Motion Quantification and Automated Correction in Clinical RSOM.

Abstract

Raster-scan optoacoustic mesoscopy (RSOM) offers high-resolution non-invasive insights into skin pathophysiology, which holds promise for disease diagnosis and monitoring in dermatology and other fields. However, RSOM is quite vulnerable to vertical motion of the skin, which can depend on the part of the body being imaged. Motion correction algorithms have already been proposed, but they are not fully automated, they depend on anatomical segmentation pre-processing steps that might not be performed successfully, and they are not site- specific. Here, we determined for the first time the magnitude of the micrometric vertical skin displacements at different sites on the body that affect RSOM. The quantifi- cation of motion allowed us to develop a site-specific correction algorithm. The algorithm is fully automated and does not need prior anatomical information. We found that the magnitude of the vertical motion depends strongly on the site of imaging and is caused by breathing, heart beating, and arterial pulsation. The developed algorithm resulted in more than 2-fold improvement in the signal-to-noise ratio of the reconstructed images at every site tested. Proposing an effective automated motion correction algorithm paves the way for realizing the full clinical potential of RSOM.