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Recent Advances in the Acquisition and Processing of Biomedical Signals...
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Recent Advances in the Acquisition and Processing of Biomedical Signals and Images

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3214

Special Issue Editors

Dr. Maria Filomena Santarelli

E-Mail Website
Guest Editor
CNR Institute of Clinical Physiology, 56124 Pisa, Italy
Interests: medical image acquisition and reconstruction
Special Issues, Collections and Topics in MDPI journals
Dr. Vincenzo Positano

E-Mail Website
Guest Editor
Fondazione Toscana “G. Monasterio”, 56124 Pisa, Italy
Interests: medical image processing and analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Nicola Vanello

E-Mail Website
Guest Editor
Dipartimento di Ingegneria dell’Informazione, University of Pisa, 56126 Pisa, Italy
Interests: biomedical signal processing; fMRI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical images and signals have the potential to provide information about the anatomical, structural and functional properties of different organs and tissues, from single cells to the whole body. The development of new technologies in the field of biomedical and signal processing has led to a continuous evolution of both instrumentation and methods of analysis.

Furthermore, the development and application of advanced methods and/or models for the extraction of quantitative indices from images and signals is an ever-evolving research activity.

This Special Issue therefore aims to collect original research and review articles on recent advances, technologies, applications and new challenges in the field of biomedical images and signal processing.

Potential topics include but are not limited to:

  • Innovative detectors in biomedical image and signal acquisition;
  • New trends in biomedical imaging methodologies, such as magnetic resonance imaging, nuclear medicine imaging, computed tomography and echography;
  • Hybrid biomedical imaging technologies;
  • Advances in one-dimensional and multi-dimensional signal processing techniques;
  • Machine learning and deep learning for biomedical image and signal processing;
  • Explainable artificial intelligence for biomedical image and signal processing

Dr. Maria Filomena Santarelli
Dr. Vincenzo Positano
Dr. Nicola Vanello
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.

Published Papers (4 papers)

