Cargando…
An MRI framework for respiratory motion modelling validation
INTRODUCTION: Respiratory motion models establish a correspondence between respiratory‐correlated (RC) 4‐dimensional (4D) imaging and respiratory surrogates, to estimate time‐resolved (TR) 3D breathing motion. To evaluate the performance of motion models on real patient data, a validation framework...
Autores principales: | , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8251859/ https://www.ncbi.nlm.nih.gov/pubmed/33773081 http://dx.doi.org/10.1111/1754-9485.13175 |
_version_ | 1783717177909575680 |
---|---|
author | Meschini, Giorgia Paganelli, Chiara Vai, Alessandro Fontana, Giulia Molinelli, Silvia Pella, Andrea Vitolo, Viviana Barcellini, Amelia Orlandi, Ester Ciocca, Mario Riboldi, Marco Baroni, Guido |
author_facet | Meschini, Giorgia Paganelli, Chiara Vai, Alessandro Fontana, Giulia Molinelli, Silvia Pella, Andrea Vitolo, Viviana Barcellini, Amelia Orlandi, Ester Ciocca, Mario Riboldi, Marco Baroni, Guido |
author_sort | Meschini, Giorgia |
collection | PubMed |
description | INTRODUCTION: Respiratory motion models establish a correspondence between respiratory‐correlated (RC) 4‐dimensional (4D) imaging and respiratory surrogates, to estimate time‐resolved (TR) 3D breathing motion. To evaluate the performance of motion models on real patient data, a validation framework based on magnetic resonance imaging (MRI) is proposed, entailing the use of RC 4DMRI to build the model, and on both (i) TR 2D cine‐MRI and (ii) additional 4DMRI data for testing intra‐/inter‐fraction breathing motion variability. METHODS: Repeated MRI data were acquired in 7 patients with abdominal lesions. The considered model relied on deformable image registration (DIR) for building the model and compensating for inter‐fraction baseline variations. Both 2D and 3D validation were performed, by comparing model estimations with the ground truth 2D cine‐MRI and 4DMRI respiratory phases, respectively. RESULTS: The median DIR error was comparable to the voxel size (1.33 × 1.33 × 5 mm(3)), with higher values in the presence of large inter‐fraction motion (median value: 2.97 mm). In the 2D validation, the median estimation error on anatomical landmarks’ position resulted below 4 mm in every scenario, whereas in the 3D validation it was 1.33 mm and 4.21 mm when testing intra‐ and inter‐fraction motion, respectively. The range of motion described in the cine‐MRI was comparable to the motion of the building 4DMRI, being always above the estimation error. Overall, the model performance was dependent on DIR error, presenting reduced accuracy when inter‐fraction baseline variations occurred. CONCLUSIONS: Results suggest the potential of the proposed framework in evaluating global motion models for organ motion management in MRI‐guided radiotherapy. |
format | Online Article Text |
id | pubmed-8251859 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82518592021-07-07 An MRI framework for respiratory motion modelling validation Meschini, Giorgia Paganelli, Chiara Vai, Alessandro Fontana, Giulia Molinelli, Silvia Pella, Andrea Vitolo, Viviana Barcellini, Amelia Orlandi, Ester Ciocca, Mario Riboldi, Marco Baroni, Guido J Med Imaging Radiat Oncol MEDICAL IMAGING—RADIATION ONCOLOGY INTRODUCTION: Respiratory motion models establish a correspondence between respiratory‐correlated (RC) 4‐dimensional (4D) imaging and respiratory surrogates, to estimate time‐resolved (TR) 3D breathing motion. To evaluate the performance of motion models on real patient data, a validation framework based on magnetic resonance imaging (MRI) is proposed, entailing the use of RC 4DMRI to build the model, and on both (i) TR 2D cine‐MRI and (ii) additional 4DMRI data for testing intra‐/inter‐fraction breathing motion variability. METHODS: Repeated MRI data were acquired in 7 patients with abdominal lesions. The considered model relied on deformable image registration (DIR) for building the model and compensating for inter‐fraction baseline