Model-based deep learning reconstruction by SmartSpeed AI for head and neck contrast-enhanced 3D-T1 weighted imaging

Motivation: Fat-suppressed (Fs) contrast-enhanced (CE) three-dimensional (3D) T1-weighted imaging (T1WI) enables the clear visualization of head and neck structures; however, it requires a long scanning time to obtain high quality images. Goal(s): To demonstrate the utility of model-based deep learning (DL) reconstruction, named SmartSpeed AI, for the acquisition of Fs-CE-3D T1WI of the head and neck. Approach: Three reconstruction techniques were compared for head and neck Fs-CE-3D T1WI: 1) conventional compressed-sensing sensitivity-encoding (CS), 2) CS followed by end-to-end DL reconstruction, and 3) SmartSpeed AI. Results: SmartSpeed AI provided the superior image quality than other two reconstruction techniques. Impact: SmartSpeed AI, a model-based deep learning deep learning reconstruction technique, demonstrated improved image quality in head and neck Fs-CE-3D T1WI, even with a high reduction factor of 12, compared to conventional CS and CS followed by end-to-end deep learning reconstruction.