High-Frequency Ultrasound Combined with Deep Learning Enables Identification and Size Estimation of Microplastics

Particles are immobilized within an agar gel in a Petri dish. The transducer performs raster scanning in the X–Y plane to acquire backscattered signals. These measurements are organized into a 3D tensor, with X–Y representing spatial dimensions and Z corresponding to the temporal axis.

High-frequency mechanical waves in the MHz range are transmitted into the medium, where the incident wave interacts with particles and generates scattered waves. A portion of this scattered energy returns to the transducer as backscatter. The characteristics of the backscattered signal are governed by factors such as the acoustic impedance mismatch between the particle and surrounding medium, the particle size relative to the wavelength, and the excitation frequency. These signal patterns are subsequently analyzed using machine learning models to extract discriminative features for particle identification and characterization.