Meeting Banner
Abstract #3626

Deep learning-based Reconstruction with Super Resolution for Abdominal Diffusion Weighted Imaging

Jihun Kwon1, Jiro Sato2, Kohei Yuda2, Masami Yoneyama1, Yasutomo Katsumata3, Hiroshi Hamano1, Makoto Obara1, and Marc Van Cauteren3
1Philips Japan, Tokyo, Japan, 2Tokyo Metropolitan Police Hospital, Tokyo, Japan, 3BIU MR, Philips Healthcare, Tokyo, Japan

Synopsis

Keywords: Pancreas, Image Reconstruction, AI, Super Resolution

Motivation: Abdominal diffusion-weighted imaging (DWI) plays a significant role in the detection and characterization of lesions. However, the spatial resolution of single-shot echo-planar imaging (ssh-EPI) readout is limited by the acquisition time.

Goal(s): To enhance the image quality and sharpness of abdominal ssh-EPI-DWI image using a prototype AI-based reconstruction technique (SuperRes).

Approach: We examined eight healthy volunteers using abdominal ssh-EPI-DWI, and the acquired data were reconstructed using both conventional Compressed SENSE and SuperRes. The image quality was assessed qualitatively and quantitatively.

Results: SuperRes demonstrated a significant improvement in the image quality and sharpness of both DWI and ADC map.

Impact: The dedicated deep learning-based super-resolution technique enhanced the image quality and sharpness in abdominal ssh-EPI-DWI. Enhanced sharpness resulted in better delineation of structures, such as the pancreas. The improvement in image quality was demonstrated in both qualitative and quantitative assessments.

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