Reconstruction in deep learning of highly under-sampled T2-weighted image with T1-weighted image.

T1WI and T2WI are clinical routines for assessing structure and pathology, respectively. Typical scan time for both images is ~10 minutes, which also subjects to motion artifact from the patient. More patient groups will be able to be imaged if the scan time can be reduced further. Parallel imaging is commonly used to increase the acquisition speed by factors of 1.5 to 3 in most commercially available applications. In theory, much higher acceleration is possible but currently limited by artifact and signal-to-noise ratio (SNR) considerations. In this study, we propose a deep learning approach to achieve high acceleration rate of T2WI by incorporating T1WI into the reconstruction of the highly undersampled T2WI. We adopt the deep fully convolutional neural network that consists of a contracting path and a symmetric expanding path that can leverage the context information from multi-scale feature maps. Our results suggest that the acceleration rate can be as high as 8 or above with negligible penalty of aliasing artifact and SNR.