Multi-contrast magnetic resonance imaging (MRI) is usually required in clinical diagnosis but different contrast MRI may need different scan time. To balance total scan time and reconstruction fidelity, the recovery of multi-contrast MR images relies on the collaborative acquisition of sampling patterns and reconstruction algorithm. We proposed a novel neural network that could jointly optimize sampling patterns and concurrently reconstruct multi-contrast MR images. The reconstructed multi-contrast MR images using optimized sampling patterns on a two-contrast dataset demonstrate that the average peak signal-to-noise ratio and structural similarity among contrasts improve obviously compared with reconstructed results using fixed and independent sampling patterns.
This abstract and the presentation materials are available to members only; a login is required.