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Research

13 pages, 627 KiB  
Article
Hardware and Software Setup for Quantitative 23Na Magnetic Resonance Imaging at 3T: A Phantom Study
by Giulio Giovannetti, Alessandra Flori, Nicola Martini, Filippo Cademartiri, Giovanni Donato Aquaro, Alessandro Pingitore and Francesca Frijia
Sensors 2024, 24(9), 2716; https://doi.org/10.3390/s24092716 - 24 Apr 2024
Abstract
Magnetic resonance (MR) with sodium (23Na) is a noninvasive tool providing quantitative biochemical information regarding physiology, cellular metabolism, and viability, with the potential to extend MR beyond anatomical proton imaging. However, when using clinical scanners, the low detectable 23Na signal [...] Read more.
Magnetic resonance (MR) with sodium (23Na) is a noninvasive tool providing quantitative biochemical information regarding physiology, cellular metabolism, and viability, with the potential to extend MR beyond anatomical proton imaging. However, when using clinical scanners, the low detectable 23Na signal and the low 23Na gyromagnetic ratio require the design of dedicated radiofrequency (RF) coils tuned to the 23Na Larmor frequency and sequences, as well as the development of dedicated phantoms for testing the image quality, and an MR scanner with multinuclear spectroscopy (MNS) capabilities. In this work, we propose a hardware and software setup for evaluating the potential of 23Na magnetic resonance imaging (MRI) with a clinical scanner. In particular, the reliability of the proposed setup and the reproducibility of the measurements were verified by multiple acquisitions from a 3T MR scanner using a homebuilt RF volume coil and a dedicated sequence for the imaging of a phantom specifically designed for evaluating the accuracy of the technique. The final goal of this study is to propose a setup for standardizing clinical and research 23Na MRI protocols. Full article
(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)
24 pages, 9326 KiB  
Article
Information-Theoretical Analysis of the Cycle of Creation of Knowledge and Meaning in Brains under Multiple Cognitive Modalities
by Joshua J. J. Davis, Florian Schübeler and Robert Kozma
Sensors 2024, 24(5), 1605; https://doi.org/10.3390/s24051605 - 29 Feb 2024
Viewed by 968
Abstract
It is of great interest to develop advanced sensory technologies allowing non-invasive monitoring of neural correlates of cognitive processing in people performing everyday tasks. A lot of progress has been reported in recent years in this research area using scalp EEG arrays, but [...] Read more.
It is of great interest to develop advanced sensory technologies allowing non-invasive monitoring of neural correlates of cognitive processing in people performing everyday tasks. A lot of progress has been reported in recent years in this research area using scalp EEG arrays, but the high level of noise in the electrode signals poses a lot of challenges. This study presents results of detailed statistical analysis of experimental data on the cycle of creation of knowledge and meaning in human brains under multiple cognitive modalities. We measure brain dynamics using a HydroCel Geodesic Sensor Net, 128-electrode dense-array electroencephalography (EEG). We compute a pragmatic information (PI) index derived from analytic amplitude and phase, by Hilbert transforming the EEG signals of 20 participants in six modalities, which combine various audiovisual stimuli, leading to different mental states, including relaxed and cognitively engaged conditions. We derive several relevant measures to classify different brain states based on the PI indices. We demonstrate significant differences between engaged brain states that require sensory information processing to create meaning and knowledge for intentional action, and relaxed-meditative brain states with less demand on psychophysiological resources. We also point out that different kinds of meanings may lead to different brain dynamics and behavioral responses. Full article
(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)
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20 pages, 14704 KiB  
Article
Multi-Feature Automatic Extraction for Detecting Obstructive Sleep Apnea Based on Single-Lead Electrocardiography Signals
by Yu Zhou and Kyungtae Kang
Sensors 2024, 24(4), 1159; https://doi.org/10.3390/s24041159 - 09 Feb 2024
Viewed by 825
Abstract
Obstructive sleep apnea (OSA), a prevalent sleep disorder, is intimately associated with various other diseases, particularly cardiovascular conditions. The conventional diagnostic method, nocturnal polysomnography (PSG), despite its widespread use, faces challenges due to its high cost and prolonged duration. Recent developments in electrocardiogram-based [...] Read more.
Obstructive sleep apnea (OSA), a prevalent sleep disorder, is intimately associated with various other diseases, particularly cardiovascular conditions. The conventional diagnostic method, nocturnal polysomnography (PSG), despite its widespread use, faces challenges due to its high cost and prolonged duration. Recent developments in electrocardiogram-based diagnostic techniques have opened new avenues for addressing these challenges, although they often require a deep understanding of feature engineering. In this study, we introduce an innovative method for OSA classification that combines a composite deep convolutional neural network model with a multimodal strategy for automatic feature extraction. This approach involves transforming the original dataset into scalogram images that reflect heart rate variability attributes and Gramian angular field matrix images that reveal temporal characteristics, aiming to enhance the diversity and richness of data features. The model comprises automatic feature extraction and feature enhancement components and has been trained and validated on the PhysioNet Apnea-ECG database. The experimental results demonstrate the model’s exceptional performance in diagnosing OSA, achieving an accuracy of 96.37%, a sensitivity of 94.67%, a specificity of 97.44%, and an AUC of 0.96. These outcomes underscore the potential of our proposed model as an efficient, accurate, and convenient tool for OSA diagnosis. Full article
(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)
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20 pages, 3700 KiB  
Article
Three-Dimensional Multi-Modality Registration for Orthopaedics and Cardiovascular Settings: State-of-the-Art and Clinical Applications
by Simone Garzia, Katia Capellini, Emanuele Gasparotti, Domenico Pizzuto, Giuseppe Spinelli, Sergio Berti, Vincenzo Positano and Simona Celi
Sensors 2024, 24(4), 1072; https://doi.org/10.3390/s24041072 - 07 Feb 2024
Viewed by 642
Abstract
The multimodal and multidomain registration of medical images have gained increasing recognition in clinical practice as a powerful tool for fusing and leveraging useful information from different imaging techniques and in different medical fields such as cardiology and orthopedics. Image registration could be [...] Read more.
The multimodal and multidomain registration of medical images have gained increasing recognition in clinical practice as a powerful tool for fusing and leveraging useful information from different imaging techniques and in different medical fields such as cardiology and orthopedics. Image registration could be a challenging process, and it strongly depends on the correct tuning of registration parameters. In this paper, the robustness and accuracy of a landmarks-based approach have been presented for five cardiac multimodal image datasets. The study is based on 3D Slicer software and it is focused on the registration of a computed tomography (CT) and 3D ultrasound time-series of post-operative mitral valve repair. The accuracy of the method, as a function of the number of landmarks used, was performed by analysing root mean square error (RMSE) and fiducial registration error (FRE) metrics. The validation of the number of landmarks resulted in an optimal number of 10 landmarks. The mean RMSE and FRE values were 5.26 ± 3.17 and 2.98 ± 1.68 mm, respectively, showing comparable performances with respect to the literature. The developed registration process was also tested on a CT orthopaedic dataset to assess the possibility of reconstructing the damaged jaw portion for a pre-operative planning setting. Overall, the proposed work shows how 3D Slicer and registration by landmarks can provide a useful environment for multimodal/unimodal registration. Full article
(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)
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