Meeting Banner
Abstract #4195

Clinical assessment of a deep learning-based denoising method for DWI of Nasopharyngeal Cancer

Tiebao Meng1, Haibin Liu1, Haoqiang He1, Jialu Zhang2, and Long Qian2
1Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China, 2MR Research, GE Healthcare, Beijing, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Diffusion/other diffusion imaging techniquesDeep Learning reconstruction (DLR) has the potential to reduce MRI scan time while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study evaluated results of DLR in 66 patients undergoing clinical DWI of nasopharyngeal cancer (NPC). To assess the image quality of the DWI with DLR, each patient underwent three different protocols: conventional DWI without DLR, fast DWI without DLR and fast DWI with DLR. The image quality was evaluated among these groups. Preliminary results suggested the feasibility of fast DWI with DLR in the diagnosis of NPC with reduced scan time.

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.

Click here for more information on becoming a member.

Keywords