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Abstract #1163

Applying advanced denoisers to enhance highly undersampled MRI reconstruction under plug-and-play ADMM framework

Kang Yan1, Zhixing Wang1, Quan Dou1, Sheng Chen1, and Craig H Meyer1,2
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 2Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, United States


Accelerating MRI acquisition is always in high demand, since long scan time can increase the potential risk of image degradation caused by patient motion. Generally, MRI reconstruction at higher undersampling rates requires regularization terms, such as wavelet transformation and total variation transformation. This work investigates employing the plug-and-play (PnP) ADMM framework to reconstruct highly undersampled MRI k-space data with three different denoiser algorithms: block matching and 3D filtering (BM3D), weighted nuclear norm minimization (WNNM) and residual learning of deep CNN (DnCNN). The results show that these three PnP-based methods outperform current regularization methods.

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