Joint Multi-Contrast Image Reconstruction with Self-Supervised Learning

Abstract

Motivation: Self-supervised learning via data undersampling (SSDU) uses single contrast images in reconstruction, but a typical protocol contains multiple contrasts that provide additional information. Goal(s): Our goal is to improve self-supervised image reconstruction fidelity by jointly reconstructing multi-contrast images. Approach: We modify SSDU by concatenating independently under-sampled contrasts along the channel dimension in a VarNet architecture. Results: Joint multi-contrast SSDU reconstructs with higher SSIM and lower NMSE than single contrast supervised and self-supervised methods. Impact: Joint multi-contrast SSDU produces higher quality reconstructions than single-contrast methods, without fully-sampled training data. Accelerated multi-contrast imaging protocols will benefit from higher diagnostic quality or higher acceleration factors.