Introduction:
Photon counting computed tomography (PCCT) marks a new era in medical imaging, offering an unprecedented ability to discriminate between different photon energy levels. This feature of PCCT is crucial for enhancing image contrast and specificity, allowing for more accurate tissue characterization. However, efficiently managing and converting between these diverse energy levels in a clinically practical manner remains a significant challenge.
This project aims to utilize diffusion model to streamline and optimize the energy level conversion process in PCCT. By leveraging the advanced pattern recognition and computational capabilities of diffusion model, the project intends to develop a system that can automatically and accurately translate between different photon energy levels, enhancing the utility and clarity of PCCT images.
The ultimate goal is to provide a robust and efficient framework that not only improves the diagnostic quality of PCCT images but also expands the practical applications of this technology in clinical settings. This involves intricate work in both the development of diffusion model and the understanding of the physics underlying PCCT.
Requirements:
- Completion of Deep Learning is mandatory.
- Proficiency in PyTorch is essential.
- Strong analytical and problem-solving skills.
Prospective candidates are warmly invited to send their CV and transcript to yipeng.sun@fau.de.