Special Issue "Signal Processing and Image Analysis Techniques for Lung Ultrasound Imaging"

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

Deadline for manuscript submissions: 15 December 2019.

Special Issue Editors

Assis. Prof. Libertario Demi
E-Mail Website
Guest Editor
University of Trento, Department of Information Engineering and Computer Science
Interests: signal processing, image analysis, ultrasound imaging, beam-forming
M.D. Gino Soldati
E-Mail Website
Guest Editor
USL nord-ovest Toscana, AdET (Academy of Thoracic Echocardiograpy) Scientific Director
Interests: lung ultrasound; emergency medicine; critical care; trauma; ultrasound; interventional ultrasound; echocardiography; hemodynamics

Special Issue Information

Dear Colleagues,

The use of ultrasound imaging for the monitoring and diagnosis of lung diseases is finally receiving the attention it deserves. Of particular interest are specific imaging artifacts, e.g., A- and B-lines. In particular, while A-lines are generally believed to be an indication of a healthy lung, B-lines have been found to correlate with several pathological conditions. In essence, these patterns carry information.

Spotting and recognising these artifacts is by itself already useful in clinical practice, and technical solutions aimed at automatically detecting and localising these patterns are of clear support for the clinician. However, deepening our knowledge on the ultrasound signals behind these artifacts, on their genesis, and on their link with the alterations of the lung structure, is crucial in order to develop quantitative signal processing and image analysis solutions able to distinguish different grades and types of diseases. The lung is in fact a very peculiar acoustic medium, and calls for a dedicated imaging approach.

This special issue focuses on the challenges we have to face if we are to make use of the full potentials of ultrasound technologies when applied to the lung. Contributions are welcome on the design, development and testing of automated artifact detection algorithms, dedicated signal processing and image analysis approaches, as well as on the fabrication of lung-mimicking phantoms, and on the clinical and safety aspects of LUS.

Assistant Professor, Dr. Libertario Demi
M.D. Gino Soldati
Guest Editors

Manuscript Submission Information

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Keywords

  • lung ultrasound
  • signal processing
  • image analysis
  • pattern recognition
  • deep learning

Published Papers (1 paper)

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Research

Open AccessArticle
A Pilot Study of Wet Lung Using Lung Ultrasound Surface Wave Elastography in an Ex Vivo Swine Lung Model
Appl. Sci. 2019, 9(18), 3923; https://doi.org/10.3390/app9183923 - 19 Sep 2019
Abstract
Extravascular lung water (EVLW) is a basic symptom of congestive heart failure and other conditions. Computed tomography (CT) is standard method used to assess EVLW, but it requires ionizing radiation and radiology facilities. Lung ultrasound reverberation artifacts called B-lines have been used to [...] Read more.
Extravascular lung water (EVLW) is a basic symptom of congestive heart failure and other conditions. Computed tomography (CT) is standard method used to assess EVLW, but it requires ionizing radiation and radiology facilities. Lung ultrasound reverberation artifacts called B-lines have been used to assess EVLW. However, analysis of B-line artifacts depends on expert interpretation and is subjective. Lung ultrasound surface wave elastography (LUSWE) was developed to measure lung surface wave speed. This pilot study aimed at measureing lung surface wave speed due to lung water in an ex vivo swine lung model. The surface wave speeds of a fresh ex vivo swine lung were measured at 100 Hz, 200 Hz, 300 Hz, and 400 Hz. An amount of water was then filled into the lung through its trachea. Ultrasound imaging was used to guide the water filling until significant changes were visible on the imaging. The lung surface wave speeds were measured again. It was found that the lung surface wave speed increases with frequency and decreases with water volume. These findings are confirmed by experimental results on an additional ex vivo swine lung sample. Full article
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