Keywords: Skeletal, MSK
Motivation: Three-dimensional (3D) Fast Spin Echo (FSE) magnetic resonance imaging (MRI) can be acquired with high spatial resolution but at a cost of reduced signal-to-noise ratio (SNR). Deep-learning methods are promising for denoising in MRI.
Goal(s): The existing 3D denoising convolutional neural networks (CNNs) can be further improved with the sturcture to extract high dimensional features.
Approach: We developed a deep-learning approach based on multi-channel 3D CNN to utilize inherent noise information embedded in multiple number-of-excitation (NEX) acquisition.
Results: The proposed method achieves improved denoising performance compared to the current state-of-the-art denoising methods in both slice-by-slice 2D and 3D metrics of PSNR and SSIM.
Impact: The proposed network can realize a denoised effect with details well preserved for clinically achievable 2-NEX MR images. This shows great potential for 3D MRI, fast imaging, and low-feild MRI that demanding for noise suppression.
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