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Signal processing with unequally spaced data in Fourier-domain optical coherence tomography

Different algorithms for performing Fourier transforms with unequally sampled data in wavenumber space for Fourier-domain optical coherence tomography are considered. The efficiency of these algorithms is evaluated from point-spread functions obtained with a swept-source optical coherence tomography system and from computational time. Images of a 4-layer phantom processed with these different algorithms are compared. We show that convolving the data with an optimized Kaiser-Bessel window allowing a small oversampling factor before computing the fast Fourier transform provides the optimal trade-off between image quality and computational time.