Research
Optimization & 3D Imaging
Multi-Objective Optimization for Medical Imaging
Overview
This research investigates the intersection of multi-objective optimization and 3D medical imaging. By combining gradient-based methods with modern learning approaches, we aim to advance the state of the art in volumetric medical image analysis and reconstruction.
Technical Approach
- Gradient-Based Methods: Leveraging advanced optimization algorithms for efficient parameter tuning.
- Deep Learning: Applying neural networks to learn complex 3D representations from medical scans.
- Multi-Objective Optimization: Balancing competing objectives like image quality, speed, and radiation dose.
- Volumetric Analysis: Processing full 3D volumes rather than individual 2D slices.
Applications
- CT Reconstruction: Improving image quality from fewer projections.
- MRI Enhancement: Accelerating scan times while maintaining diagnostic quality.
- 3D Segmentation: Accurate organ and tumor delineation in volumetric data.
- Radiation Planning: Optimizing treatment plans with multiple objectives.
Research Significance
The ability to efficiently optimize across multiple competing objectives is crucial in medical imaging, where we constantly balance trade-offs between image quality, acquisition time, patient comfort, and radiation exposure. This research contributes novel methodologies that can be applied across various imaging modalities and clinical scenarios.