Bias-reduced neural networks for parameter estimation in quantitative MRI.
Magn Reson Med
; 92(4): 1638-1648, 2024 Oct.
Article
en En
| MEDLINE
| ID: mdl-38703042
ABSTRACT
PURPOSE:
To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound. THEORY ANDMETHODS:
We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications.RESULTS:
In simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cramér-Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as nonlinear least-squares fitting, while state-of-the-art NNs show larger deviations.CONCLUSION:
The proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Simulación por Computador
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Procesamiento de Imagen Asistido por Computador
/
Encéfalo
/
Imagen por Resonancia Magnética
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Redes Neurales de la Computación
Límite:
Humans
Idioma:
En
Revista:
Magn Reson Med
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos