Application of Deep Learning-based Reconstruction for Diffusion Kurtosis Imaging in Head and Neck Cancer

A Deep learning (DL)-based reconstruction is a promising method to achieve higher resolution for diffusion-weighted Kurtosis imaging (DKI) without increasing signal averaging. The DKI phantom and patient results demonstrated improved image quality and reduced Gibbs (ringing) artifact, aiding in the robust estimation of Dapp and Kapp. In all phantom and patient data, the standard deviation of Dapp and Kapp measured in images reconstructed without DL was higher than in images reconstructed using DL. The NEX=1 significantly reduced the multi-b-value data acquisition time, and the DL-based reconstruction can produce images comparable to the standard NEX=2 or 4, depending on the b-value.