In previous research, we have developed a method, based on reinforcement learning, to denoise cone-beam CT. This method involved the use of denoisers in both the sinogram and the reconstructed image domain. The denoisers are bilateral filters with the sigma parameters tuned by a convolutional agent.
Recent research has shown the use of neural ODEs to improve the speed of convergence of neural network training. Neural ODEs have been applied to tasks which can be modelled by differential equations, such as fluid mechanics. They have also been expanded to cover classical deep learning tasks, such as image segmentation.
In this thesis we aim to complete the following tasks:
- Experiment with different recon kernels (B40, B70 etc.) to observe the effect of sharpness dependent noise.
- Implement neural ODE to speed up reinforcement learning convergence, and also reduce parameter count.
- Implement data consistent reward to ensure correct reconstruction and data consistent denoising.
- Experiment with deep learned quality metrics as additional reward functions for parameter tuning
As a dataset, we will use the Mayo Clinic TCIA dataset for testing the quality of our denoising algorithms. Quality can be compared with standard dose images using PSNR and SSIM, and can be calculated reference-free using the IRQM. If time permits, we can use deep model observers to assess low contrast preservation.
- Knowledge of CT reconstruction techniques. Knowledge of the ASTRA toolbox is a plus.
- Understanding of reinforcement learning
- Experience with PyTorch for developing neural networks
- Experience with image processing