Innovations in Ultrasound Imaging for Medical Diagnosis

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

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 13992

Special Issue Editor

School of Clinical Sciences, Queensland University of Technology, 4000 Brisbane, Australia
Interests: quantitative ultrasound; image guidance; radiotherapy; artificial intelligence; surgery

Special Issue Information

Dear Colleagues,

Ultrasound is one of the most widespread medical imaging modalities; among its other characteristics, it is radiation-free, portable, cost-effective, and high-quality both in terms of contrast and resolution. Its use has been steadily growing, with a tenfold increase between 2000 and 2011. However, its potential is still far from fully explored, with new technologies (such as flexible/wearable or capacitive micromachined transducers) and new techniques (for example, elastography or photoacoustic imaging) being continuously developed. Additionally, ultrasound image processing has bloomed in recent years, mainly thanks to the advent of machine-learning-based applications which have allowed more robust, more accurate, and often automatic interpretation and use of images. This, together with significant advances in artefact reduction implemented on state-of-the-art commercial ultrasound machines, has significantly reduced what have traditionally been considered limitations of this modality, such as operator dependence and complex image interpretation.

The aim of this Special Issue is to present novel technologies or applications or techniques in ultrasound imaging for medical diagnostics. Authors are invited to submit innovative research papers or comprehensive review papers.

Dr. Davide Fontanarosa
Guest Editor

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Keywords

  • ultrasound imaging
  • diagnostics
  • medical image processing
  • image interpretation
  • image acquisition

Published Papers (7 papers)

