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.