Photoacoustic tomography system for roughly painted micro objects

Abstract

Subsurface imaging is challenging; it is difficult to detect objects visually. In this research, a novel non-contact photoacoustic (PA) imaging system was developed to detect subsurface objects. The Rosencwaig-Gersho (RG) model was successfully employed to capture microobject images covered by rough paint. The experiments were conducted using a copper ring with a 1-mm diameter fully coated by rough paint with an average thickness of 2.3 μm. The resulting PA images exhibited up to 72% consistency despite the rough paint; the shapes of the objects were clearly recognized before and after coating. To conduct the experiment, simulations and image acquisitions were arranged. Then, the system capability to produce tomographic images was improved by adjusting the thermal diffusion lengths, and subsurface object images were successfully acquired at depths of 2.0, 2.6, 9.8, and 52 μm. The detailed composition of image slices displayed the structure profile of subsurface objects appropriately.