Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction.
Neuroimage
; 211: 116579, 2020 05 01.
Article
en En
| MEDLINE
| ID: mdl-31981779
ABSTRACT
Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand engineered priors. However, supervised DL-based methods may achieve poor performance when the test data deviates from the training data, for example, when it has pathologies not encountered in the training data. Furthermore, DL-based image reconstructions do not always incorporate the underlying forward physical model, which may improve performance. Therefore, in this work we introduce a novel approach, called fidelity imposed network edit (FINE), which modifies the weights of a pre-trained reconstruction network for each case in the testing dataset. This is achieved by minimizing an unsupervised fidelity loss function that is based on the forward physical model. FINE is applied to two important inverse problems in neuroimaging quantitative susceptibility mapping (QSM) and under-sampled image reconstruction in MRI. Our experiments demonstrate that FINE can improve reconstruction accuracy.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Encéfalo
/
Imagen por Resonancia Magnética
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Hemorragia Cerebral
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Neuroimagen
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Aprendizaje Profundo
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Esclerosis Múltiple
Tipo de estudio:
Prognostic_studies
Límite:
Adult
/
Humans
Idioma:
En
Revista:
Neuroimage
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos