Surface reconstruction from multiview projection data employing a microlens array-based optical detector: a simulation study

Abstract. This article proposes a surface reconstruction method from multiview projectional data acquired by means of a rotationally mounted microlens array-based light detector (MLA-D). The technique is adapted for in vivo small animal imaging, specifically for imaging of nude mice, and does not require an additional imaging step (e.g., by means of a secondary structural modality) or additional hardware (e.g., laser-scanning approaches). Any potential point within the field of view (FOV) is evaluated by a proposed photo-consistency measure, utilizing sensor image light information as provided by elemental images (EIs). As the superposition of adjacent EIs yields depth information for any point within the FOV, the three-dimensional surface of the imaged object is estimated by a graph cuts-based method through global energy minimization. The proposed surface reconstruction is evaluated on simulated MLA-D data, incorporating a reconstructed mouse data volume as acquired by x-ray computed tomography. Compared with a previously presented back projection-based surface reconstruction method, the proposed technique yields a significantly lower error rate. Moreover, while the back projection-based method may not be able to resolve concave surfaces, this approach does. Our results further indicate that the proposed method achieves high accuracy at a low number of projections.