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

clinical for to acceleration is possible currently by and signal-to-noise ratio (SNR) this 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. T1-weighted image (T1WI) and T2-weighted image (T2WI) are routinely acquired in MRI protocols, which can provide complementary information to each other. However, the acquisition time for each sequence is non-trivial, making clinical MRI a slow and expensive procedure. With the purpose to shorten MRI acquisition time, we present a deep learning approach to reconstruct T2WI from T1WI and highly under-sampled T2WI. Our results demonstrate that the proposed method could achieve 8 or higher acceleration rate while keeping high image quality of the reconstructed T2WI.