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Photoacoustic Tomography with a Ring Ultrasound Transducer: A Comparison of Different Illumination Strategies
 
 
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Editorial

Special Issue on Photoacoustic Tomography

1
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48105, USA
2
Department of Mechanical Engineering, University of Kansas, Lawrence, KS 66045, USA
3
Institute for Biological and Medical Imaging, Helmholtz Zentrum Munich & Technical University of Munich, 85748 Garching, Germany
*
Authors to whom correspondence should be addressed.
Submission received: 24 September 2019 / Accepted: 30 September 2019 / Published: 8 October 2019
(This article belongs to the Special Issue Photoacoustic Tomography (PAT))
Biomedical photoacoustic (or optoacoustic) tomography (PAT), or more generally, photoacoustic imaging (PAI), has been an active area of study and development in the last two decades [1,2]. As an emerging hybrid imaging modality that combines the high-contrast of optical imaging with the high spatial resolution of ultrasound imaging, PAI has been demonstrated to have broad applications in areas including basic research, pre-clinical investigation and clinical studies [3]. It offers great specificity with the ability to detect light-absorbing chomophores, such as hemoglobin, melanin, lipids, etc. This ability enables PAI to provide rich soft-tissue information regarding anatomy and function. The application of contrast agents further enables PAI to provide molecular information [4]. Because of these advantages, PAI has great potential in the clinical diagnosis of various diseases, such as cancer, stroke, atherosclerosis, arthritis, etc.
The goal of the current Special Issue is to showcase some of the most recent research and development into this fascinating imaging technology. Indeed, there are so many aspects that an engineer can work on to improve PAI and facilitate its translation into the clinic, from ultrasonic detector design, light delivery and illumination, to reconstruction algorithms. The 11 articles in the current Special Issue represent some of these efforts from research groups all over the world. Alijabbari et al. [5] were able to demonstrate that a full-ring system, not only for the ultrasound transducer, but also for light illumination, can provide superior imaging quality because of the improved illumination configuration. A similar idea was also adopted by Avanaki’s research group [6], as well as by Sun et al. [7]. However, these research groups presented a low-cost system made of multi single-element transducers, rather than an expensive array system that could be cost-prohibited for many users. Improvement on the reconstruction algorithm is another major theme for the current Special Issue. Various methods were presented to improve the quality of PAT images by overcoming artifacts due to respiration [8], reflection [9] and scattering [10]. Novel approaches were proposed to reconstruct PAT images based on full-field detection [11] as well as detections with limited view angles [12]. Based on deconvolution and empirical mode decomposition, Guo et al. was able to improve final image quality by alleviating signal aliasing introduced by N-shape waves [13]. Ma et al. introduced a machine-learning method to PAT image analysis in order to measure the size of adipocytes [14]. Finally, Kothapalli and his research group developed a single simulation platform for both photoacoustic and thermoacoustic imaging [15]. These are truly some of the exciting developments.
PAI is a fast-evolving research and development field. Researchers all over the world are working diligently on continuously improving the technology and translating it to the clinic environment. The current Special Issue, with 11 articles from research groups across different parts of the world, is a very small sample to demonstrate the potential impact of this technology. We hope you will enjoy reading these selected articles and look forward to your future contribution to a future Special Issue.

