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<p>Dynamic cardiac magnetic resonance imaging is a robust non-invasive imaging technique capable of quantitatively evaluating cardiovascular function. Nevertheless, its clinical application is hindered by the relatively slow data acquisition. Accelerated imaging strategies that rely on acquiring undersampled <i>k</i>-space data offer a practical approach to mitigate this drawback, yet producing high-quality reconstructions with limited measurements remains difficult. To address this, we propose a multi-scale sparse learning network (MSSL-Net) built upon an iterative unfolding framework. Each iterative reconstruction unit of the network integrates a multi-scale attention fusion module, a deep sparse module, and a gradient update module, enabling the full exploitation of multi-scale spatial features and sparse priors of the image. Extensive experimental results on two public cardiac MRI datasets demonstrate that MSSL-Net clearly outperforms the comparison methods under various undersampling patterns and acceleration factors.</p>

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Multi-Scale Sparse Learning Network for Dynamic Cardiac Magnetic Resonance Imaging Reconstruction

  • 期刊论文

  • 发表日期：

    2026年5月8日

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  • Jizhong Duan,
  • Xiao Li

摘要

Dynamic cardiac magnetic resonance imaging is a robust non-invasive imaging technique capable of quantitatively evaluating cardiovascular function. Nevertheless, its clinical application is hindered by the relatively slow data acquisition. Accelerated imaging strategies that rely on acquiring undersampled k-space data offer a practical approach to mitigate this drawback, yet producing high-quality reconstructions with limited measurements remains difficult. To address this, we propose a multi-scale sparse learning network (MSSL-Net) built upon an iterative unfolding framework. Each iterative reconstruction unit of the network integrates a multi-scale attention fusion module, a deep sparse module, and a gradient update module, enabling the full exploitation of multi-scale spatial features and sparse priors of the image. Extensive experimental results on two public cardiac MRI datasets demonstrate that MSSL-Net clearly outperforms the comparison methods under various undersampling patterns and acceleration factors.