific implementation may be obscure to the wider clinical audience. We endeavored to provide a detailed step‐by‐step description of the quantitative tests’ execution, applied as an example to a commercial software package (Mirada Medical, Oxford, UK), while striving for simplicity and utilization of readily available software. We demonstrated how the rigid registration data could be easily extracted from the DICOM registration object and used, following some simple matrix math, to quantify accuracy of rigid translations and rotations. The options for validating deformable image registration (DIR) were enumerated, and it was shown that the most practically viable ones are comparison of propagated internal landmark points on the published datasets, or of segmented contours that can be generated locally. The multimodal rigid registration in our example did not always result in the desired registration error below ½ voxel size, but was considered acceptable with the maximum errors under 1.3 mm and 1°. The DIR target registration errors in the thorax based on internal landmarks were far in excess of the Report recommendations of 2 mm average and 5 mm maximum. On the other hand, evaluation of the DIR major organs’ contours propagation demonstrated good agreement for lung and abdomen (Dice Similarity Coefficients, DSC, averaged over all cases and structures of 0.92 ± 0.05 and 0.91 ± 0.06, respectively), and fair agreement for Head and Neck (average DSC = 0.73 ± 0.14). The average for head and neck is reduced by small volume structures such as pharyngeal constrictor muscles. Even these relatively simple tests show that commercial registration algorithms cannot be automatically assumed sufficiently accurate for all applications. Formalized task‐specific accuracy quantification should be expected from the vendors. John Wiley and Sons Inc. 2018-06-07 /pmc/articles/PMC6036411/ /pubmed/29882231 http://dx.doi.org/10.1002/acm2.12348 Text en © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Radiation Oncology Physics Latifi, Kujtim Caudell, Jimmy Zhang, Geoffrey Hunt, Dylan Moros, Eduardo G. Feygelman, Vladimir Practical quantification of image registration accuracy following the AAPM TG‐132 report framework |
title | Practical quantification of image registration accuracy following the AAPM TG‐132 report framework |
title_full | Practical quantification of image registration accuracy following the AAPM TG‐132 report framework |
title_fullStr | Practical quantification of image registration accuracy following the AAPM TG‐132 report framework |
title_full_unstemmed | Practical quantification of image registration accuracy following the AAPM TG‐132 report framework |
title_short | Practical quantification of image registration accuracy following the AAPM TG‐132 report framework |
title_sort | practical quantification of image registration accuracy following the aapm tg‐132 report framework |
topic | Radiation Oncology Physics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6036411/ https://www.ncbi.nlm.nih.gov/pubmed/29882231 http://dx.doi.org/10.1002/acm2.12348 |
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