报告题目：A Bilevel Sensitivity-Corrected Reconstruction Framework with Deep Priors for Parallel MRI

报告人：李炎然副教授，深圳大学

报告时间：2026年9月16日（周三）14:00-17:00

报告地点：20-306

报告摘要：Parallel magnetic resonance imaging (pMRI) accelerates data acquisition by undersampling multi-coil K-space. Its reconstruction quality, however, deteriorates when coil sensitivity maps (CSMs) or calibration kernels are inaccurate, especially when only limited auto-calibration signal (ACS) data are available. We propose SRSC+, a model-driven bilevel optimization framework that couples SENSE-based image reconstruction with SPIRiT-based K-space calibration through shared CSMs. The bilevel formulation explicitly decouples sensitivity estimation from kernel calibration, thereby enabling iterative correction of both components and reducing error accumulation that often arises in dual-domain methods. In addition, SRSC+ introduces a deep-prior-guided regularization strategy that preserves the structure of classical linear regularizers while adaptively learning spatially varying regularization weights from denoised intermediate reconstructions. Experiments on out-of-distribution datasets under diverse sampling patterns show that SRSC+ achieves state-of-the-art performance across multiple fidelity and perceptual metrics, while remaining robust to scarce ACS data and imperfect CSM initialization. Visual comparisons further demonstrate effective artifact suppression without pseudo-structural distortions, together with strong generalization across scanners and acquisition protocols.

报告人简介：李炎然，深圳大学计算机与软件学院副教授、博士生导师。先后主持国家自然科学基金面上项目、青年科学基金项目、广东省自然科学基金项目和深圳市基础研究计划项目。主要从事紧框架理论、正则化方法及其在图像处理和医学图像重建中的应用研究，相关成果发表在Applied and Computational Harmonic Analysis、SIAM Journal on Imaging Sciences、IEEE Transactions on Image Processing等国际权威期刊。

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