A Data Consistent Variational Segmentation Approach Suitable for Real-time Tomography

Abstract

Computed Tomography (CT) is an imaging technique that allows to reconstruct volumetric information of the analyzed objects from their projections. The most popular reconstruction technique is the Filtered Back Projection (FBP). It has the advantage of being the fastest technique available, but also the disadvantage to require a high number of projections to retrieve good quality reconstructions. In this article we propose a segmentation method for tomographic volumes composed of few materials. Our method combines existing high-quality variational segmentation frameworks with the data consistency approach used in tomography and discrete tomography. We show that our algorithm performs well under high noise level and with moderately low number of projections, and that the data consistency significantly improves the segmentation, at the cost of only one FBP reconstruction and forward projection.

Three-dimensional reconstruction of intragranular strain and orientation in polycrystals by near-field X-ray diffraction

“…In our case, all grain voxels are allowed to change for the next set of I real iterations, so that the grain boundaries do not need to be determined, which allows for a more simple and coherent implementation, although potentially less efficient. The binary threshold for determining the grain shape from the real-valued SIRT reconstruction is found iteratively similar to the approach described in (Der Sarkissian et al, 2018)[71]. A lower threshold results in a larger binary grain volume.…”