Noise removal in clinical CT is necessary to make images clearer and enhance the diagnostic quality of an image. There are several deep learning techniques designed to remove noise in CT, however, they have several thousand parameters, making the behavior difficult to comprehend. We attempt to alleviate this problem by using known denoising models to remove the noise.
Due to the non-stationary nature of the CT noise, it is natural that the image will require different noise filtering strengths at different points in the image. One way to ensure this is to tune the parameters at each point in the image. Since a ground truth cannot be established for pixelwise ideal parameter values, this task can be formulated as a reinforcement task, which maximizes the image quality. Our previous research established such an approach for the joint bilateral filter.
In this thesis, we aim to complete the following tasks:
- Develop a general reinforcement learning framework for parameter tuning problems in medical imaging.
- Experiment with different denoising models such as non-local means, and block matching 3D.
- Experiment with a parameter selection strategy to choose which parameters to include into the learning process
- Study the impact of parameter tuning on denoising, and of the denoising model on the parameter tuning and the overall image quality.
In this thesis, the AAPM Grand Challenge dataset and Mayo Clinic TCIA dataset will be used. Quality shall be measured using PSNR and SSIM, and perhaps IRQM.
Requirements:
- Some knowledge of image processing. Experience with image processing libraries is a plus
- Good knowledge of PyTorch and C++
- Understanding of CT reconstruction and CT noise
- Experience with deep Q learning