Helical CT is the most commonly used CT scan protocol in clinical CT today. Helical CT generally applies a cone-beam scan in a spiral trajectory over the object to be scanned. The collected sinograms, and subsequently reconstructed volumes, contain some amount of noise due to fluctuations in the line integrals. Removing this noise is necessary for diagnostic image quality.
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. The reconstruction was carried out by the FDK algorithm in the ASTRA toolbox.
Due to the lack of time, we had limited our previous research to the simpler problem of circular cone-beam CT. In this research internship, we hope to extend our method to denoise helical CT as well. Since helical CT uses cone-beam projections, we hope that our method will work out of the box without any retraining being needed.
The following tasks are to be conducted as part of this research internship:
- Develop methods to reconstruct helical CT for the given sinograms i.e. ADMM, WFBP
- Formulate and train a reinforcement learning task to train denoisers for helical CT in sinogram and volume domain
- Figure out ways to train tasks without ground truth volumes, to obtain image quality better than currently existing methods
- Train current volume based neural network solutions (GAN-3D, WGAN-VGG, CPCE3D, QAE, etc.) and compare the solutions.
Requirements:
- Knowledge of CT reconstruction techniques
- Understanding of reinforcement learning
- Experience with PyTorch for developing neural networks
- Experience with image processing. Knowledge of the ASTRA toolbox is a plus.