An MRI framework for respiratory motion modelling validation.
J Med Imaging Radiat Oncol
; 65(3): 337-344, 2021 Jun.
Article
em En
| MEDLINE
| ID: mdl-33773081
ABSTRACT
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 mm3 ), 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.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Imageamento por Ressonância Magnética
/
Radioterapia Guiada por Imagem
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article