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

• Published in: IEEE transactions on biomedical engineering Vol. 71; no. 7; pp. 2253 - 2264
• Main Authors: Guan, Yue, Li, Yudu, Ke, Ziwen, Peng, Xi, Liu, Ruihao, Li, Yao, Du, Yiping P., Liang, Zhi-Pei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation; Biomedical imaging; constrained image reconstruction; Experimental data; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image quality; Image reconstruction; Learning algorithms; Learning systems; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Measurement; Methods; Neural networks; Neural Networks, Computer; optimization; Parameters; Phantoms, Imaging; Regularization; Regularization parameter selection; Training
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3367762

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.