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Applied Sciences
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Novel Photoacoustic Imaging Technologies (Advanced Technological Developments, Imaging Systems, Reconstruction, and Applications)

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (23 November 2020) | Viewed by 2920

Special Issue Editor

Dr. Mohammad Mehrmohammadi

E-Mail Website
Guest Editor
Assistant Professor, Wayne State University Barbara Ann Karmanos Cancer Institute, Detroit, MI 48201, USA
Interests: molecular imaging and therapy; nano-biotechnology; photoacoustic imaging; elasticity imaging; medical devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue titled “Novel Photoacoustic Imaging Technologies”, to be published by the journal Applied Sciences.

Photoacoustic imaging, also referred to as optoacoustic imaging, is an emerging medical diagnostic technolgy that has had steady growth over the past two decades. The notable advantages of photoacoustic (PA) imaging include being non-ionizing, safe, having a high spatial and temporal resolution, and the capability to be integrated with widely used ultrasound imaging, making this relatively new imaging modality a suitable candidate for a large number of clinical applications. Over past decade, significant efforts have been directed toward refinements and improvements of PA imaging systems (both hardware and software), as well as for finding new pre-clinical and clinical utilities for this modality. Given the nature of photoacoustics, reserachers with expertise in optics, acoustics, system development, and image reconstruction and analysis have been involved in the achieved advances.

This Special Issue provides an opportunity to gather a broad range of research, including novel PA imaging systems (microscopic, tomographic, and planar imaging systems), newly developed image formation, and image reconstruction algorithms, and innovative applications, both in the pre-clinical and clinical domains. In addition, PA imaging contrast agents fit well within the scope of this Special Issue. We invite researchers and investigators to submit original research as well as mini-reviews to this Special Issue.

Dr. Mohammad Mehrmohammadi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Photoacoustic imaging
  • Photoacoustic microscopy
  • Photoacoustic tomography
  • Planar photoacoustic imaging
  • Optocacoustic
  • Photoacoustic image reconstruction
  • Photoacoustic image analysis
  • Contrast agents
  • Pre-clinical photoacoustic
  • Clinical photoacoustic
  • Photoaccoustic-guided interventions

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Published Papers (1 paper)

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Research

18 pages, 20732 KiB  
Article
Improved Photoacoustic Imaging of Numerical Bone Model Based on Attention Block U-Net Deep Learning Network
by Panpan Chen, Chengcheng Liu, Ting Feng, Yong Li and Dean Ta
Appl. Sci. 2020, 10(22), 8089; https://doi.org/10.3390/app10228089 - 15 Nov 2020
Cited by 5 | Viewed by 2652
Abstract
Photoacoustic (PA) imaging can provide both chemical and micro-architectural information for biological tissues. However, photoacoustic imaging for bone tissue remains a challenging topic due to complicated ultrasonic propagations in the porous bone. In this paper, we proposed a post-processing method based on the [...] Read more.
Photoacoustic (PA) imaging can provide both chemical and micro-architectural information for biological tissues. However, photoacoustic imaging for bone tissue remains a challenging topic due to complicated ultrasonic propagations in the porous bone. In this paper, we proposed a post-processing method based on the convolution neural network (CNN) to improve the image quality of PA bone imaging in a numerical model. To be more adaptive for imaging bone samples with complex structure, an attention block U-net (AB-U-Net) network was designed from the standard U-net by integrating the attention blocks in the feature extraction part. The k-wave toolbox was used for the simulation of photoacoustic wave fields, and then the direct reconstruction algorithm—time reversal was adopted for generating a dataset of deep learning. The performance of the proposed AB-U-Net network on the reconstruction of photoacoustic bone imaging was analyzed. The results show that the AB-U-Net based deep learning method can obtain the image presented as a clear bone micro-structure. Compared with the traditional photoacoustic reconstruction method, the AB-U-Net-based reconstruction algorithm can achieve better performance, which greatly improves image quality on test set with peak signal to noise ratio (PSNR) and structural similarity increased (SSIM) by 3.83 dB and 0.17, respectively. The deep learning method holds great potential in enhancing PA imaging technology for bone disease detection. Full article
(This article belongs to the Special Issue Novel Photoacoustic Imaging Technologies (Advanced Technological Developments, Imaging Systems, Reconstruction, and Applications))
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