Performance Evaluation of Deep Learning-based Image Reconstruction for Head and Neck Imaging Protocol

MRI has excellent extracranial soft-tissue contrast to detect tumors in the head and neck (HN) region. Technical challenges arise due to MRI related artifacts. In routine radiological practice, HN MR imaging protocols are optimized specifically to the subsites. We aimed to evaluate the performance of the HN imaging protocol that include qualitative T1w, T2w, and quantitative diffusion MRI powered by a novel deep learning (DL) based reconstruction (recon) using the ACR and QIBA diffusion phantoms. This phantom study showed that qualitative T1w and T2w images and multiple b-value DWI data powered with DL recon substantially improves the image quality.