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Research

15 pages, 7079 KiB  
Article
Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy
by Gayatri Kompella, Jeevakala Singarayan, Maria Antico, Fumio Sasazawa, Takeda Yu, Keerthi Ram, Ajay K. Pandey, Davide Fontanarosa and Mohanasankar Sivaprakasam
Appl. Sci. 2022, 12(11), 5488; https://0-doi-org.brum.beds.ac.uk/10.3390/app12115488 - 28 May 2022
Cited by 3 | Viewed by 1679
Abstract
Registration of partial view intra-operative ultrasound (US) to pre-operative MRI is an essential step in image-guided minimally invasive surgery. In this paper, we present an automatic, landmark-free 3D multimodal registration of pre-operative MRI to 4D US (high-refresh-rate 3D-US) for enabling guidance in knee [...] Read more.
Registration of partial view intra-operative ultrasound (US) to pre-operative MRI is an essential step in image-guided minimally invasive surgery. In this paper, we present an automatic, landmark-free 3D multimodal registration of pre-operative MRI to 4D US (high-refresh-rate 3D-US) for enabling guidance in knee arthroscopy. We focus on the problem of initializing registration in the case of partial views. The proposed method utilizes a pre-initialization step of using the automatically segmented structures from both modalities to achieve a global geometric initialization. This is followed by computing distance maps of the procured segmentations for registration in the distance space. Following that, the final local refinement between the MRI-US volumes is achieved using the LC2 (Linear correlation of linear combination) metric. The method is evaluated on 11 cases spanning six subjects, with four levels of knee flexion. A best-case error of 1.41 mm and 2.34 and an average registration error of 3.45 mm and 7.76 is achieved in translation and rotation, respectively. An inter-observer variability study is performed, and a mean difference of 4.41 mm and 7.77 is reported. The errors obtained through the developed registration algorithm and inter-observer difference values are found to be comparable. We have shown that the proposed algorithm is simple, robust and allows for the automatic global registration of 3D US and MRI that can enable US based image guidance in minimally invasive procedures. Full article
(This article belongs to the Special Issue Innovations in Ultrasound Imaging for Medical Diagnosis)
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33 pages, 10250 KiB  
Article
Autonomous Prostate Segmentation in 2D B-Mode Ultrasound Images
by Jay Carriere, Ron Sloboda, Nawaid Usmani and Mahdi Tavakoli
Appl. Sci. 2022, 12(6), 2994; https://0-doi-org.brum.beds.ac.uk/10.3390/app12062994 - 15 Mar 2022
Viewed by 1461
Abstract
Prostate brachytherapy is a treatment for prostate cancer; during the planning of the procedure, ultrasound images of the prostate are taken. The prostate must be segmented out in each of the ultrasound images, and to assist with the procedure, an autonomous prostate segmentation [...] Read more.
Prostate brachytherapy is a treatment for prostate cancer; during the planning of the procedure, ultrasound images of the prostate are taken. The prostate must be segmented out in each of the ultrasound images, and to assist with the procedure, an autonomous prostate segmentation algorithm is proposed. The prostate contouring system presented here is based on a novel superpixel algorithm, whereby pixels in the ultrasound image are grouped into superpixel regions that are optimized based on statistical similarity measures, so that the various structures within the ultrasound image can be differentiated. An active shape prostate contour model is developed and then used to delineate the prostate within the image based on the superpixel regions. Before segmentation, this contour model was fit to a series of point-based clinician-segmented prostate contours exported from conventional prostate brachytherapy planning software to develop a statistical model of the shape of the prostate. The algorithm was evaluated on nine sets of in vivo prostate ultrasound images and compared with manually segmented contours from a clinician, where the algorithm had an average volume difference of 4.49 mL or 10.89%. Full article
(This article belongs to the Special Issue Innovations in Ultrasound Imaging for Medical Diagnosis)
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16 pages, 2119 KiB  
Article
A Cross-Machine Comparison of Shear-Wave Speed Measurements Using 2D Shear-Wave Elastography in the Normal Female Breast
by Emma Harris, Ruchi Sinnatamby, Elizabeth O’Flynn, Anna M. Kirby and Jeffrey C. Bamber
Appl. Sci. 2021, 11(20), 9391; https://0-doi-org.brum.beds.ac.uk/10.3390/app11209391 - 10 Oct 2021
Cited by 1 | Viewed by 1837
Abstract
Quantitative measures of radiation-induced breast stiffness are required to support clinical studies of novel breast radiotherapy regimens and exploration of personalised therapy, however, variation between shear-wave elastography (SWE) machines may limit the usefulness of shear-wave speed (cs) for this purpose. [...] Read more.
Quantitative measures of radiation-induced breast stiffness are required to support clinical studies of novel breast radiotherapy regimens and exploration of personalised therapy, however, variation between shear-wave elastography (SWE) machines may limit the usefulness of shear-wave speed (cs) for this purpose. Mean cs measured in four healthy volunteers’ breasts and a phantom using 2D-SWE machines Acuson S2000 (Siemens Medical Solutions) and Aixplorer (Supersonic Imagine) were compared. Shear-wave speed was measured in the skin region, subcutaneous adipose tissue and parenchyma. cs estimates were on average 2.3% greater when using the Aixplorer compared to S2000 in vitro. In vivo, cs estimates were on average 43.7%, 36.3% and 49.9% significantly greater (p << 0.01) when using the Aixplorer compared to S2000, for skin region, subcutaneous adipose tissue and parenchyma, respectively. In conclusion, despite relatively small differences between machines observed in vitro, large differences in absolute measures of shear wave speed measured were observed in vivo, which may prevent pooling of cross-machine data in clinical studies of the breast. Full article
(This article belongs to the Special Issue Innovations in Ultrasound Imaging for Medical Diagnosis)
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9 pages, 2806 KiB  
Article
Development of an Automatic Ultrasound Image Classification System for Pressure Injury Based on Deep Learning
by Masaru Matsumoto, Mikihiko Karube, Gojiro Nakagami, Aya Kitamura, Nao Tamai, Yuka Miura, Atsuo Kawamoto, Masakazu Kurita, Tomomi Miyake, Chieko Hayashi, Akiko Kawasaki and Hiromi Sanada
Appl. Sci. 2021, 11(17), 7817; https://0-doi-org.brum.beds.ac.uk/10.3390/app11177817 - 25 Aug 2021
Cited by 2 | Viewed by 1942
Abstract
The classification of ultrasound (US) findings of pressure injury is important to select the appropriate treatment and care based on the state of the deep tissue, but it depends on the operator’s skill in image interpretation. Therefore, US for pressure injury is a [...] Read more.
