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<p>Parallel magnetic resonance imaging (pMRI) accelerates data acquisition by undersampling multi-coil <i>k</i>-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 <i>k</i>-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. The implementation code is available at <a href="https://github.com/Chenvp/SRSC">https://github.com/Chenvp/SRSC</a>.</p>

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A Bilevel Sensitivity-Corrected Reconstruction Framework with Deep Priors for Parallel MRI

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • Weipeng Chen,
  • Yan-Ran Li,
  • Raymond H. Chan,
  • Lixin Shen,
  • Xiaosheng Zhuang

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. The implementation code is available at https://github.com/Chenvp/SRSC.