Multi-slice computed tomography (MCT) is one of the most recent technology advancements in CT. Because of the large number of detector rows placed along the patient long axis, MCT offers improved volume coverage, better contrast utilization, and increased spatial resolution. Despite many of its advantages, MCT brings new challenges to the image reconstruction. In general, the challenges of multi-slice CT reconstruction can be classified into two categories: the effects of cone beam and helical interpolation. The cone beam effect comes from the fact that the scanning planes for the outer detector rows are no longer parallel to the plane-of-reconstruction. The helical interpolation effect is mainly due to the inherent projection data inconsistency as a result of the constant table motion. In this paper, we present an algorithmic approach to address both issues. We first propose a weighted hyper-plane reconstruction (WHR) to overcome the sharp transitions in the helical weight. Using WHR, a weighting function is derived based on the scaled average of a set of weights obtained from multiple reconstruction hyper-planes. This approach effectively enables the derivation of a smooth weighting function from any non-smooth functions. We then present a cone-angle dependent weighting (CADW) scheme to overcome the cone beam effects of the outer detector rows. Extensive phantom and clinical experiments have shown that the proposed algorithm is superior to the previously proposed weighting approaches, in terms of reduction of image artifacts and preservation of slice thickness. At the same time, the algorithm utilizes only fan beam back-projections, instead of the computationally more intensive cone beam back-projection.