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Fast spin echo diffusion magnetic resonance imaging (FSE-DWI) is often referred to as a standard for MRI diagnosis of Cholesteatoma. However, the acquired data require multiple steps during image reconstruction which turn out high residual artifacts. In this work, we develop rapid reconstruction for propeller FSE-DWI to improve its signal-to-noise ratio (SNR) through unrolled deep learning (DL) framework. Results show that the proposed unrolled DL reconstruction enables increasing bout 2x SNR compared to SNR obtained by online reconstructed images. Moreover, its speed is about 200x faster than conventional locally low rank constraint reconstruction.
Yarach et al. (Wed,) studied this question.