Few-view tomography using roughness-penalized nonparametric regression and periodic spline interpolation

Abstract

The ability to reconstruct high-quality tomographic images from a smaller number of projections than is usually used could reduce imaging time for many nuclear-medicine studies. This would particularly benefit studies such as cardiac SPECT where patient motion during long acquisitions can lead to motion artifacts in the reconstructed images. To this end, the authors have investigated sinogram pre-processing techniques designed to enable filtered backprojection (FBP) to produce high-quality reconstructions from a small number of views. Each projection is first smoothed by performing roughness-penalized nonparametric regression using a generalized linear model that explicitly accounts for the Poisson statistics of the data. The resulting fit curves are natural cubic splines. After smoothing, additional angular views are generated using periodic spline interpolation, and images are reconstructed using FBP. The algorithm was tested on data from SPECT studies of a cardiac phantom placed at various radial offsets to enable examination of the algorithm's dependence on the radial extent of the object being imaged.