Multimodality image registration in the head-and-neck using a deep learning-derived synthetic CT as a bridge.
Med Phys
; 47(3): 1094-1104, 2020 Mar.
Article
em En
| MEDLINE
| ID: mdl-31853975
ABSTRACT
PURPOSE:
To develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis. METHODS AND MATERIALS Twenty-five head-and-neck patients received magnetic resonance (MR) and computed tomography (CT) (CTaligned ) scans on the same day with the same immobilization. Fivefold cross validation was used with all of the MR-CT pairs to train a neural network to generate synthetic CTs from MR images. Twenty-four of 25 patients also had a separate CT without immobilization (CTnon-aligned ) and were used for testing. CTnon-aligned 's were deformed to the synthetic CT, and compared to CTnon-aligned registered to MR. The same registrations were performed from MR to CTnon-aligned and from synthetic CT to CTnon-aligned . All registrations used B-splines for modeling the deformation, and mutual information for the objective. Results were evaluated using the 95% Hausdorff distance among spinal cord contours, landmark error, inverse consistency, and Jacobian determinant of the estimated deformation fields.RESULTS:
When large initial rigid misalignment is present, registering CT to MRI-derived synthetic CT aligns the cord better than a direct registration. The average landmark error decreased from 9.8 ± 3.1 mm in MRâCTnon-aligned to 6.0 ± 2.1 mm in CTsynth âCTnon-aligned deformable registrations. In the CT to MR direction, the landmark error decreased from 10.0 ± 4.3 mm in CTnon-aligned âMR deformable registrations to 6.6 ± 2.0 mm in CTnon-aligned âCTsynth deformable registrations. The Jacobian determinant had an average value of 0.98. The proposed method also demonstrated improved inverse consistency over the direct method.CONCLUSIONS:
We showed that using a deep learning-derived synthetic CT in lieu of an MR for MRâCT and CTâMR deformable registration offers superior results to direct multimodal registration.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Tomografia Computadorizada por Raios X
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Aprendizado Profundo
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Neoplasias de Cabeça e Pescoço
Limite:
Humans
Idioma:
En
Revista:
Med Phys
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos