# Weighted Back-Projection Methods

Chapter

## Abstract

Traditionally, three-dimensional reconstruction methods have been classified into two major groups, Fourier reconstruction methods and direct methods (e.g., Crowther et al., 1970; Gilbert, 1972). Fourier methods are defined as algorithms that restore the Fourier transform of the object from the Fourier transforms of the projections and then obtain the real-space distribution of the object by inverse Fourier transformation. Included in this group are also equivalent reconstruction schemes that use expansions of object and projections into orthogonal function systems (e.g., Cormack, 1963, 1964; Smith et al., 1973; Zeitler, Chapter 4). In contrast, direct methods are defined as those that carry out all calculations in real space. These include the convolution back-projection algorithms (Bracewell and Riddle, 1967; Ramachandran and Lakshminarayanan, 1971; Gilbert, 1972) and iterative algorithms (Gordon et al., 1970; Colsher, 1977). Weighted back-projection methods are difficult to classify in this scheme, since they are equivalent to convolution back-projection algorithms, but work on the real-space data as well as the Fourier transform data of either the object or the projections. Both convolution back-projection and weighted back-projection algorithms are based on the same theory as Fourier reconstruction methods, whereas iterative methods normally do not take into account the Fourier relations between object transform and projection transforms. Thus, it seems justified to classify the reconstruction algorithms into three groups: Fourier reconstruction methods, modified back-projection methods, and iterative direct space methods, where the second group includes convolution backprojection as well as weighted back-projection methods.

## Keywords

Weighting Function Inverse Fourier Transform Sinc Function Arbitrary Geometry Tilt Series
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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