Zero-Shot Artifact2Artifact: Self-incentive artifact removal for photoacoustic imaging without any data

TL;DR

This paper introduces ZS-A2A, a zero-shot, self-supervised artifact removal method for 3D photoacoustic imaging that does not require training data and effectively enhances image quality in sparse or angle-limited configurations.

Contribution

The paper presents a novel zero-shot artifact removal approach using a lightweight network and data perturbations, eliminating the need for large datasets or prior artifact knowledge.

Findings

Achieves state-of-the-art zero-shot artifact removal performance.

Significantly improves contrast-to-noise ratio in vivo from 17.48 to 43.46.

Operates efficiently in 8 seconds on in vivo rat liver data.

Abstract

Photoacoustic imaging (PAI) uniquely combines optical contrast with the penetration depth of ultrasound, making it critical for clinical applications. However, the quality of 3D PAI is often degraded due to reconstruction artifacts caused by the sparse and angle-limited configuration of detector arrays. Existing iterative or deep learning-based methods are either time-consuming or require large training datasets, significantly limiting their practical application. Here, we propose Zero-Shot Artifact2Artifact (ZS-A2A), a zero-shot self-supervised artifact removal method based on a super-lightweight network, which leverages the fact that reconstruction artifacts are sensitive to irregularities caused by data loss. By introducing random perturbations to the acquired PA data, it spontaneously generates subset data, which in turn stimulates the network to learn the artifact patterns in the reconstruction results, thus enabling zero-shot artifact removal. This approach requires neither training data nor prior knowledge of the artifacts, and is capable of artifact removal for 3D PAI. For maximum amplitude projection (MAP) images or slice images in 3D PAI acquired with arbitrarily sparse or angle-limited detector arrays, ZS-A2A employs a self-incentive strategy to complete artifact removal and improves the Contrast-to-Noise Ratio (CNR). We validated ZS-A2A in both simulation study and $ in\ vivo $ animal experiments. Results demonstrate that ZS-A2A achieves state-of-the-art (SOTA) performance compared to existing zero-shot methods, and for the $ in\ vivo $ rat liver, ZS-A2A improves CNR from 17.48 to 43.46 in just 8 seconds. The project for ZS-A2A will be available in the following GitHub repository: https://github.com/JaegerCQ/ZS-A2A.