Automated Characterization of Macroscale Tissue 3D Spatial Material Properties via Robotically-Directed Impedimetric Sensing

Abstract

Here, we report a novel method for automated characterization of bulk tissue 3D spatial properties based on reverse engineering-driven non-planar tool path planning and robotically-directed sensing. The method incorporates information on object (e.g., tissue) and inspection tool (e.g., sensor) geometry for automated inspection of tissue mechanical and dielectric properties across macroscopic nonplanar domains as large as 44 cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{2}$</tex-math> </inline-formula> . The process avoids the need for manual sensor-tissue integration processes. The impact and the utility of the method were demonstrated by automated mapping of 3D spatial distributions of mechanical and dielectric properties of plant and animal tissues using multiple complementary impedimetric-based sensors of varying types and form factor, including rigid micro-electromechanical systems (MEMS) and flexible multi-functional fibers. Applications to automated characterization of food quality (e.g., type and age) are provided, including 3D spatial mapping of plant and animal tissue mechanical and dielectric property distributions. Ultimately, automated methods for 3D spatial inspection of plant and animal tissue properties are critical to agriculture, food processing, organ transplantation, and biomanufacturing industries. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the need for automating the inspection of soft 3D biological objects. Here, we present a method that generates 3D quality maps of tissue properties using several sensors. The proposed tool path planning program outputs a customized tissue-conforming path for inspection based on the topographical features of the tissue. Hence, this method enables high-throughput, spatially-resolved, minimally-invasive, and reliable inspection of soft 3D biological objects. Applications to inspection of food quality were provided using two impedimetric-based sensors. Practitioners can directly apply the framework to inspection of other tissue properties and soft objects. This work provides an advance in automated methods for inspection, and real-time monitoring of tissue 3D property distributions, which reduces the need for manual tissue handling and requirement for sensor-product interface prior to characterization. This work can be implemented in various biomanufacturing applications and industries, including food safety and quality control, tissue engineering, and organ transplantation, to ensure the quality and safety of macroscopic tissue-engineered medical and food products.