Meeting Banner
Abstract #2114

Deep Learning-based Super-Resolution reconstruction for Fast T1 and T2-weighted Head and Neck MRI

Shuang Li1, Weijie Yan1, Xiao Yong Zhang 2, Lin Ji1, and Qiang Yue1
1West China Hospital of Sichuan university, Chengdu, China, 2Clinical Science, Philips Healthcare, Chengdu, China

Synopsis

Keywords: Image Reconstruction, Image Reconstruction

Motivation: Current acceleration techniques for head and neck MRI face trade-offs between acquisition speed and image quality.

Goal(s): To evaluate a novel deep learning-based reconstruction framework integrating compressed sensing and super-resolution techniques for T1- and T2-weighted head and neck MRI.

Approach: We prospectively enrolled 54 patients who underwent paired conventional and DL-reconstructed sequences, with quantitative and qualitative assessment by two radiologists.

Results: The framework achieved 46.3% and 26.9% reduction in acquisition time for T1WI and T2WI respectively, with significant improvements in SNR (both P<0.001), CR (both P<0.001), and superior qualitative scores in image sharpness, lesion conspicuity, and overall image quality (all P<0.05).

Impact: This integrated deep learning framework offers a clinically viable solution for accelerated head and neck MRI acquisition while enhancing image quality, potentially improving workflow efficiency and patient comfort in routine clinical practice.

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