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Ultrasound Transducers in Industrial and Medical Applications: From Design to Image Reconstruction

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

Deadline for manuscript submissions: 1 July 2024 | Viewed by 7478

Special Issue Editors

Dr. Mohamed Almekkawy

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Science, Penn State University, University Park, PA 16802, USA
Interests: signal processing and machine learning with applications for biomedical ultrasound; inverse scattering problems; speckle tracking; numerical methods for linear and nonlinear wave propagation in tissue; focused ultrasound for therapeutic applications
Dr. Yun Jing

E-Mail Website
Guest Editor
Graduate Program in Acoustics, Penn State University, University Park, PA 16802, USA
Interests: acoustic metamaterials; architectural acoustics; biomedical ultrasound; noise control; nonlinear acoustics; physical acoustics; ultrasonic imaging; vibration control

Special Issue Information

Dear Colleagues,

Recent technological developments in the field of ultrasound have led to great improvements in diagnostic outcomes for medical and industrial applications. This technology is highly useful in screening for anomalies in both clinical scans, as well as in scans of materials used in industry. In comparison to other imaging modalities, ultrasound is advantageous in several ways. It is inexpensive, widely available, and is, thus, more accessible than other approaches. Furthermore, since this technology relies on sound waves for imaging of subjects, it is favorable for clinical use as the patient is exposed to no amount of ionizing radiation. Despite these benefits, much work is still needed to refine the diagnostic accuracy and capability of ultrasound technology through an increase in sensitivity and improved spatial and temporal resolution. This Special Issue aims to address the enhancements needed for this imaging modality by reviewing the current research on the design, development, and applications of ultrasound. We are looking for contributions in ultrasound reconstruction and transducer design indicating that this technology may be able to move from research settings to industrial and clinical applications. In searching for contributions to this Special Issue, the topics of consideration will span a variety of ultrasound-related disciplines. This includes the design and manufacturing of various types of application-specific transducers and their role in both biomedical diagnostics, as well as non-destructive testing for industrial purposes. It will also include the software aspects of ultrasound imaging, such as artificial intelligence and machine learning methods, as well as research relating to improved image acquisition, reconstruction, super resolution, resampling, and analytical and numerical modeling. Clinically important topics will be included and will incorporate spatiotemporally efficient diagnostic imaging, as well as direct therapeutic applications for ultrasound.

All contributions to this Special Issue will be published articles that includes original research; review papers; perspectives; methods and concepts that show a novel, practical, ultrasound-related application or approach that has been theoretically and/or experimentally verified.

Dr. Mohamed Almekkawy
Dr. Yun Jing
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 transducers
  • wave equation
  • numerical modeling
  • ultrasound localization microscopy
  • ultrasound medical imaging
  • nondestructive testing
  • ultrasound image reconstruction
  • elastography
  • speckle tracking

Published Papers (3 papers)

