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Ultrasound Imaging With a Flexible Probe Based on Element Array Geometry Estimation Using Deep Neural Network.

Takumi Noda, Takashi Azuma, Yutaka Ohtake

IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

|September 28, 2022

Summary

This summary is machine-generated.

Flexible ultrasound probes offer advantages for long-term monitoring. This study introduces a deep neural network (DNN) method to accurately estimate probe geometry from radio frequency (RF) data, improving image quality for real-time applications.

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Area of Science:

Medical Imaging
Biomedical Engineering
Artificial Intelligence in Healthcare

Background:

Conventional ultrasound (US) diagnosis relies on rigid probes, limiting long-term monitoring due to the need for continuous pressure and air removal.
Flexible probes offer superior body adherence for extended monitoring but suffer from image blurring and distortion caused by their geometry.
Accurate element array geometry is crucial for high-fidelity ultrasound image reconstruction.

Purpose of the Study:

To develop and validate a novel ultrasound imaging method for flexible probes.
To utilize a deep neural network (DNN) for estimating element array geometry from radio frequency (RF) data.
To enable real-time, high-quality ultrasound imaging with flexible probes for long-term monitoring.

Main Methods:

A deep neural network (DNN) was designed to predict element array geometry parameters directly from ultrasound RF data.
The DNN was initially trained using simulated data and subsequently fine-tuned with in vivo ultrasound data.
Performance was assessed by measuring element position mean absolute error (MAE) and evaluating reconstructed image quality using peak-signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM).

Main Results:

The DNN achieved an average element position MAE of 0.86 mm on simulation data and 1.11 mm on in vivo data.
Reconstructed images showed an average PSNR of 20.6 and MSSIM of 0.791 for simulation data, and 19.4 and 0.798 for in vivo data.
The average estimation time for the DNN was remarkably fast at 0.045 seconds, indicating real-time feasibility.

Conclusions:

The proposed DNN-based method effectively estimates flexible ultrasound probe geometry from RF data.
This approach significantly improves the quality of reconstructed ultrasound images, overcoming limitations of flexible probe geometry.
The method demonstrates strong potential for enabling real-time, long-term ultrasound monitoring applications using flexible probes.