MR-Srnet: Transformation of Low Field MR Images to High Field MR Images
Prabhjot Kaur, Aditya Jaikumar Sharma, Aditya Nigam, Arnav V. Bhavsar · 2018
We propose an approach to reconstruct high field (7T) like MR images from low field (3T) MR images, which involves a merged convolutional autoencoder neural network. The network uses merge connections from downsampling encoder layers to cascade the input at upsampling layers of the decoder in order to preserve the local image details in transformed space. Further, we have used three channel input, defined by its image intensity values and corresponding gradient values in order to guide the discrimination. In terms of comparison with state-of-the-art, the proposed algorithm reconstruct 7T MR images with better tissue contrast, yields quantitative improvements, and has a significantly more efficient run-time. We also demonstrate the effectiveness of the approach with low training data and noise.