variations. Both 2D and 3D validation were performed, by comparing model estimations with the ground truth 2D cine‐MRI and 4DMRI respiratory phases, respectively. RESULTS: The median DIR error was comparable to the voxel size (1.33 × 1.33 × 5 mm(3)), with higher values in the presence of large inter‐fraction motion (median value: 2.97 mm). In the 2D validation, the median estimation error on anatomical landmarks’ position resulted below 4 mm in every scenario, whereas in the 3D validation it was 1.33 mm and 4.21 mm when testing intra‐ and inter‐fraction motion, respectively. The range of motion described in the cine‐MRI was comparable to the motion of the building 4DMRI, being always above the estimation error. Overall, the model performance was dependent on DIR error, presenting reduced accuracy when inter‐fraction baseline variations occurred. CONCLUSIONS: Results suggest the potential of the proposed framework in evaluating global motion models for organ motion management in MRI‐guided radiotherapy. John Wiley and Sons Inc. 2021-03-26 2021-06 /pmc/articles/PMC8251859/ /pubmed/33773081 http://dx.doi.org/10.1111/1754-9485.13175 Text en © 2021 The Authors. Journal of Medical Imaging and Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of Royal Australian and New Zealand College of Radiologists. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | MEDICAL IMAGING—RADIATION ONCOLOGY Meschini, Giorgia Paganelli, Chiara Vai, Alessandro Fontana, Giulia Molinelli, Silvia Pella, Andrea Vitolo, Viviana Barcellini, Amelia Orlandi, Ester Ciocca, Mario Riboldi, Marco Baroni, Guido An MRI framework for respiratory motion modelling validation |
title | An MRI framework for respiratory motion modelling validation |
title_full | An MRI framework for respiratory motion modelling validation |
title_fullStr | An MRI framework for respiratory motion modelling validation |
title_full_unstemmed | An MRI framework for respiratory motion modelling validation |
title_short | An MRI framework for respiratory motion modelling validation |
title_sort | mri framework for respiratory motion modelling validation |
topic | MEDICAL IMAGING—RADIATION ONCOLOGY |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8251859/ https://www.ncbi.nlm.nih.gov/pubmed/33773081 http://dx.doi.org/10.1111/1754-9485.13175 |
work_keys_str_mv | AT meschinigiorgia anmriframeworkforrespiratorymotionmodellingvalidation AT paganellichiara anmriframeworkforrespiratorymotionmodellingvalidation AT vaialessandro anmriframeworkforrespiratorymotionmodellingvalidation AT fontanagiulia anmriframeworkforrespiratorymotionmodellingvalidation AT molinellisilvia anmriframeworkforrespiratorymotionmodellingvalidation AT pellaandrea anmriframeworkforrespiratorymotionmodellingvalidation AT vitoloviviana anmriframeworkforrespiratorymotionmodellingvalidation AT barcelliniamelia anmriframeworkforrespiratorymotionmodellingvalidation AT orlandiester anmriframeworkforrespiratorymotionmodellingvalidation AT cioccamario anmriframeworkforrespiratorymotionmodellingvalidation AT riboldimarco anmriframeworkforrespiratorymotionmodellingvalidation AT baroniguido anmriframeworkforrespiratorymotionmodellingvalidation AT meschinigiorgia mriframeworkforrespiratorymotionmodellingvalidation AT paganellichiara mriframeworkforrespiratorymotionmodellingvalidation AT vaialessandro mriframeworkforrespiratorymotionmodellingvalidation AT fontanagiulia mriframeworkforrespiratorymotionmodellingvalidation AT molinellisilvia mriframeworkforrespiratorymotionmodellingvalidation AT pellaandrea mriframeworkforrespiratorymotionmodellingvalidation AT vitoloviviana mriframeworkforrespiratorymotionmodellingvalidation AT barcelliniamelia mriframeworkforrespiratorymotionmodellingvalidation AT orlandiester mriframeworkforrespiratorymotionmodellingvalidation AT cioccamario mriframeworkforrespiratorymotionmodellingvalidation AT riboldimarco mriframeworkforrespiratorymotionmodellingvalidation AT baroniguido mriframeworkforrespiratorymotionmodellingvalidation |