Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications

TL;DR

This paper surveys advances in photoacoustic imaging (PAI) reconstruction and quantitative analysis, focusing on three main implementations—PACT, PAM, and PAE—and the pivotal role of deep learning (DL) in overcoming depth–resolution limits and enabling rapid, artifact-robust imaging. It contrasts conventional analytic and iterative reconstruction with DL-based approaches across preprocessing, direct, postprocessing, and hybrid schemes, highlighting diffusion models, foundation-model adaptation, and self-supervised learning as avenues to improved image quality and speed. The review also covers quantitative PAI (qPAI), detailing optical fluence compensation and spectral unmixing, and discusses multimodal integrations (e.g., PA/US, PA/MRI) and novel excitation sources that expand spectral reach. Collectively, the work identifies DL-driven reconstruction, physics-informed modeling, and multimodal fusion as key levers to bring PAI from preclinical studies toward routine clinical deployment, while acknowledging challenges in data availability, generalization, and uncertainty quantification. The insights lay out future directions toward single-image super-resolution, wearable systems, and closed-loop theranostics that could significantly impact diagnostic accuracy and personalized therapy.

Abstract

Photoacoustic imaging (PAI) represents an innovative biomedical imaging modality that harnesses the advantages of optical resolution and acoustic penetration depth while ensuring enhanced safety. Despite its promising potential across a diverse array of preclinical and clinical applications, the clinical implementation of PAI faces significant challenges, including the trade-off between penetration depth and spatial resolution, as well as the demand for faster imaging speeds. This paper explores the fundamental principles underlying PAI, with a particular emphasis on three primary implementations: photoacoustic computed tomography (PACT), photoacoustic microscopy (PAM), and photoacoustic endoscopy (PAE). We undertake a critical assessment of their respective strengths and practical limitations. Furthermore, recent developments in utilizing conventional or deep learning (DL) methodologies for image reconstruction and artefact mitigation across PACT, PAM, and PAE are outlined, demonstrating considerable potential to enhance image quality and accelerate imaging processes. Furthermore, this paper examines the recent developments in quantitative analysis within PAI, including the quantification of haemoglobin concentration, oxygen saturation, and other physiological parameters within tissues. Finally, our discussion encompasses current trends and future directions in PAI research while emphasizing the transformative impact of deep learning on advancing PAI.

Figures (10)

• Figure 1: Schematic of photoacoustic image generation process 7park2025clinical.
• Figure 2: Bridging the imaging gap: comparison of resolution versus depth in photoacoustic, ultrasound, and optical imaging.
• Figure 3: Three key implementations of PAI.
• Figure 4: Comparative analysis of OR-PAM and AR-PAM modalities.
• Figure 5: Illustrative overview outlining the scope of this review.