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TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling

arXiv 2408.05705 ↗First posted 11 Aug 2024eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

Magnetic Resonance Imaging (MRI) has become essential in clinical diagnosis due to its high resolution and multiple contrast mechanisms. However, the relatively long acquisition time limits its broader application. To address this issue, this study presents an innovative conditional guided diffusion model, named as TC-KANRecon, which incorporates the Multi-Free U-KAN (MF-UKAN) module and a dynamic clipping strategy. TC-KANRecon model aims to accelerate the MRI reconstruction process through deep learning methods while maintaining the quality of the reconstructed images. The MF-UKAN module can effectively balance the tradeoff between image denoising and structure preservation. Specifically, it presents the multi-head attention mechanisms and scalar modulation factors, which significantly enhances the model's robustness and structure preservation capabilities in complex noise environments. Moreover, the dynamic clipping strategy in TC-KANRecon adjusts the cropping interval according to the sampling steps, thereby mitigating image detail loss typicalching the visual features of the images. Furthermore, the MC-Model incorporates full-sampling k-space information, realizing efficient fusion of conditional information, enhancing the model's ability to process complex data, and improving the realism and detail richness of reconstructed images. Experimental results demonstrate that the proposed method outperforms other MRI reconstruction methods in both qualitative and quantitative evaluations. Notably, TC-KANRecon method exhibits excellent reconstruction results when processing high-noise, low-sampling-rate MRI data. Our source code is available at https://github.com/lcbkmm/TC-KANRecon.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 45 references that are in the map. First posted on arXiv 11 Aug 2024.

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References · 26

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
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  10. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
  11. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  12. 2017Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Losscited by 82
14 more
  1. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model samplingcited by 59
  2. 2022ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformercited by 52
  3. 2017A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networkscited by 43
  4. 2022SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluationcited by 42
  5. 2022High-Frequency Space Diffusion Models for Accelerated MRIcited by 41
  6. 2021AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysiscited by 38
  7. 2021Over-and-Under Complete Convolutional RNN for MRI Reconstructioncited by 29
  8. 2017ADMM-Net: A Deep Learning Approach for Compressive Sensing MRIcited by 28
  9. 2019Pyramid Convolutional RNN for MRI Image Reconstructioncited by 14
  10. 2020MRI Image Reconstruction via Learning Optimization Using Neural ODEscited by 14
  11. 2019Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstructioncited by 8
  12. 2019SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstructioncited by 6
  13. 2024TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet methodcited by 5
  14. 2023BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray Imagescited by 3

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