Andreas Hauptmann and Ben T. Cox "Deep learning in photoacoustic tomography: current approaches and future directions," Journal of Biomedical Optics 25(11), 112903 (26 October 2020). https://doi.org/10.1117/1.JBO.25.11.112903
Deep learning in photoacoustic tomography : current approaches and future directions
|Author:||Hauptmann, Andreas1,2; Cox, Ben3|
1University of Oulu, Research Unit of Mathematical Sciences, Oulu, Finland
2University College London, Department of Computer Science, London, United Kingdom
3University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
|Online Access:||PDF Full Text (PDF, 3.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020103088839
|Publish Date:|| 2020-10-30
Biomedical photoacoustic tomography, which can provide high-resolution 3D soft tissue images based on optical absorption, has advanced to the stage at which translation from the laboratory to clinical settings is becoming possible. The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges. There are many classical approaches to image reconstruction, but ameliorating the effects of incomplete or imperfect data through the incorporation of accurate priors is challenging and leads to slow algorithms. Recently, the application of deep learning (DL), or deep neural networks, to this problem has received a great deal of attention. We review the literature on learned image reconstruction, summarizing the current trends and explain how these approaches fit within, and to some extent have arisen from, a framework that encompasses classical reconstruction methods. In particular, it shows how these techniques can be understood from a Bayesian perspective, providing useful insights. We also provide a concise tutorial demonstration of three prototypical approaches to learned image reconstruction. The code and data sets for these demonstrations are available to researchers. It is anticipated that it is in in vivo applications—where data may be sparse, fast imaging critical, and priors difficult to construct by hand—that DL will have the most impact. With this in mind, we conclude with some indications of possible future research directions.
Journal of biomedical optics
|Type of Publication:||
A2 Review article in a scientific journal
|Field of Science:||
113 Computer and information sciences
114 Physical sciences
217 Medical engineering
This work was partly funded by the European Union’s Horizon 2020 Research and Innovation Program H2020 ICT 2016-2017 under Grant Agreement No. 732411, which is an initiative of the Photonics Public Private Partnership, and partly by the Academy of Finland Project 312123 (Finnish Centre of Excellence in Inverse Modeling and Imaging, 2018–2025) and the CMIC-EPSRC platform grant (No. EP/M020533/1).
|Academy of Finland Grant Number:||
312123 (Academy of Finland Funding decision)
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