Evaluation of shape recognition abilities for a micro-lens array based optical detector by a dedicated simulation framework

A micro-lens array based optical detector (MLA-D) has been developed for preclinical in vivo optical imaging applications. While primarily intended for detecting signals from molecular optical probes within living subjects (mice), the MLA-D also can be used effectively to capture the surface of the imaged object in three dimensions from only a few projection angles a feature that is very important for in vivo optical imaging. In order to study the shape recognition ability of the MLA-D design we have developed a ray-tracing simulation framework. The impact of the following physical MLA-D parameters on surface recognition efficiency can be studied: micro-lens diameter, micro-lens focal length, and sensor pixel size. By using this framework the performance of two surface recognition algorithms the optical flow method and the multi-projection surface reconstruction (back-projection) method has been assessed within the specific context of preclinical imaging application. By way of example, the commonly used DigiMouse dataset is adopted to generate simulated raw image data. Results of the simulation framework conform well with the depth-of-field theory, and both surface recognition methods yield comparable, but unsatisfactory results. Whereas the optical flow method reveals the relative shape of the phantom at a comparatively lesser spatial and depth resolution, the back-projection method, while providing higher resolution data, could not resolve concave regions in all cases which needs further investigation. Very promising preliminary results have been attained, however, with the multi-view stereo algorithm that has been implemented most recently.