What technique reduces unnecessary 3D data to a stack of independent 2D sinograms for reconstruction?

Fourier Re-binning is a technique that converts 3D data acquired during a PET scan into a series of 2D sinograms, making the data more manageable for reconstruction processes. This method leverages the mathematical principles of Fourier transforms to re-arrange the projection data, allowing for the reconstruction algorithms to efficiently utilize the data in a 2D format. By transforming 3D projections into 2D sinograms, Fourier Re-binning effectively simplifies the reconstruction process while maintaining the integrity of the data, facilitating greater computational efficiency and accuracy during image reconstruction.

In contrast, data smoothing, image registration, and image filtering serve different functions. Data smoothing is primarily used to reduce noise in images rather than restructuring the data for reconstruction. Image registration is concerned with aligning different datasets into a common coordinate system, and image filtering aims to enhance or suppress certain image features or noise, rather than reducing dimensionality for reconstruction. Thus, Fourier Re-binning stands out as the focused technique for reducing unnecessary 3D data into independent 2D sinograms for efficient reconstruction in PET imaging.