Accelerate Single-Channel 3D MRI through Undersampling and Deep Neural Network Reconstruction

3D MRI data contains more redundant information than 2D MRI data, which is favourable for reconstruction. However, deep learning reconstruction of 3D MRI data remains to be explored due to the computational burden that scales exponentially with spatial dimensions. This study presents a deep learning method to reconstruct single-channel 3D MRI data with uniform undersampling along two phase-encoding directions, in which conventional multi-channel parallel imaging methods are generally not applicable. The results demonstrate the robust reconstruction for single-channel 3D MRI data at high acceleration and in the presence of anomaly.