Microwave Breast Imaging via Neural Networks for Almost Real-time Applications

TL;DR

This paper introduces a neural network-based microwave breast imaging method that offers rapid, safe, and cost-effective imaging, overcoming traditional techniques' limitations in speed and computational demands.

Contribution

The paper presents a novel neural network approach trained on realistic breast phantoms to enable almost real-time microwave breast imaging.

Findings

High recovery accuracy demonstrated in simulations

Significant reduction in computational time

Potential for clinical application due to safety and speed

Abstract

Conventional breast cancer imaging techniques are nowadays based on the use of ionising radiations or ultrasound waves for the inspection of breast areas. Nevertheless, these conventional techniques present some drawbacks related to patient safety, processing time and resolution issues. In this framework, microwave imaging can represent a valid alternative or a complementary technique compared to other conventional medical imaging modalities since it is safe (using non-ionising radiations), relatively cheap and more comfortable from patient point of view. Unfortunately, it is slow and computationally expensive, which strongly limit its use in clinical scenarios. In this paper, an artificial neural network for effective and almost real-time breast imaging is proposed. First, a realistic breast-like phantom generator was developed for training the network. Subsequently, numerical analyses have been conducted for the optimisation and the performance evaluation of the approach. The results seem very promising in terms of recovery performance as well as for the computation burden.