• Adaptive regularization weight...

*Result*: Adaptive regularization weight selection for compressed sensing MRI reconstruction.

Title:
Adaptive regularization weight selection for compressed sensing MRI reconstruction.
Authors:
Lian Y; Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China., Jiang Y; Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China., Guo H; Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China. Electronic address: huaguo@tsinghua.edu.cn.
Source:
Magnetic resonance imaging [Magn Reson Imaging] 2026 Apr; Vol. 127, pp. 110579. Date of Electronic Publication: 2025 Dec 01.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Country of Publication: Netherlands NLM ID: 8214883 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-5894 (Electronic) Linking ISSN: 0730725X NLM ISO Abbreviation: Magn Reson Imaging Subsets: MEDLINE
Imprint Name(s):
Publication: <2008->: Amsterdam : Elsevier
Original Publication: New York : Pergamon, c1982-
MeSH Terms:
Magnetic Resonance Imaging*/methods , Data Compression*/methods , Image Processing, Computer-Assisted*/methods , Brain*/diagnostic imaging , Brain*/anatomy & histology, Humans ; Algorithms ; Bayes Theorem ; Artifacts ; Wavelet Analysis ; Retrospective Studies ; Reproducibility of Results ; Normal Distribution ; Prospective Studies
Contributed Indexing:
Keywords: Compressed Sensing; Image reconstruction; Threshold selection
Entry Date(s):
Date Created: 20251203 Date Completed: 20260201 Latest Revision: 20260201
Update Code:
20260202
DOI:
10.1016/j.mri.2025.110579
PMID:
41338441
Database:
MEDLINE

*Further Information*

*Purpose: Proper regularization weights are crucial for the reconstruction quality of compressed sensing (CS) MRI. This work aims to develop an automatic and adaptive regularization weights selection method for CS reconstruction METHODS: A statistical model based on Bayesian theory is designed, incorporating prior information about the Gaussian distribution of incoherent noise and the Laplacian distribution of wavelet coefficients in the wavelet transform domain. Using the variance of coefficients and noise, the adaptive regularization weight for achieving optimal reconstruction quality in each iteration step is obtained through a maximum a posteriori estimator. The adaptive regularization weights vary across different subjects, slices, iterations, and wavelet sub-bands RESULTS: The efficacy of the proposed method was demonstrated through retrospective and prospective studies. Compared to reconstruction results using optimal fixed regularization weights and sparsity-adaptive composite recovery method (SCoRe), the proposed method successfully reduces reconstruction errors and effectively recovers original signals from noise-like incoherent artifacts in the wavelet transform domain. It also saves weight selection time when searching for optimal fixed regularization weights CONCLUSION: We propose an adaptive regularization weights selection method for CS-MRI reconstruction. It provides optimal regularization weights for different subjects, slices, and iterations without requiring manual intervention.
(Copyright © 2025 Elsevier Inc. All rights reserved.)*

*Declaration of competing interest None.*