High performance tomosynthesis enabled via a GPU-based iterative reconstruction framework

Digital tomosynthesis (DTS) often suffers from slow reconstruction speed due to the high complexity of the computation, particularly when iterative methods are employed. To fulfill clinical performance constraints, graphics cards (GPUs) were investigated and proved to be an efficient platform for accelerating tomographic reconstruction. However, hardware programming constraints often led to complicated memory management and resulted in reduced accuracy or compromised performance. In this paper we proposed a new GPU-based reconstruction framework targeting on tomosynthesis applications. Our framework benefits from latest GPU functionalities and improves the design from previous applications. A high-quality ray-driven forward projection help simplify the data flow when arbitrary acquisition matrices are provided. Our results show that a near-interactive reconstruction speed is achieved with the new framework at no loss of accuracy.