Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images
This work addresses the need for affordable high-quality medical imaging for diagnosis, though it is incremental as it builds on existing enhancement techniques.
The paper tackles the problem of reconstructing high-quality 7T-like MR images from lower-quality 3T MR images to reduce costs, achieving improved performance and faster reconstruction times compared to existing methods.
Increasing demand for high field magnetic resonance (MR) scanner indicates the need for high-quality MR images for accurate medical diagnosis. However, cost constraints, instead, motivate a need for algorithms to enhance images from low field scanners. We propose an approach to process the given low field (3T) MR image slices to reconstruct the corresponding high field (7T-like) slices. Our framework involves a novel architecture of a merged convolutional autoencoder with a single encoder and multiple decoders. Specifically, we employ three decoders with random initializations, and the proposed training approach involves selection of a particular decoder in each weight-update iteration for back propagation. We demonstrate that the proposed algorithm outperforms some related contemporary methods in terms of performance and reconstruction time.