Towards Fast Region Adaptive Ultrasound Beamformer for Plane Wave Imaging Using Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based ultrasound beamformer that enhances image quality by leveraging spatial information, achieving higher resolution and contrast, and enabling faster imaging with fewer angles.

Contribution

A novel CNN architecture for ultrasound beamforming that improves image quality and region adaptiveness over traditional methods, reducing the number of angles needed.

Findings

6 dB improvement in CNR over baseline

Enhanced resolution and contrast in images

Potential for higher frame rates with fewer angles

Abstract

Automatic learning algorithms for improving the image quality of diagnostic B-mode ultrasound (US) images have been gaining popularity in the recent past. In this work, a novel convolutional neural network (CNN) is trained using time of flight corrected in-vivo receiver data of plane wave transmit to produce corresponding high-quality minimum variance distortion less response (MVDR) beamformed image. A comprehensive performance comparison in terms of qualitative and quantitative measures for fully connected neural network (FCNN), the proposed CNN architecture, MVDR and Delay and Sum (DAS) using the dataset from Plane wave Imaging Challenge in Ultrasound (PICMUS) is also reported in this work. The CNN architecture could leverage the spatial information and will be more region adaptive during the beamforming process. This is evident from the improvement seen over the baseline FCNN approach and conventional MVDR beamformer, both in resolution and contrast with an improvement of 6 dB in CNR using only zero-angle transmission over the baseline. With the observed reduction in the requirement of number of angles to produce similar image metrics would prove advantageous in providing a possibility for higher frame rates.