Papers
1
Total Citations
6
H-Index
1
About
Siyuan Mei is a rising star in medical imaging, whose work is reshaping cone-beam computed tomography (CBCT) reconstruction. Her key research areas include differentiable reconstruction algorithms, arbitrary-orbit CBCT, and computational imaging. Mei’s major contribution is the development of DRACO (Differentiable Reconstruction for Arbitrary CBCT Orbits), a groundbreaking method introduced in 2025 that solves the long-standing computational and memory bottlenecks of traditional iterative reconstruction. By enabling high-quality image reconstruction from non-standard, flexible scanning trajectories, DRACO opens the door to more patient-specific and hardware-efficient imaging protocols. Already garnering 6 citations in its first year, this work signals a paradigm shift in CBCT. Mei’s achievement stands out for its elegant fusion of deep learning and physics-based modeling, offering a practical solution that balances speed, accuracy, and adaptability. For students and researchers, Mei exemplifies how innovative algorithm design can directly impact clinical imaging—making her a name to watch in the future of computed tomography.
Research Focus
Key Achievements
Top Papers
- 1DRACO: differentiable reconstruction for arbitrary CBCT orbits6 citations · 2025