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<p>NUFFT is widely used in MRI reconstruction, offering a balance of efficiency and accuracy. However, it struggles with uneven or sparse sampling, leading to unacceptable under sampling errors. To address this, we introduced DCNUFFT, a novel method that enhances NUFFT with deep convolutional neural network. The interpolation kernel and density compensation in inverse NUFFT were replaced with trainable neural network layers and incorporated a new global correlation prior in the spatial-frequency domain to better recover high-frequency information, enhancing reconstruction quality. DCNUFFT outperformed inverse NUFFT, iterative methods, and other deep learning approaches in terms of normalized root mean square error (NRMSE) and structural similarity index (SSIM) across various anatomies and sampling trajectories. Importantly, DCNUFFT also excelled in reconstructing under sampled PET and CT data, showing strong generalization capabilities. In subjective evaluations by radiologists, DCNUFFT scored highest in visual quality (VQ) and lesion distinguishing ability (LD), highlighting its clinical potential.</p>

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Deep Convolutional Neural Network Enhanced Non-uniform Fast Fourier Transform for Undersampled MRI Reconstruction

  • 期刊论文

  • 发表日期：

    2025年2月22日

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  • Yuze Li,
  • Haikun Qi,
  • Zhangxuan Hu,
  • Haozhong Sun,
  • Guangqi Li,
  • Zhe Zhang,
  • Yilong Liu,
  • Hua Guo,
  • Huijun Chen

摘要

NUFFT is widely used in MRI reconstruction, offering a balance of efficiency and accuracy. However, it struggles with uneven or sparse sampling, leading to unacceptable under sampling errors. To address this, we introduced DCNUFFT, a novel method that enhances NUFFT with deep convolutional neural network. The interpolation kernel and density compensation in inverse NUFFT were replaced with trainable neural network layers and incorporated a new global correlation prior in the spatial-frequency domain to better recover high-frequency information, enhancing reconstruction quality. DCNUFFT outperformed inverse NUFFT, iterative methods, and other deep learning approaches in terms of normalized root mean square error (NRMSE) and structural similarity index (SSIM) across various anatomies and sampling trajectories. Importantly, DCNUFFT also excelled in reconstructing under sampled PET and CT data, showing strong generalization capabilities. In subjective evaluations by radiologists, DCNUFFT scored highest in visual quality (VQ) and lesion distinguishing ability (LD), highlighting its clinical potential.