Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue

TL;DR

This paper introduces a deep learning method for removing electrical noise from multispectral optoacoustic tomography signals, significantly improving image contrast and depth resolution for better clinical imaging.

Contribution

The study presents a novel discriminative deep learning approach that learns spatiotemporal correlations in signals and noise, enabling real-time electrical noise removal in MSOT imaging.

Findings

Achieved up to 19% higher blood vessel contrast.

Enhanced spectral contrast at depths over 2 cm.

Validated on synthetic, phantom, and human breast data.

Abstract

Image contrast in multispectral optoacoustic tomography (MSOT) can be severely reduced by electrical noise and interference in the acquired optoacoustic signals. Signal processing techniques have proven insufficient to remove the effects of electrical noise because they typically rely on simplified models and fail to capture complex characteristics of signal and noise. Moreover, they often involve time-consuming processing steps that are unsuited for real-time imaging applications. In this work, we develop and demonstrate a discriminative deep learning (DL) approach to separate electrical noise from optoacoustic signals prior to image reconstruction. The proposed DL algorithm is based on two key features. First, it learns spatiotemporal correlations in both noise and signal by using the entire optoacoustic sinogram as input. Second, it employs training based on a large dataset of experimentally acquired pure noise and synthetic optoacoustic signals. We validated the ability of the trained model to accurately remove electrical noise on synthetic data and on optoacoustic images of a phantom and the human breast. We demonstrate significant enhancements of morphological and spectral optoacoustic images reaching 19% higher blood vessel contrast and localized spectral contrast at depths of more than 2 cm for images acquired in vivo. We discuss how the proposed denoising framework is applicable to clinical multispectral optoacoustic tomography and suitable for real-time operation.