Joint learning of optimal acquisition and high quality ADC mapping for low field diffusion-weighted PROPELLER MRI

Motivation: Adequate image signal-to-noise ratio (SNR) and resolution within a reasonable scan time is challenging for low-field diffusion quantitative MRI. Goal(s): To present a PROPELLER-acquisition and ADC mapping joint learning neural network to accelerate DWI with improved image SNR and resolution. Approach: Considering the similar anatomical structure between diffusion-weighted MR images, this work achieved DWI PROPELLER-acquisition optimization and reconstructed high quality ADC maps from data acquired by optimized acquisition using U-net. Results: In vivo and simulation results demonstrate that our proposed method can generate high SNR and resolution ADC maps within 2 minutes scan time under 0.23T human scanner. Impact: Joint learning acquisition and quantitative reconstruction based on PROPELLER sampling trajectory using neural network has successfully achieved fast ADC mapping, offering great possibility for quantitative analysis in low-field diffusion MRI.