Introduction:
Computed Tomography (CT) is a powerful imaging modality, but its images often suffer from artifacts that can obscure crucial diagnostic information. Physics-informed artifact simulation offers a promising solution by realistically modeling artifact generation based on underlying physical principles. This approach enables improved artifact understanding, provides realistic training data for machine learning algorithms, and allows for robust evaluation of artifact correction techniques.
This project will focus on exploring state-of-the-art techniques for simulating various types of CT artifacts and investigating their impact on image quality. We will assess the potential of utilizing these simulations to develop advanced artifact reduction methodologies. By further researching this cutting-edge field, we hope to contribute to the continuous improvement of the accuracy and reliability of CT imaging.
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.