Keywords: AI/ML Image Reconstruction, Image Reconstruction
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.
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