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Welcome to CXIL (Computational X-ray Imaging Laboratory, 전산엑스레이이미징 연구실)
Our lab conducts research in advanced X-ray imaging technologies, including detector design, image reconstruction, dose estimation, and deep learning-based image processing. We are currently investigating trajectory optimization in robot-assisted CT systems using deep learning and computational algorithms, aiming to improve image quality and efficiency for object-specific imaging applications.
Advanced detector design
Scintillator
Photon-counting detectors
Mathematical modeling using cascaded-systems analysis (CSA)
Experimental measurements
Monte Carlo simulation
Overseas collaborations (Western University, Ryerson University, etc.)
Dual-energy imaging
Single-shot dual-energy imaging (Sadwitch detectors)
Image registration algorithm
Background suppression
Mathematical modeling
Model-based optimization
Medical / industrial applications
Registration
Linear model
Optimal design
Application
Image reconstruction
2D / 3D reconstruction algorithm
Iterative / sparse-view reconstructions (low-dose)
Algorithm optimization
CT system configuration
CT simulations
Controller Board
Character LCD
AVR mega 128 module
Absorbed dose estimation
Evaluation of patient dose
Experimental measurements (collaboration w/ PNUYH)
Monte Carlo simulations
Mathematical modeling
Image processing & deep learning
Image processing techniques
Deep learning model development
Background suppression model
Network optimization (acceleration)
Trajectory optimization for robot-CT
Trajectory optimization for robot-CT
Trajectory optimization for object-specific CT scanning
CT scan trajectory optimization using deep learning and algorithm
Development of non-destructive inspection system using a robotic arm
Reconstruction algorithm suitable for arbitrary scan trajectories
Reference
Conventional
RL-optimized
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