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Iterative methods are popular choices in image reconstruction fields due to their capability of recovering object information from incomplete acquisition data. However, the computation process involves frequent uses of forward and backward projections that are computationally expensive. Past research has proved that a forward projector that can produce high quality images is crucial to achieve a good convergence rate. In this paper a high performance iterative reconstruction framework is introduced, where two most popular iterative algorithms: Simultaneous Algebraic Reconstruction Technique (SART) and Ordered-subsets Expectation Maximization (OSEM) are supported. The framework utilizes Siddon's ray-driven method to generate forward projected images. Benefited from functionalities offered by current generation of graphics processing units (GPUs), it achieves better performance when compared to previous GPU implementations that use grid-interpolated methods, on top of the significant speedups over CPU-based solutions.