References

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  2. Xia, J.; Yao, J.; Wang, L.V. Photoacoustic tomography: Principles and advances. Electromagn. Waves 2014, 147, 1–22. [Google Scholar] [CrossRef]
  3. Steinberg, I.; Huland, D.M.; Vermesh, O.; Frostig, H.E.; Tummers, W.S.; Gambhir, S.S. Photoacoustic clinical imaging. Photoacoustics 2019, 14, 77–98. [Google Scholar] [CrossRef] [PubMed]
  4. Zeng, L.; Ma, G.; Lin, J.; Huang, P. Photoacoustic Probes for Molecular Detection: Recent Advances and Perspectives. Small 2018, 14, e1800782. [Google Scholar] [CrossRef]
  5. Alijabbari, N.; Alshahrani, S.S.; Pattyn, A.; Mehrmohammadi, M. Photoacoustic Tomography with a Ring Ultrasound Transducer: A Comparison of Different Illumination Strategies. Appl. Sci. 2019, 9, 3094. [Google Scholar] [CrossRef]
  6. Zafar, M.; Kratkiewicz, K.; Manwar, R.; Avanaki, M. Development of Low-Cost Fast Photoacoustic Computed Tomography: System Characterization and Phantom Study. Appl. Sci. 2019, 9, 374. [Google Scholar] [CrossRef]
  7. Sun, M.J.; Hu, D.P.; Zhou, W.X.; Liu, Y.; Qu, Y.W.; Ma, L.Y. 3D Photoacoustic Tomography System Based on Full-View Illumination and Ultrasound Detection. Appl. Sci. 2019, 9, 1904. [Google Scholar] [CrossRef]
  8. Ron, A.; Davoudi, N.; Dean-Ben, X.L.; Razansky, D. Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography. Appl. Sci. 2019, 9, 2737. [Google Scholar] [CrossRef]
  9. Shan, H.M.; Wang, G.; Yang, Y. Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography. Appl. Sci. 2019, 9, 2615. [Google Scholar] [CrossRef]
  10. Rui, W.; Liu, Z.P.; Tao, C.; Liu, X.J. Reconstruction of Photoacoustic Tomography Inside a Scattering Layer Using a Matrix Filtering Method. Appl. Sci. 2019, 9, 2071. [Google Scholar] [CrossRef]
  11. Zangerl, G.; Haltmeier, M.; Nguyen, L.V.; Nuster, R. Full Field Inversion in Photoacoustic Tomography with Variable Sound Speed. Appl. Sci. 2019, 9, 1563. [Google Scholar] [CrossRef]
  12. Omidi, P.; Zafar, M.; Mozaffarzadeh, M.; Hariri, A.; Haung, X.Z.; Orooji, M.; Nasiriavanaki, M. A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography. Appl. Sci. 2018, 8, 1570. [Google Scholar] [CrossRef]
  13. Guo, C.W.; Chen, Y.N.; Yuan, J.; Zhu, Y.H.; Cheng, Q.; Wang, X.D. Biomedical Photoacoustic Imaging Optimization with Deconvolution and EMD Reconstruction. Appl. Sci. 2018, 8, 2113. [Google Scholar] [CrossRef]
  14. Ma, X.; Cao, M.; Shen, Q.H.; Yuan, J.; Feng, T.; Cheng, Q.; Wang, X.D.; Washabaugh, A.R.; Baker, N.A.; Lumeng, C.N.; et al. Adipocyte Size Evaluation Based on Photoacoustic Spectral Analysis Combined with Deep Learning Method. Appl. Sci. 2018, 8, 2178. [Google Scholar] [CrossRef]
  15. Fadden, C.; Kothapalli, S.R. A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography. Appl. Sci. 2018, 8, 1568. [Google Scholar] [CrossRef] [PubMed]

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MDPI and ACS Style

Wang, X.; Yang, X.; Dean-Ben, X.L. Special Issue on Photoacoustic Tomography. Appl. Sci. 2019, 9, 4186. https://0-doi-org.brum.beds.ac.uk/10.3390/app9194186

AMA Style

Wang X, Yang X, Dean-Ben XL. Special Issue on Photoacoustic Tomography. Applied Sciences. 2019; 9(19):4186. https://0-doi-org.brum.beds.ac.uk/10.3390/app9194186

Chicago/Turabian Style

Wang, Xueding, Xinmai Yang, and Xose Luis Dean-Ben. 2019. "Special Issue on Photoacoustic Tomography" Applied Sciences 9, no. 19: 4186. https://0-doi-org.brum.beds.ac.uk/10.3390/app9194186

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