A GRADUALLY UNMASKING METHOD FOR LIMITED DATA TOMOGRAPHY

In limited data tomography, with applications such as electron microscopy, medical imaging, industrial non-destructive testing, etc., the scanning views are within an angular range that is either limited (i.e., less than the full 180deg) or sparsely sampled. In these situations, standard reconstruction algorithms produce reconstructions with notorious intrinsic artifacts. We propose a novel technique that gradually recovers (or "unmasks") the densities in the image, and whose implementation is based on the algebraic reconstruction techniques (ART). Using our method, we show that the artifacts are thus significantly reduced.