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Updated: May 20, 2026

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

System-on-chip design for ultrasonic target detection using split-spectrum processing and neural networks.

Jafar Saniie¹, Erdal Oruklu, Sungjoon Yoon

¹Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA.

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

|July 26, 2012

Summary

This study introduces a novel neural network (NN) combined with split-spectrum processing (SSP) to enhance ultrasonic target detection in noisy environments. The system significantly improves target visibility and outperforms traditional methods.

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Area of Science:

Ultrasonic imaging
Signal processing
Artificial intelligence

Background:

Ultrasonic detection is challenged by scattering noise.
Target echo visibility enhancement is crucial for accurate characterization.

Purpose of the Study:

To develop and evaluate a neural network (NN) coupled with split-spectrum processing (SSP) for improved target detection.
To enhance target echo visibility in challenging, noise-dominated ultrasonic environments.

Main Methods:

Experimental measurements with signal-to-noise ratio (SNR) around 0 dB.
Integration of a trainable neural network with split-spectrum processing.
Development of a field-programmable gate array (FPGA)-based hardware platform for real-time system-on-chip (SoC) realization.

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Wideband Optical Detector of Ultrasound for Medical Imaging Applications

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Published on: May 11, 2014

Evaluating Targeting Accuracy in the Focal Plane for an Ultrasound-guided High-intensity Focused Ultrasound Phased-array System

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Main Results:

The split-spectrum processing-neural network (SSP-NN) system achieved an average target-to-clutter ratio improvement of 40 dB.
The proposed system demonstrated superior robustness compared to conventional detection techniques.
High-speed operation and computational throughput were achieved through hardware/software co-design on an FPGA platform.

Conclusions:

The SSP-NN system offers a significant advancement in ultrasonic target detection and characterization.
The developed FPGA platform enables real-time implementation for practical imaging applications.
This approach provides a robust and effective solution for overcoming noise limitations in ultrasonic sensing.