Meeting Banner
Abstract #3845

Robust Water-Fat Separation in Nasal MRI Using Hybrid Model and Data Driven Network

Dandan Li1, Kaicong Sun1, Jiadong Zhang2, and Dinggang Shen1,3,4
1School of Biomedical Engineering, & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China, 2City University of Hong Kong, HongKong, China, 3Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China, 4Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China

Synopsis

Keywords: Other AI/ML, Fat and Fat/Water Separation

Motivation: Robust water-fat separation is crucial for nasal lesion detection and tracking. Existing methods often suffer from limited contrast and issues with water-fat swaps.

Goal(s): We aim to accurately extract water/fat-only images from multi-contrast nasal MRI using two-echo FSE sequences.

Approach: We propose a hybrid model and data driven network to provide reliable water-fat separation from two-echo signals based on end-to-end learning. A 3D physical model is also used to guide the network to learn layer-wise features.

Results: The proposed framework outperforms the representative methods, with PSNR of 50.57 and SSIM of 0.9983. Visually, our method also provides more precise separation of nasal structures.

Impact: Our proposed hybrid separation framework combines multi-task networks with a 3D physical model to enhance the robustness of water/fat separation in multi-contrast nasopharyngeal MRI images, significantly expanding its clinical applicability.

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