哈尔滨商业大学学报（自然科学版）2025，Vol.41Issue(1)：59-66,8.
LKA-ADMM-CSNet在MRI高精度重建中的研究
Research on LKA-ADMM-CSNet for high-precision MRI reconstruction
摘要
Abstract
The high-precision reconstruction research of MRI images can provide more accurate diagnoses for patients,enabling timely detection and targeted treatment of diseases,thereby offering reliable support for clinical decision-making.In response to this impact,an improved compressed sensing algorithm was proposed,known as the LKA-ADMM-CSNet algorithm.This algorithm combines traditional model-based compressed sensing(CS)methods with data-driven deep learning to reconstruct measured images from sparse samples.Experimental comparisons showed that for fast CS complex-valued MRI imaging,compared to traditional methods and other deep learning approaches,the reconstructed precision of the proposed LKA-ADMM-CSNet model was significantly improved.The best reconstruction precision increased by about 30%,while the worst improved by approximately 6%,with an average improvement of around 16% .This demonstrated that the new model performed better in MRI imaging applications.关键词
压缩感知/深度学习/MR成像/ADMM/LKA/LKA-ADMM-CSNetKey words
compressed sensing/deep learning/MR Imaging/ADMM/LKA/LKA-ADMM-CSNet分类
信息技术与安全科学引用本文复制引用
王德成,于瓅..LKA-ADMM-CSNet在MRI高精度重建中的研究[J].哈尔滨商业大学学报（自然科学版）,2025,41(1):59-66,8.基金项目
2021安徽省重点研究与开发计划项目(No.202104d07020010) （No.202104d07020010）
