This project focuses on reimplementing the Detectability Index for evaluating individual CT projections, with the goal of improving the performance and adaptability of existing Python-based algorithms using PyTorch. The selected candidate will delve into the current code, identify performance bottlenecks, and propose innovative solutions to optimize efficiency. The goal is to minimize package dependencies to ensure code longevity and maintainability.
The following questions should be considered:
- How can the existing Python code be improved with PyTorch for better performance and adaptability?
- Where do the current code’s performance bottlenecks lie, and how can these be addressed?
- How can the usage of external packages be minimized to ensure the code’s longevity?
- What innovative approaches can be implemented to enhance the Detectability Index calculation?
- How can the updated algorithm be validated for effectiveness and efficiency?
Candidates should possess strong skills in Python and PyTorch, with the ability to quickly understand and improve upon existing code. A background in computational imaging or related fields, along with a problem-solving mindset, is essential.
For your application, please send your transcript of record.