Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications

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.

References in corpus (13)

• Deep Learning in Neural Networks: An Overview
• On instabilities of deep learning in image reconstruction - Does AI come at a cost?
• Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal
• Model based learning for accelerated, limited-view 3D photoacoustic tomography
• Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning
• Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues
• Deep Learning in Photoacoustic Tomography: Current approaches and future directions
• A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography
• Deep image prior for undersampling high-speed photoacoustic microscopy
• Context encoding enables machine learning-based quantitative photoacoustics
• Photoacoustic image reconstruction via deep learning
• A method for delineation of bone surfaces in photoacoustic computed tomography of the finger
• Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach