Generalized Backpropagation Algorithms for Diffraction Tomography

Abstract

Filtered backpropagation (FBPP) is a well-known technique used for Diffraction Tomography (DT). For accurate reconstruction of a complex image using FBPP, full $360^{\circ}$ angular coverage is necessary. However, it has been shown that using some inherent redundancies in projection data in a tomographic setup, accurate reconstruction is still possible with $270^{\circ}$ coverage which is called the minimal-scan angle range. This can be done by applying weighing functions (or filters) on projection data of the object to eliminate the redundancies and accurately reconstruct the image from this lower angular coverage. This paper demonstrates procedures to generate many general classes of these weighing filters. These are all equivalent at $270^{\circ}$ coverage but would perform differently at lower angular coverages and under presence of noise. This paper does a comparative analysis of different filters when angular coverage is lower than minimal-scan angle of $270^{\circ}$. Simulation studies have been done to find optimum weight filters for sub-minimal angular coverage. The optimum weights can generate images comparable to a full $360^{\circ}$ coverage FBPP reconstruction. Performance of these in presence of noise is also analyzed. These algorithms are capable of reconstructing almost distortionless complex images even at angular coverages of $200^{\circ}$.