Deep Learning-based Regularizers for Cone Beam Computed Tomography Reconstruction

Abstract: Cone Beam Computed Tomography is a technology to visualize the 3D interior anatomy of a patient. It is important for image-guided radiation therapy in cancer treatment. During a scan, iterative methods are often used for the image reconstruction step. A key challenge is the ill-posedness of the resulting inversion problem, causing the images to become noisy. To combat this, regularizers can be introduced, which help stabilize the problem. This thesis focuses on Adversarial Convex Regularization that with deep learning regularize the scans according to a target image quality. It can be interpreted in a Bayesian setting by letting the regularizer be the prior, approximating the likelihood with the measurement error, and obtaining the patient image through the maximum-a-posteriori estimate. Adversarial Convex Regularization has previously shown promising results in regular Computed Tomography, and this study aims to investigate its potential in Cone Beam Computed Tomography.  Three different learned regularization methods have been developed, all based on Convolutional Neural Network architectures. One model is based on three-dimensional convolutional layers, while the remaining two rely on 2D layers. These two are in a later stage crafted to be applicable to 3D reconstruction by either stacking a 2D model or by averaging 2D models trained in three orthogonal planes. All neural networks are trained on simulated male pelvis data provided by Elekta. The 3D convolutional neural network model has proven to be heavily memory-consuming, while not performing better than current reconstruction methods with respect to image quality. The two architectures based on merging multiple 2D neural network gradients for 3D reconstruction are novel contributions that avoid memory issues. These two models outperform current methods in terms of multiple image quality metrics, such as Peak Signal-to-Noise Ratio and Structural Similarity Index Measure, and they also generalize well for real Cone Beam Computed Tomography data. Additionally, the architecture based on a weighted average of 2D neural networks is able to capture spatial interactions to a larger extent and is adjustable to favor the plane that best shows the field of interest, a possibly desirable feature in medical practice.