Interpolation vs Interpolation Filter? Hi guys, I've been reading some papers on - how to remove ghosting artefacts from the Fourier Slice theorem applied to a 3D discrete image volume. The papers mention that in order to remove ghosting artefacts "employ a better(and typically wider) interpolation filter".

Currently, in Matlab I fft a 3D image space volume to Fourier space. I then extract a 2D plane at an arbitrary angle from this 3D frequency domain volume, making sure that my 2D plane passes through the centre of the volume. Since, this 2D plane is at an angle (not all points on the plane correspond to the uniform discrete values in the volume), I the use interpolation on the 3D volume in Fourier space, which I believe uses weighted linear interpolation (8 nearest point average). 

What do these papers mean when they say - apply an interpolation filter, is this any different from what I am doing? If it is different, should I be multiplying this interpolation filter with the frequency spectrum and if so what does this filter look like?
Interpolation is the process of adding zeros in between your samples and then low-pass filtering to get rid of the aliases that result.  Linear interpolation is just one out of many different possible filters.  Linear interpolation filters can always be represented by FIR filters.  In one-dimension the interpolation by two linear interpolation filter is $[\frac{1}{2}, 1, \frac{1}{2}]$.  There are better low-pass filters.

It is not clear from your quote what they meant by a "wider" filter.  I'm guessing they mean "one with more taps", but it could also mean "one with a wider passband".