Abstract #5213

High-Speed Tissue Quantification Using Spatiotemporal Attention Mechanism with Undersampling Strategy Consideration

Jintao Wei¹, Paween Wongkornchaovalit¹, Xiaozhi Cao^2,3, Zihan Zhou^3,4, Qiuping Ding¹, HuiHui Ye⁵, Jianhui Zhong⁶, and Hongjian He^7,8

¹College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China, ²Department of Electrical Engineering, Stanford University, Stanford, CA, United States, ³Department of Radioloey, Stanford University, Stanford, CA, United States, ⁴Graduate school of Eduction, Stanford Universiy, Stanford, CA, United States, ⁵School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China, ⁶Department of Imaging Sciences, University of Rochester, Rochester, NY, United States, ⁷School of Physics, Zhejiang University, Hangzhou, China, ⁸State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China

Synopsis

Keywords: MR Fingerprinting, AI/ML Image Reconstruction

Motivation: Current deep learning methods for accelerating MRF rely on extreme undersampling in k-space or time frames, but this is suboptimal. And the model designs struggle to fully utilize the spatiotemporal features of the data.

Goal(s): To develop a method that identifies the optimal two-dimensional undersampling combination, surpassing single-dimension undersampling in quantitative performance.

Approach: We enhanced Stolk et al.'s error model and developed a Spatiotemporal Attention with Tissue Specificity Network (STA-TS) that integrates tissue features across spatial and temporal dimensions.

Results: This approach achieves 1mm full brain coverage in 2.5 minutes, with significantly improved quantitative accuracy for T1 and T2 compared to single-dimension undersampling.

Impact: The proposed STA-TS network significantly improves MRF accuracy and efficiency, achieving full brain coverage at 1 mm resolution in just 2.5 minutes, providing a faster and more accurate imaging solution for clinical applications.

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.