Sounding out the hidden data: A concise review of deep learning in photoacoustic imaging.

Journal Article (Review;Journal Article)

The rapidly evolving field of photoacoustic tomography utilizes endogenous chromophores to extract both functional and structural information from deep within tissues. It is this power to perform precise quantitative measurements in vivo- with endogenous or exogenous contrast- that makes photoacoustic tomography highly promising for clinical translation in functional brain imaging, early cancer detection, real-time surgical guidance, and the visualization of dynamic drug responses. Considering photoacoustic tomography has benefited from numerous engineering innovations, it is of no surprise that many of photoacoustic tomography's current cutting-edge developments incorporate advances from the equally novel field of artificial intelligence. More specifically, alongside the growth and prevalence of graphical processing unit capabilities within recent years has emerged an offshoot of artificial intelligence known as deep learning. Rooted in the solid foundation of signal processing, deep learning typically utilizes a method of optimization known as gradient descent to minimize a loss function and update model parameters. There are already a number of innovative efforts in photoacoustic tomography utilizing deep learning techniques for a variety of purposes, including resolution enhancement, reconstruction artifact removal, undersampling correction, and improved quantification. Most of these efforts have proven to be highly promising in addressing long-standing technical obstacles where traditional solutions either completely fail or make only incremental progress. This concise review focuses on the history of applied artificial intelligence in photoacoustic tomography, presents recent advances at this multifaceted intersection of fields, and outlines the most exciting advances that will likely propagate into promising future innovations.

Full Text

Duke Authors

Cited Authors

  • DiSpirito, A; Vu, T; Pramanik, M; Yao, J

Published Date

  • June 2021

Published In

Volume / Issue

  • 246 / 12

Start / End Page

  • 1355 - 1367

PubMed ID

  • 33779342

Pubmed Central ID

  • PMC8243210

Electronic International Standard Serial Number (EISSN)

  • 1535-3699

International Standard Serial Number (ISSN)

  • 1535-3702

Digital Object Identifier (DOI)

  • 10.1177/15353702211000310

Language

  • eng