Mitigating Object Prior-Bias from Sparse-Projection Tomographic Reconstructions

Tomographic reconstruction from undersampled measurements is a necessity when the measurement process is potentially harmful, needs to be rapid, or is resource-expensive. In such cases, information from previously existing longitudinal scans of the same object ('object-prior') helps in the reconstruction of the current object ('test') from its significantly fewer measurements. A common problem with these techniques is the influence of object-priors in the reconstruction of new changes in the test. In this work, we mitigate this problem by first estimating the location of changes ('new regions') and then imposing object-prior in only those regions which are similar to the prior ('old regions'). Our work is based on longitudinal data acquisition scenarios where we wish to study new changes that evolve within an object over time, such as in repeated scanning for disease monitoring, or in tomography-guided surgical procedures. While reconstruction is easily feasible when measurements are acquired from a large number of projection angles ('views'), it is challenging when the number of views is limited (sub-Nyquist). We show that in the latter case, a 'spatially-varying' technique is appropriate in order to prevent the prior from adversely affecting the reconstruction of new structures that are absent in any of the earlier scans. The reconstruction of new regions is safeguarded from the bias of the prior by computing regional weights that moderate the local influence of the priors. We are thus able to effectively reconstruct both the old and the new structures in the test. We have tested the efficacy of our method on synthetic as well as real projection data, in both 2D and 3D. Our technique significantly improves the overall quality of reconstructions while minimizing the number of measurements needed for imaging in longitudinal studies.