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Multi-contrast MRI images provide complementary contrast information aboutthe characteristics of anatomical structures and are commonly used in clinicalpractice. Recently, a multi-flip-angle (FA) and multi-echo GRE method(MULTIPLEX MRI) has been developed to simultaneously acquire multipleparametric images with just one single scan. However, it poses two challengesfor MULTIPLEX to be used in the 3D high-resolution setting: a relatively longscan time and the huge amount of 3D multi-contrast data for reconstruction.Currently, no DL based method has been proposed for 3D MULTIPLEX datareconstruction. We propose a deep learning framework for undersampled 3D MRIdata reconstruction and apply it to MULTIPLEX MRI. The proposed deep learningmethod shows good performance in image quality and reconstruction time.

Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning