Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior. (November 2022) - Shu, Ziyu; Entezari, Alireza
Highlights: The proposed framework doesn't require a training dataset and is compatible with all differentiable constraints. The proposed framework can be used as a post-processing technique. The proposed framework significantly accelerates the forward and back-projection operation under parallel geometry. The proposed framework shows significant improvement under sparse-view, limited-angle, and low-dose conditions. The proposed framework is more like an MBIR method, and all of its hyperparameters can be adjusted on-demand as it requires no training process. Abstract: Background and objective: Neural network based image reconstruction methods are becoming increasingly popular. However, limited training data and the lack of theoretical guarantees for generalizability raised concerns, especially in biomedical imaging applications. These challenges are known to lead to an unstable reconstruction process that poses significant problems in biomedical image reconstruction. In this paper, we present a new framework that uses untrained generator networks to tackle this challenge, leveraging the structure of deep networks for regularizing solutions based on a technique known as Deep Image Prior (DIP). Methods: To achieve a high reconstruction accuracy, we propose a framework optimizing both the latent vector and the weights of a generator network during the reconstruction process. We also propose the corresponding reconstruction strategies to improve the stability and convergent performance of the proposed framework. Furthermore, instead of calculating forward projection in each iteration, we propose implementing its normal operator as a convolutional kernel under parallel beam geometry, thus greatly accelerating the calculation. Results: Our experiments show that the proposed framework has significant improvements over other state-of-the-art conventional, pre-trained, and untrained methods under sparse-view, limited-angle, and low-dose conditions. Conclusions: Applying to parallel beam X-ray imaging, our framework shows advantages in speed, accuracy, and stability of the reconstruction process. We also show that the proposed framework is compatible with all differentiable regularizations that are commonly used in biomedical image reconstruction literature. Our framework can also be used as a post-processing technique to further improve the reconstruction generated by any other reconstruction methods. Furthermore, the proposed framework requires no training data and can be adjusted on-demand to adapt to different conditions (e.g. noise level, geometry, and imaged object).