A direct filtered-backprojection(FBP) reconstruction algorithm is presented for circular cone-beam computed tomography(CB-CT) that allows the filter operation to be applied efficiently with shift-variant band-pass characteristics on the kernel function.Our algorithm is derived from the ramp-filter based FBP method of Feldkamp et al.and obtained by decomposing the ramp filtering into a convolution involving the Hilbert kernel(global operation) and a subsequent differentiation operation(local operation).The differentiation is implemented as a finite difference of two(Hilbert filtered) data samples and carried out as part of the backprojection step.The spacing between the two samples,which defines the low-pass characteristics of the filter operation,can thus be selected individually for each point in the image volume.We here define the sample spacing to follow the magnification of the divergent-beam geometry and thus obtain a novel,depth-dependent filtering algorithm for circular CB-CT.We evaluate this resulting algorithm using computer-simulated CB data and demonstrate that our algorithm yields results where spatial resolution and image noise are distributed much more uniformly over the field-of-view,compared to Feldkamp’s approach. A direct filtered-backprojection (FBP) reconstruction algorithm is presented for circular cone-beam computed tomography (CB-CT) that allows the filter operation to be applied efficiently with shift-variant band-pass characteristics on the kernel function. Our algorithm is derived from the ramp-filter based FBP method of Feldkamp et al. and obtained by decomposing the ramp filtering into a convolution involving the Hilbert kernel (global operation) and a subsequent differentiation operation (local operation). The differentiation is implemented as a finite difference of of two (Hilbert filtered) data samples and carried out as part of the backprojection step. The spacing between the two samples, which defines the low-pass characteristics of the filter operation, can thus be selected individually for each point in the image volume. here define the sample spacing to follow the magnification of the divergent-beam geometry and thus obtain a novel, depth-dependent filtering algorithm for circular CB-CT.We evaluate this resulting algorithm using computer-simulated CB data and demonstrate that our algorithm yields results where spatial resolution and image noise are distributed much more uniform than the field-of-view, compared to Feldkamp’s approach.