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Research

20 pages, 12395 KiB  
Article
Research on the 3D Reverse Time Migration Technique for Internal Defects Imaging and Sensor Settings of Pressure Pipelines
by Daicheng Peng, Xiaoyu She, Yunpeng Zheng, Yongjie Tang, Zhuo Fan and Guang Hu
Sensors 2023, 23(21), 8742; https://doi.org/10.3390/s23218742 - 26 Oct 2023
Viewed by 705
Abstract
Although pressure pipelines serve as a secure and energy-efficient means of transporting oil, gas, and chemicals, they are susceptible to fatigue cracks over extended periods of cyclic loading due to the challenging operational conditions. Their quality and efficiency directly affect the safe operation [...] Read more.
Although pressure pipelines serve as a secure and energy-efficient means of transporting oil, gas, and chemicals, they are susceptible to fatigue cracks over extended periods of cyclic loading due to the challenging operational conditions. Their quality and efficiency directly affect the safe operation of the project. Therefore, a thorough and precise characterization approach towards pressure pipelines can proactively mitigate safety risks and yield substantial economic and societal benefits. At present, the current mainstream 2D ultrasound imaging technology faces challenges in fully visualizing the internal defects and topography of pressure pipelines. Reverse time migration (RTM), widely employed in geophysical exploration, has the capability to visualize intricate geological structures. In this paper, we introduced the RTM into the realm of ultrasonic non-destructive testing, and proposed a 3D ultrasonic RTM imaging method for internal defects and sensor settings of pressure pipelines. To accurately simulate the extrapolation of wave field in 3D pressure pipelines, we set the absorbing boundary and double free boundary in cylindrical coordinates. Subsequently, using the 3D ultrasonic RTM approach, we attained higher-precision 3D imaging of internal defects in the pressure pipelines through suppressing imaging artifacts. By comparing and analyzing the imaging results of different sensor settings, the design of the observation system is optimized to provide a basis for the imaging and interpretation of actual data. Both simulations and actual field data demonstrate that our approach delivers top-notch 3D imaging of pipeline defects (with an imaging range accuracy up to 97.85%). This method takes into consideration the complexities of multiple scattering and mode conversions occurring at the base of the defects as well as the optimal sensor settings. Full article
(This article belongs to the Special Issue Ultrasound Transducers in Industrial and Medical Applications: From Design to Image Reconstruction)
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13 pages, 3105 KiB  
Article
3D Ultrasound Reconstructions of the Carotid Artery and Thyroid Gland Using Artificial-Intelligence-Based Automatic Segmentation—Qualitative and Quantitative Evaluation of the Segmentation Results via Comparison with CT Angiography
by Tudor Arsenescu, Radu Chifor, Tiberiu Marita, Andrei Santoma, Andrei Lebovici, Daniel Duma, Vitalie Vacaras and Alexandru Florin Badea
Sensors 2023, 23(5), 2806; https://doi.org/10.3390/s23052806 - 03 Mar 2023
Cited by 4 | Viewed by 1975
Abstract
The aim of this study was to evaluate the feasibility of a noninvasive and low-operator-dependent imaging method for carotid-artery-stenosis diagnosis. A previously developed prototype for 3D ultrasound scans based on a standard ultrasound machine and a pose reading sensor was used for this [...] Read more.
The aim of this study was to evaluate the feasibility of a noninvasive and low-operator-dependent imaging method for carotid-artery-stenosis diagnosis. A previously developed prototype for 3D ultrasound scans based on a standard ultrasound machine and a pose reading sensor was used for this study. Working in a 3D space and processing data using automatic segmentation lowers operator dependency. Additionally, ultrasound imaging is a noninvasive diagnosis method. Artificial intelligence (AI)-based automatic segmentation of the acquired data was performed for the reconstruction and visualization of the scanned area: the carotid artery wall, the carotid artery circulated lumen, soft plaque, and calcified plaque. A qualitative evaluation was conducted via comparing the US reconstruction results with the CT angiographies of healthy and carotid-artery-disease patients. The overall scores for the automated segmentation using the MultiResUNet model for all segmented classes in our study were 0.80 for the IoU and 0.94 for the Dice. The present study demonstrated the potential of the MultiResUNet-based model for 2D-ultrasound-image automated segmentation for atherosclerosis diagnosis purposes. Using 3D ultrasound reconstructions may help operators achieve better spatial orientation and evaluation of segmentation results. Full article
(This article belongs to the Special Issue Ultrasound Transducers in Industrial and Medical Applications: From Design to Image Reconstruction)
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20 pages, 1118 KiB  
Article
Wave Equation Modeling via Physics-Informed Neural Networks: Models of Soft and Hard Constraints for Initial and Boundary Conditions
by Shaikhah Alkhadhr and Mohamed Almekkawy
Sensors 2023, 23(5), 2792; https://doi.org/10.3390/s23052792 - 03 Mar 2023
Cited by 9 | Viewed by 4157
Abstract
Therapeutic ultrasound waves are the main instruments used in many noninvasive clinical procedures. They are continuously transforming medical treatments through mechanical and thermal effects. To allow for effective and safe delivery of ultrasound waves, numerical modeling methods such as the Finite Difference Method [...] Read more.
Therapeutic ultrasound waves are the main instruments used in many noninvasive clinical procedures. They are continuously transforming medical treatments through mechanical and thermal effects. To allow for effective and safe delivery of ultrasound waves, numerical modeling methods such as the Finite Difference Method (FDM) and the Finite Element Method (FEM) are used. However, modeling the acoustic wave equation can result in several computational complications. In this work, we study the accuracy of using Physics-Informed Neural Networks (PINNs) to solve the wave equation when applying different combinations of initial and boundary conditions (ICs and BCs) constraints. By exploiting the mesh-free nature of PINNs and their prediction speed, we specifically model the wave equation with a continuous time-dependent point source function. Four main models are designed and studied to monitor the effects of soft or hard constraints on the prediction accuracy and performance. The predicted solutions in all the models were compared to an FDM solution for prediction error estimation. The trials of this work reveal that the wave equation modeled by a PINN with soft IC and BC (soft–soft) constraints reflects the lowest prediction error among the four combinations of constraints. Full article
(This article belongs to the Special Issue Ultrasound Transducers in Industrial and Medical Applications: From Design to Image Reconstruction)
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