The classification of ultrasound (US) findings of pressure injury is important to select the appropriate treatment and care based on the state of the deep tissue, but it depends on the operator’s skill in image interpretation. Therefore, US for pressure injury is a procedure that can only be performed by a limited number of highly trained medical professionals. This study aimed to develop an automatic US image classification system for pressure injury based on deep learning that can be used by non-specialists who do not have a high skill in image interpretation. A total 787 training data were collected at two hospitals in Japan. The US images of pressure injuries were assessed using the deep learning-based classification tool according to the following visual evidence: unclear layer structure, cobblestone-like pattern, cloud-like pattern, and anechoic pattern. Thereafter, accuracy was assessed using two parameters: detection performance, and the value of the intersection over union (IoU) and DICE score. A total of 73 images were analyzed as test data. Of all 73 images with an unclear layer structure, 7 showed a cobblestone-like pattern, 14 showed a cloud-like pattern, and 15 showed an anechoic area. All four US findings showed a detection performance of 71.4–100%, with a mean value of 0.38–0.80 for IoU and 0.51–0.89 for the DICE score. The results show that US findings and deep learning-based classification can be used to detect deep tissue pressure injuries. Full article
(This article belongs to the Special Issue Innovations in Ultrasound Imaging for Medical Diagnosis)
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13 pages, 5109 KiB  
Article
Arthroscope Localization in 3D Ultrasound Volumes Using Weakly Supervised Deep Learning
by Jeroen M. A. van der Burgt, Saskia M. Camps, Maria Antico, Gustavo Carneiro and Davide Fontanarosa
Appl. Sci. 2021, 11(15), 6828; https://0-doi-org.brum.beds.ac.uk/10.3390/app11156828 - 25 Jul 2021
Viewed by 1434
Abstract
This work presents an algorithm based on weak supervision to automatically localize an arthroscope on 3D ultrasound (US). The ultimate goal of this application is to combine 3D US with the 2D arthroscope view during knee arthroscopy, to provide the surgeon with a [...] Read more.
This work presents an algorithm based on weak supervision to automatically localize an arthroscope on 3D ultrasound (US). The ultimate goal of this application is to combine 3D US with the 2D arthroscope view during knee arthroscopy, to provide the surgeon with a comprehensive view of the surgical site. The implemented algorithm consisted of a weakly supervised neural network, which was trained on 2D US images of different phantoms mimicking the imaging conditions during knee arthroscopy. Image-based classification was performed and the resulting class activation maps were used to localize the arthroscope. The localization performance was evaluated visually by three expert reviewers and by the calculation of objective metrics. Finally, the algorithm was also tested on a human cadaver knee. The algorithm achieved an average classification accuracy of 88.6% on phantom data and 83.3% on cadaver data. The localization of the arthroscope based on the class activation maps was correct in 92–100% of all true positive classifications for both phantom and cadaver data. These results are relevant because they show feasibility of automatic arthroscope localization in 3D US volumes, which is paramount to combining multiple image modalities that are available during knee arthroscopies. Full article
(This article belongs to the Special Issue Innovations in Ultrasound Imaging for Medical Diagnosis)
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12 pages, 1970 KiB  
Article
Computer-Assisted Detection of Cemento-Enamel Junction in Intraoral Ultrasonographs
by Kim-Cuong T. Nguyen, Yuening Yan, Neelambar R. Kaipatur, Paul W. Major, Edmond H. Lou, Kumaradevan Punithakumar and Lawrence H. Le
Appl. Sci. 2021, 11(13), 5850; https://0-doi-org.brum.beds.ac.uk/10.3390/app11135850 - 23 Jun 2021
Cited by 4 | Viewed by 2020
Abstract
The cemento-enamel junction (CEJ) is an important reference point for various clinical measurements in oral health assessment. Identifying CEJ in ultrasound images is a challenging task for dentists. In this study, a computer-assisted detection method is proposed to identify the CEJ in ultrasound [...] Read more.
The cemento-enamel junction (CEJ) is an important reference point for various clinical measurements in oral health assessment. Identifying CEJ in ultrasound images is a challenging task for dentists. In this study, a computer-assisted detection method is proposed to identify the CEJ in ultrasound images, based on the curvature change of the junction outlining the upper edge of the enamel and cementum at the cementum–enamel intersection. The technique consists of image preprocessing steps for image enhancement, segmentation, and edge detection to locate the boundary of the enamel and cementum. The effects of the image preprocessing and the sizes of the bounding boxes enclosing the CEJ were studied. For validation, the algorithm was applied to 120 images acquired from human volunteers. The mean difference of the best performance between the proposed method and the two raters’ measurements was an average of 0.25 mm with reliability ≥ 0.98. The proposed method has the potential to assist dental professionals in CEJ identification on ultrasonographs to provide better patient care. Full article
(This article belongs to the Special Issue Innovations in Ultrasound Imaging for Medical Diagnosis)
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10 pages, 1474 KiB  
Article
Intra-System Reliability Assessment of 2-Dimensional Shear Wave Elastography
by Christopher Edwards, Erika Cavanagh, Sailesh Kumar, Vicki Clifton and Davide Fontanarosa
Appl. Sci. 2021, 11(7), 2992; https://0-doi-org.brum.beds.ac.uk/10.3390/app11072992 - 26 Mar 2021
Cited by 7 | Viewed by 2416
Abstract
The availability of 2-Dimensional Shear Wave Elastography (2D-SWE) technology on modern medical ultrasound systems is becoming increasingly common. The technology is now being used to investigate a range of soft tissues and related pathological conditions. This work investigated the reliability of a single [...] Read more.
The availability of 2-Dimensional Shear Wave Elastography (2D-SWE) technology on modern medical ultrasound systems is becoming increasingly common. The technology is now being used to investigate a range of soft tissues and related pathological conditions. This work investigated the reliability of a single commercial 2D-SWE system using a tissue-mimicking elastography phantom to understand the major causes of intra-system variability. Sources of shear wave velocity (SWV) measurement variability relates to imaging depth, target stiffness, sampling technique and the operator. Higher SWV measurement variability was evident with increasing depth and stiffness of the phantom targets. The influence of the operator was minimal, and variations in sampling technique had little impact on the SWV. Full article
(This article belongs to the Special Issue Innovations in Ultrasound Imaging for Medical Diagnosis)
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