Short-Dipole Sensor Response Linearization Through Physics-Informed Neural Networks.

Short-dipole diode sensors loaded with highly resistive lines are commonly used to measure the time-averaged square of the high-frequency electromagnetic field amplitude directly. Their precision, simplicity, broadband, high dynamic range capability, and minimal scattering make them ideal for application in the near-field of sources, particularly for demonstrating compliance with exposure limits. However, the usage of these sensors to cover multiple orders of magnitude of field amplitude requires signal-specific linearization of the sensor response.

Learning the Imaging Model of Speed-of-Sound Reconstruction via a Convolutional Formulation.

Speed-of-sound (SoS) is an emerging ultrasound contrast modality, where pulse-echo techniques using conventional transducers offer multiple benefits. For estimating tissue SoS distributions, spatial domain reconstruction from relative speckle shifts between different beamforming sequences is a promising approach. This operates based on a forward model that relates the sought local values of SoS to observed speckle shifts, for which the associated image reconstruction inverse problem is solved.