Towards practical implementation of the compressed sensing framework for multi-element synthetic transmit aperture...

Area of Science:

• Medical Imaging
• Ultrasound Technology
• Signal Processing

Background:

• Synthetic aperture (SA) ultrasound imaging uses compressed sensing (CS) to enhance frame rates.
• Previous CS frameworks for multi-element synthetic transmit aperture (MSTA) imaging required random receive element selection per transmission, posing implementation challenges.
• Modifying schemes to use a fixed set of receive elements degraded image quality.

Purpose of the Study:

• To develop a novel, optimized sampling scheme for CS in MSTA imaging.
• To address the practical implementation challenges of random receive element selection.
• To improve image quality and reduce recovery error in CS-based MSTA systems.

Main Methods:

• Proposed a novel sampling scheme utilizing a genetic algorithm (GA) to optimally select a fixed set of receive element positions for all MSTA transmissions.
• Evaluated the CS performance of the GA-based sampling scheme against a Gaussian under-sampling framework.
• Tested the methods on both in-vitro and in-vivo ultrasound datasets.

Main Results:

• The GA-based approach enables the use of the same sparse receive elements for each transmission, simplifying implementation.
• Achieved the lowest normalized root mean square error (NRMSE) in CS recovery compared to the Gaussian scheme.
• Demonstrated a 14% overall improvement in image contrast.

Conclusions:

• The GA-based sampling scheme offers a practical and effective solution for CS in MSTA ultrasound imaging.
• This method significantly reduces implementation complexity while enhancing image quality.
• The GA-CS framework presents a promising advancement for high-frame-rate ultrasound systems.