Index

This project focuses on advancing computer tomography (CT) image reconstruction by utilizing neural network technology. The project is divided into three main sections.

In the first part, a versatile iterative reconstruction algorithm is introduced for Cone Beam CT (CBCT) reconstruction. This algorithm gradually converges to the actual values through backpropagation, using the disparity between reconstructed results and ground truth. The TV-Norm regularization method is also employed to reduce noise while preserving image edge clarity.

Recognizing the limitations of iterative reconstruction methods, the second part employs a data-driven CT reconstruction approach. A trainable filter-backprojection (FBP) reconstruction neural network is designed to enhance filter design, resulting in a filter with high-frequency noise suppression capabilities. This improves the quality of reconstruction.

In the third part, the data-driven FBCT approach is extended to the Feldkamp-Davis-Kress (FDK) reconstruction algorithm for CBCT. Through neural network training, a latent mapping is learned, progressively converging towards the Ram-lak filter. This sets the groundwork for learning complex filters tailored to non-circular trajectories.

Tackling Travelling Salesman Problem with Graph Neural Network and Reinforcement Learning

This project focuses on solving the Travelling Salesman Problem using Graph Neural Network combined with Reinforcement Learning algorithms. Two variants of Graph Neural Network are tested, including Graph Pointer Network and Hybrid Pointer Network, both trained in Actor-Critic algorithm and double Q-learning algorithm separately. Double Q-learning is tried carefully as it is rarely applied in the training of Graph Neural Network compared with Actor-Critic. The models are tested on various types of TSP instances, showing that double Q-learning algorithm is a potential competitor in the improvement of
Graph Neural Networks.

Synthetic Projection Generation with Angle Conditioning

Computed Tomography (CT) plays a vital role in medical imaging, offering cross-sectional views of internal structures. Yet, radiation exposure during CT scans poses health risks. This study explores the application of existing deep learning models to synthesize CT projections at unknown arbitrary angles. Multiple input images from varying angles, along with their corresponding ground truth data, train different network architectures to reproduce target images from different view angles. This approach potentially reduces radiation exposure and addresses challenges in obtaining specific missing angular views. Experimental results confirm the effectiveness and feasibility of the methodology, establishing it as a valuable tool in CT imaging.

Uncertainty Estimation for Transformer-based Glacier Segmentation

Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography

Alzheimer’s Disease and Depression: A Bias Analysis and Machine Learning Investigation

Alzheimer’s disease is one of the most common neurodegenerative disorders that greatly impact individual and societal levels. These patients not only suffer from dementia but also from depression which can lead to more decline in cognitive abilities. However, both AD and depression have some common symptoms that make the detection of depression in Alzheimer’s extremely challenging. But several studies have used subsets of the DementiaBank database and employed different audio embeddings to detect depressive AD patients. Nevertheless, such embeddings can be biased for non-clinical factors.