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Diagnostics
Special Issues
Point-of-Care Ultrasound for an Improved and Individualized Care
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Point-of-Care Ultrasound for an Improved and Individualized Care

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Point-of-Care Diagnostics and Devices".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 2968

Special Issue Editor

Prof. Dr. Laurent Zieleskiewicz

E-Mail Website
Guest Editor
Anesthesiology and Intensive Care, North Hospital, APHM, Aix -Marseille University, 13015 Marseille, France
Interests: point of care ultrasound as a tool for an individualized peri-operative care

Special Issue Information

Dear Colleagues,

Personalized medicine is associated with improved outcomes. Point of care ultrasound is defined as an ultrasound exam performed at the bedside by the physician in charge of the patient. Point of care ultrasound can be used for diagnosis, guidance, or screening. It is used in almost all medical specialties and is now considered the fifth pillar of physical examination. It could allow for the individualized care of perioperative or critical patients, and by doing so improve prognosis.

In this issue, we will show that, by detecting at risk patients, diagnostic point of care ultrasound could allow for the personalization of prophylactic treatments. For example, point of care ultrasound can help us to detect the patients who will benefit the most from intensive care unit admission or prophylactic treatments such as non invasive ventilation.

We will also emphasize the role of diagnostic point of care ultrasound for individualized treatment. For example, point of care ultrasound can help us in the case of acute circulatory or respiratory failure or in the case of severe hemorrhage management. Finally, we will also underline the potential interest of new technologies in the field such as contrast enhanced US, speckle tracking technologies, etc.

Prof. Dr. Laurent Zieleskiewicz
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. Diagnostics 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

  • point of care ultrasound
  • thoracic ultrasound
  • abdominal ultrasound
  • cerebral ultrasound
  • ultrasound guidance
  • speckle tracking
  • contrast enhanced ultrasound

Published Papers (1 paper)

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Research

17 pages, 2806 KiB  
Article
De-Speckling Breast Cancer Ultrasound Images Using a Rotationally Invariant Block Matching Based Non-Local Means (RIBM-NLM) Method
by Gelan Ayana, Kokeb Dese, Hakkins Raj, Janarthanan Krishnamoorthy and Timothy Kwa
Diagnostics 2022, 12(4), 862; https://doi.org/10.3390/diagnostics12040862 - 30 Mar 2022
Cited by 11 | Viewed by 2506
Abstract
The ultrasonic technique is an indispensable imaging modality for diagnosis of breast cancer in young women due to its ability in efficiently capturing the tissue properties, and decreasing nega-tive recognition rate thereby avoiding non-essential biopsies. Despite the advantages, ultrasound images are affected by [...] Read more.
The ultrasonic technique is an indispensable imaging modality for diagnosis of breast cancer in young women due to its ability in efficiently capturing the tissue properties, and decreasing nega-tive recognition rate thereby avoiding non-essential biopsies. Despite the advantages, ultrasound images are affected by speckle noise, generating fine-false structures that decrease the contrast of the images and diminish the actual boundaries of tissues on ultrasound image. Moreover, speckle noise negatively impacts the subsequent stages in image processing pipeline, such as edge detec-tion, segmentation, feature extraction, and classification. Previous studies have formulated vari-ous speckle reduction methods in ultrasound images; however, these methods suffer from being unable to retain finer edge details and require more processing time. In this study, we propose a breast ultrasound de-speckling method based on rotational invariant block matching non-local means (RIBM-NLM) filtering. The effectiveness of our method has been demonstrated by com-paring our results with three established de-speckling techniques, the switching bilateral filter (SBF), the non-local means filter (NLMF), and the optimized non-local means filter (ONLMF) on 250 images from public dataset and 6 images from private dataset. Evaluation metrics, including Self-Similarity Index Measure (SSIM), Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE) were utilized to measure performance. With the proposed method, we were able to record average SSIM of 0.8915, PSNR of 65.97, MSE of 0.014, RMSE of 0.119, and computational speed of 82 seconds at noise variance of 20dB using the public dataset, all with p-value of less than 0.001 compared against NLMF, ONLMF, and SBF. Similarly, the proposed method achieved av-erage SSIM of 0.83, PSNR of 66.26, MSE of 0.015, RMSE of 0.124, and computational speed of 83 seconds at noise variance of 20dB using the private dataset, all with p-value of less than 0.001 compared against NLMF, ONLMF, and SBF. Full article
(This article belongs to the Special Issue Point-of-Care Ultrasound for an Improved and Individualized Care)
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