3D FFT's and Convolutions using FFTW
In 1D, you are likely to obtain, more precisely, $f_0, f_1, f_2,\ldots, f_{N/2}, f_{-N/2+1},\ldots, f_{-1}$. In other words, the $N/2+1$ ones correspond to positive frequencies. In higher dimensions, the same applies. In 3D, you ought to get a "top-left-front" sub-cube of positive frequencies.

To reduce periodic artifacts, you could 3D window the data before, and take care of the values of the imaginary parts after IFFTing back. If the input data is real, they should be small, and you can keep the real part only. If not, something gone wrong.