Denoising of 3D Fast Spin Echo Magnetic Resonance Imaging Using Multi-channel Three-dimensional Convolutional Neural Network

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.