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Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction
Journal article   Open access   Peer reviewed

Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction

Yue Guan, Yudu Li, Ziwen Ke, Xi Peng, Ruihao Liu, Yao Li, Yiping P Du and Zhi-Pei Liang
IEEE transactions on biomedical engineering, Vol.71(7), pp.2253-2264
02/20/2024
DOI: 10.1109/TBME.2024.3367762
PMID: 38376982
url
https://doi.org/10.1109/TBME.2024.3367762View
Published (Version of record) Open Access

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 key steps: 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.

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