Ultrasparse Ultrasonic Synthetic Aperture Focus Imaging by Passive Sensing.

Area of Science:

• Nondestructive Testing (NDT)
• Ultrasonic Imaging
• Signal Processing

Background:

• Sparse ultrasonic array transmissions in medical imaging and NDT accelerate testing and simplify hardware.
• Obtaining high image quality with reduced transmitter-receiver combinations in sparse arrays remains a challenge.
• Existing methods struggle to balance imaging speed with image fidelity in sparse SAFT.

Purpose of the Study:

• To propose and validate an ultrasparse synthetic aperture focus technique (SAFT) for efficient ultrasonic imaging.
• To demonstrate a method for obtaining full-matrix capture (FMC) data using a minimal number of transmitting elements (1-4).
• To maintain high image quality and imaging speed in passive ultrasonic sensing.

Main Methods:

• Implementation of an ultrasparse SAFT method utilizing a minimum number of transmitter elements.
• Generation of a 'virtual' FMC dataset from normalized cross-power spectra (NCPS) in passive ultrasonic sensing.
• Application of segment-averaged NCPS for robust passive reconstruction of the ultrasonic impulse response function (IRF), incorporating both causal and acausal portions, and compounding multiple wave modes for beamforming.

Main Results:

• Successful reconstruction of virtual FMC data using ultrasparse transmissions (1-4 elements).
• Demonstration of high-quality imaging of drilled holes in an aluminum block using the proposed method.
• Elimination of the near-field blind zone, enabling potential near-field imaging applications.

Conclusions:

• The proposed ultrasparse SAFT method effectively generates high-quality ultrasonic images with significantly reduced data acquisition requirements.
• Passive ultrasonic sensing combined with NCPS-based IRF reconstruction offers a robust approach for sparse array imaging.
• This technique enhances imaging speed and hardware simplicity without compromising image quality, paving the way for improved NDT and medical imaging.