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Advances in Ultrasound Imaging and Sensing Technology

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 691

Special Issue Editors


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Guest Editor
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, China
Interests: medical ultrasound imaging; medical robotics; pattern recognition; data mining; computer-aided diagnosis
Special Issues, Collections and Topics in MDPI journals
Associate Professor, School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: photoacoustic imaging technology; photoelectric detection and image engineering; multi-dimensional ultrasonic detection and imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors play a pivotal role in ultrasound imaging and sensing technology. They represent the essential properties of medical images, as well as the properties of solutions that address the issues of image resolution and contrast. In particular, ultrasound imaging and sensing technology are crucial in producing ultrasound images, processing ultrasonic signals, extracting wave features, and intelligently analyzing various types of ultrasound data. In recent years, more advanced progress in fields like artificial intelligence, computer vision, signal processing, and  high-performance computation has emerged, and it will have vast application prospects, being of significant importance in various clinical fields.

Accordingly, the presented Special Issue is devoted to recent advances in ultrasound images and sensing technology. Among the topics of the issue are the following: (1) ultrasound image analysis; (2) multi-dimensional ultrasound image segmentation, registration, and visualization; (3) measurement and analyses of clinical physiological and pathological characteristics based on ultrasound images; (4) ultrasound/photoacoustic signal analysis and processing; (5) diagnostic methods of ultrasound/photoacoustic imaging; (6) advanced ultrasound/photoacoustic imaging in biomedicine; (7) artificial intelligence for ultrasound imaging and sensing technology; (8) any topics related to the concept of ultrasound imaging and sensing technology, i.e., theory, system, and applications.

Prof. Dr. Qinghua Huang
Dr. Haigang Ma
Guest Editors

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. Sensors 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 2600 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

  • ultrasound images
  • ultrasound sensing
  • ultrasound and photoacoustic imaging
  • image analyses
  • image segmentation
  • signal processing

Published Papers (1 paper)

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Research

22 pages, 13875 KiB  
Article
Measured Regional Division Optimization for Acoustic Tomography Velocity Field Reconstruction in a Circular Area
by Yixiao Chen, Xinzhi Zhou, Jialiang Zhu, Chenlong Dong, Tao Xu and Hailin Wang
Sensors 2024, 24(6), 2008; https://doi.org/10.3390/s24062008 - 21 Mar 2024
Viewed by 422
Abstract
The acoustic tomography (AT) velocity field reconstruction technique has become a research hotspot in recent years due to its noninvasive nature, high accuracy, and real-time measurement advantages. However, most of the existing studies are limited to the reconstruction of the velocity field in [...] Read more.
The acoustic tomography (AT) velocity field reconstruction technique has become a research hotspot in recent years due to its noninvasive nature, high accuracy, and real-time measurement advantages. However, most of the existing studies are limited to the reconstruction of the velocity field in a rectangular area, and there are very few studies on a circular area, mainly because the layout of acoustic transducers, selection of acoustic paths, and division of measured regions are more difficult in a circular area than in a rectangular area. Therefore, based on AT and using the reconstruction algorithm of the Markov function and singular value decomposition (MK-SVD), this paper proposes a measured regional division optimization algorithm for velocity field reconstruction in a circular area. First, an acoustic path distribution based on the multipath effect is designed to solve the problem of the limited emission angle of the acoustic transducer. On this basis, this paper proposes an adaptive optimization algorithm for measurement area division based on multiple sub-objectives. The steps are as follows: first, two optimization objectives, the condition number of coefficient matrix and the uniformity of acoustic path distribution, were designed. Then, the weights of each sub-objective are calculated using the coefficient of variation (CV). Finally, the measured regional division is optimized based on particle swarm optimization (PSO). The reconstruction effect of the algorithm and the anti-interference ability are verified through the reconstruction experiments of the model velocity field and the simulated velocity field. Full article
(This article belongs to the Special Issue Advances in Ultrasound Imaging and Sensing Technology)
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