报告题目 (Title)：Generalizable and interpretable MRI reconstruction with high data heterogeneity（具有高数据异质性的泛化和可解释性MRI重建）
报告人 (Speaker)：陈韵梅（美国佛罗里达大学终身教授）
报告时间 (Time)：2024年11月3日(周日) 10：00-12：00
报告地点 (Place)：校本部 F楼四楼数学系讨论室
邀请人(Inviter)：彭亚新教授
报告摘要：Deep learning methods have demonstrated promising performance in a variety of image reconstruction problems. However, task specific and extremely data demanding are still a major challenging in practical applications. In this work we introduce a generalizable MRI reconstruction method with diverse dataset to tackle those problems. Our approach proposes a variational model, in which the learnable regularization function is parameterized by two sets of parameters: a task-invariant set for common feature encoding and a task-specific part to account for the variations in the heterogeneous data. Then, we generate a neural network, whose architecture follows exactly a convergent learned optimization algorithm for solving the nonconvex and nonsmooth variational model. The network is trained by a bilevel optimization algorithm to prevent overfitting and improve generalizability. A series of experimental results on heterogeneous MRI data sets indicate that the proposed method generalizes well to the reconstruction problems whose undersampling patterns and trajectories are not present during training.