Keywords: Data Processing, Diffusion/other diffusion imaging techniquesIn the past decade, complex signal averaging has been investigated for diffusion weighted imaging to address the well-known noise floor issue. The robustness of complex signal averaging highly depends on the performance of phase correction to remove the shot-to-shot background phase variations. To achieve optimal phase correction, parameters (kernel size, regularization terms, etc.) need to be tuned for different anatomies, SNR levels and/or image size. In this work, we evaluated a deep-learning based phase correction method for various DWI applications, including brain, liver, prostate and showed improved complex signal averaging with lower noise floor and less artifacts.
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