Keywords: Prostate, Machine Learning/Artificial Intelligence, Deep learning reconstructionIn 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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords