題  目：Physics-Informed Deep Learning Methods for Accelerated MRI

内容簡介：Accelerated MRI can be mathematically modeled as an inverse problem, and regularization is a crucial tool to achieve stable and accurate solutions. However, the acceleration rates of conventional MRI methods using traditional regularization have approached their limits. In recent years, deep learning methods have garnered increasing attention and are widely regarded as a breakthrough for further accelerating imaging. Nevertheless, existing deep learning imaging methods mostly lack the necessary interpretability, exposing imaging to the risk of instability. Fortunately, in contrast to general natural image processing problems, MRI is driven by MR physical principles. Therefore, we propose a physics-driven learnable regularization approach, wherein the design of inference algorithms, network structures, and loss functions is guided by physical priors. This results in a series of interpretable deep learning methods for MRI.

報告人：崔卓須

報告人簡介：中國科學院深圳先進技術研究院，PI，副研究員，中科院特别研究助理項目資助，深圳市“鵬城孔雀計劃”特聘崗位。2020年畢業于武漢大學數學與統計學院應用數學專業，獲得理學博士學位。主要研究領域為計算磁共振成像，近五年在IEEE SPM (IF 14.9)、IEEE TMI (IF 10.6)、MEDIA (IF 10.9)和Inverse Problems (IF 2.1)等學術期刊發表論文20餘篇，在ISMRM、AAAI等會議上發表會議論文/摘要10餘篇。主持國家自然科學基金、中國博士後科學基金等，作為科研骨幹參與科技部“十四五”數學和應用研究重點專項、國家自然科學基金重點項目、數學天元基金項目等。

時  間：2024年1月14日（周日）下午14：00開始

地  點：騰訊會議号：63129598867

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