Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training.
Magn Reson Med
; 85(4): 2263-2277, 2021 04.
Article
en En
| MEDLINE
| ID: mdl-33107127
ABSTRACT
PURPOSE:
To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training.METHODS:
The current T2∗ -IDEAL algorithm for solving water/fat separation is dependent on initialization. Recently, DNN has been proposed to solve water/fat separation without the need for suitable initialization. However, this approach requires supervised training of DNN using the reference water/fat separation images. Here we propose 2 novel DNN water/fat separationmethods:
1) unsupervised training of DNN (UTD) using the physical forward problem as the cost function during training, and 2) no training of DNN using physical cost and backpropagation to directly reconstruct a single dataset. The supervised training of DNN, unsupervised training of DNN, and no training of DNN methods were compared with the reference T2∗ -IDEAL.RESULTS:
All DNN methods generated consistent water/fat separation results that agreed well with T2∗ -IDEAL under proper initialization.CONCLUSION:
The water/fat separation problem can be solved using unsupervised deep neural networks.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
Idioma:
En
Revista:
Magn Reson Med
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos