Combining band-frequency separation and deep neural networks for optoacoustic imaging

TL;DR

This paper introduces a deep neural network architecture for optoacoustic image reconstruction that explicitly utilizes band-frequency information, combining filtered back-projection and UNet, with a novel loss function for improved accuracy and efficiency.

Contribution

The paper presents a new neural network model that integrates frequency band separation with a combined loss function, enhancing optoacoustic image reconstruction performance.

Findings

High-quality image reconstruction demonstrated on numerical experiments

Model generalizes well across different datasets and metrics

Achieves real-time computational performance in testing phase

Abstract

In this paper we consider the problem of image reconstruction in optoacoustic tomography. In particular, we devise a deep neural architecture that can explicitly take into account the band-frequency information contained in the sinogram. This is accomplished by two means. First, we jointly use a linear filtered back-projection method and a fully dense UNet for the generation of the images corresponding to each one of the frequency bands considered in the separation. Secondly, in order to train the model, we introduce a special loss function consisting of three terms: (i) a separating frequency bands term; (ii) a sinogram-based consistency term and (iii) a term that directly measures the quality of image reconstruction and which takes advantage of the presence of ground-truth images present in training dataset. Numerical experiments show that the proposed model, which can be easily trainable by standard optimization methods, presents an excellent generalization performance quantified by a number of metrics commonly used in practice. Also, in the testing phase, our solution has a comparable (in some cases lower) computational complexity, which is a desirable feature for real-time implementation of optoacoustic imaging.