Improving Oscillating Gradient Spin Echo based Time-Dependent Diffusion Imaging with Deep Learning-based Reconstruction: A Feasibility Study

Motivation: Time-dependent diffusion MRI (td-dMRI) using oscillating gradient spin echo (OGSE) sequences is limited by low signal-to-noise ratio (SNR) and image quality. Goal(s): To investigate the potential of combining OGSE sequences with deep learning-based reconstruction (DLR) to enhance image quality and precision of quantitative results in td-dMRI. Approach: The OGSE-based td-dMRI images were reconstructed using both conventional method and DLR. The image SNR and quality, the precision of quantitative metrics and cellular-level microstructure maps without and with DLR were compared. Results: DLR improved the SNR of images and ADC maps, eliminated Gibbs-ringing artifacts, and reduced singular values and outliers in cellular-level microstructure maps. Impact: The combination of OGSE sequences with DLR shows promise in enhancing the image quality and quantification accuracy of td-dMRI. It may increase the feasibility and acceptance of the clinical application of OGSE-based td-dMRI.