Feasibility of Deep Learning Reconstruction in Prostate Multiparametric MRI: a Preliminary Prospective Study

In this prospective study, feasibility of deep learning reconstruction (DLR) in axial FSE-T2WI and axial reduced-FOV DWI (FOCUS DWI) were evaluated compared with standard protocols. Fast protocol with DLR substantially reduced scanning time (axial FSE-T2WI: -32.1%; FOCUS-DWI: -36.8%). Fast FOCUS DWI with DLR showed the highest SNR and CNR for prostate PZ, TZ and lesion. Fast FSE-T2WI with DLR showed the highest SNR and CNR for prostate PZ and TZ. Moreover, fast FOCUS-DWI and FSE-T2WI with DLR demonstrated equivalent or better image quality than standard images. DLR may be useful in prostate multiparametric MRI protocol optimization and high-quality image acquisition.