Open Access
26 September 2024 Image reconstruction from photoacoustic projections
Chao Tian, Kang Shen, Wende Dong, Fei Gao, Kun Wang, Jiao Li, Songde Liu, Ting Feng, Chengbo Liu, Changhui Li, Meng Yang, Sheng Wang, Jie Tian
Author Affiliations +
Abstract

Photoacoustic computed tomography (PACT) is a rapidly developing biomedical imaging modality and has attracted substantial attention in recent years. Image reconstruction from photoacoustic projections plays a critical role in image formation in PACT. Here we review six major classes of image reconstruction approaches developed in the past three decades, including delay and sum, filtered back projection, series expansion, time reversal, iterative reconstruction, and deep-learning-based reconstruction. The principal ideas and implementations of the algorithms are summarized, and their reconstruction performances under different imaging scenarios are compared. Major challenges, future directions, and perspectives for the development of image reconstruction algorithms in PACT are also discussed. This review provides a self-contained reference guide for beginners and specialists in the photoacoustic community, to facilitate the development and application of novel photoacoustic image reconstruction algorithms.

CC BY: © The Authors. Published by CLP and SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Chao Tian, Kang Shen, Wende Dong, Fei Gao, Kun Wang, Jiao Li, Songde Liu, Ting Feng, Chengbo Liu, Changhui Li, Meng Yang, Sheng Wang, and Jie Tian "Image reconstruction from photoacoustic projections," Photonics Insights 3(3), R06 (26 September 2024). https://doi.org/10.3788/PI.2024.R06
Received: 8 July 2024; Accepted: 28 August 2024; Published: 26 September 2024
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KEYWORDS
Image restoration

Reconstruction algorithms

Photoacoustic spectroscopy

Sensors

Photoacoustic tomography

Detection and tracking algorithms

Signal detection

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