Abstract #3267

Improving Data Consistency and Generalizability in Deep Learning-Based QSM Reconstruction

Yuanbo Zhang^1,2, Yudu Li^1,3,4, Ruihao Liu^1,5, Ziwen Ke^1,5,6, Wen Jin^1,2, Yibo Zhao^1,2, Yao Li^5,6, and Zhi-Pei Liang^1,2

¹Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ²Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁵School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ⁶Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: Susceptibility/QSM, Quantitative Susceptibility mapping

Motivation: QSM reconstruction is a highly ill-posed inverse problem. Machine learning promises to address this problem by incorporating data-driven priors. But existing learning-based QSM reconstructions suffer from data inconsistency and limited generalizability.

Goal(s): To improve data consistency and generalizability of learning-based QSM reconstruction.

Approach: Data consistency was improved by enforcing measured tissue phase in k-space, leveraging the characteristics of the dipole kernel. Generalizability was improved by augmenting a small COSMOS training dataset with extensive phase and spatial variation from a large single-orientation QSM dataset.

Results: The proposed method has been validated on both simulation and in vivo data, producing data-consistent and generalizable QSM maps.

Impact: With improved data consistency and generalizability, the proposed method could significantly enhance the reliability of AI-powered QSM reconstruction, making it a practically useful tool to support a wide range of QSM studies in scientific and clinical applications.

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