Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction.
IEEE Trans Biomed Eng
; 71(7): 2253-2264, 2024 Jul.
Article
en En
| MEDLINE
| ID: mdl-38376982
ABSTRACT
OBJECTIVE:
To leverage machine learning (ML) for fast selection of optimal regularization parameter in constrained image reconstruction.METHODS:
Constrained image reconstruction is often formulated as a regularization problem and selecting a good regularization parameter value is an essential step. We solved this problem using an ML-based approach by leveraging the finding that for a specific constrained reconstruction problem defined for a fixed class of image functions, the optimal regularization parameter value is weakly subject-dependent and the dependence can be captured using few experimental data. The proposed method has four keysteps:
a) solution of a given constrained reconstruction problem for a few (say, 3) pre-selected regularization parameter values, b) extraction of multiple approximated quality metrics from the initial reconstructions, c) predicting the true quality metrics values from the approximated values using pre-trained neural networks, and d) determination of the optimal regularization parameter by fusing the predicted quality metrics.RESULTS:
The effectiveness of the proposed method was demonstrated in two constrained reconstruction problems. Compared with L-curve-based method, the proposed method determined the regularization parameters much faster and produced substantially improved reconstructions. Our method also outperformed state-of-the-art learning-based methods when trained with limited experimental data.CONCLUSION:
This paper demonstrates the feasibility and improved reconstruction quality by using machine learning to determine the regularization parameter in constrained reconstruction.SIGNIFICANCE:
The proposed method substantially reduces the computational burden of the traditional methods (e.g., L-curve) or relaxes the requirement of large training data by modern learning-based methods, thus enhancing the practical utility of constrained reconstruction.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Procesamiento de Imagen Asistido por Computador
/
Aprendizaje Automático
Límite:
Humans
Idioma:
En
Revista:
IEEE Trans Biomed Eng
/
IEEE trans. biomed. eng
/
IEEE transactions on biomedical engineering
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos