Reduction of ADC bias with deep learning-based acceleration in diffusion-weighted MRI: A phantom validation study

Further acceleration of diffusion MRI in clinical examinations is desired but challenging mainly due to low signal and associated potential bias in the quantitative apparent diffusion coefficient (ADC) values. Artificial intelligence-based denoising and image reconstruction may provide a solution to address this challenge. We investigate and compare different image reconstruction methods, including conventional parallel imaging, compressed sensing, and a deep learning-based technique, in ADC accuracy and precision using a diffusion phantom with illustration of the principle in numeric simulation.