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Advances in Ultrasound Imaging and Sensing for the Clinician, Researcher, and Educator

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Prof. Dr. Mark K. Timmons

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School of Kinesiology, Marshall University, Huntington, WV, USA
Interests: sports medicine; biomechanics; shoulder kinematics; ultrasound imaging motion capture

Special Issue Information

Dear Colleagues,

High-frequency sonic energy is used to treat and diagnose disease and pathology. The images formed from the echo produced when soundwaves pass through biological tissues of differing density, chemical composition, and physical makeup are used by clinicians, researchers, and educators to study the anatomy of many organisms. Advances in ultrasound imaging technology have improved the quality of ultrasound images, as well as the cost and portability of ultrasound equipment. Point-of-care ultrasound imaging is practical and affordable owing to improvements in ultrasound technology. The same improvements in ultrasound technology has led to increased research activity, improving the understanding of disease, injury, and healing mechanisms. This Special Issue of Sensors will explore several areas of ultrasound imaging that have advanced over the previous several years.

Define normal and abnormal musculoskeletal anatomy;
Determine the relationship between musculoskeletal anatomy, motion, and MSK injury or impairment;
Determine normal and abnormal blood flow and tissue perfusion;
Determine the mechanical parameters of biological tissues;
Aid in the delivery of medication and therapeutic agents;
Aid in the diagnostic accuracy of clinical tests;
Serve as an educational aid;
Improved portability and access to imaging technology.

Prof. Dr. Mark K. Timmons
Guest Editor

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19 pages, 4867 KiB

Open AccessArticle

Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study

by Haiming Ai, Yong Huang, Dar-In Tai, Po-Hsiang Tsui and Zhuhuang Zhou

Sensors 2024, 24(17), 5513; https://doi.org/10.3390/s24175513 - 26 Aug 2024

Cited by 1 | Viewed by 828

Abstract

The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultrasound backscattered radiofrequency (RF) signals is emerging for tissue characterization as the RF signals carry abundant information related to tissue microstructures. However, the existing methods only used the time-domain information of the RF signals for liver fibrosis assessment, and the liver region of interest (ROI) is outlined manually. In this study, we proposed an approach for liver fibrosis assessment using deep learning models on ultrasound RF signals. The proposed method consisted of two-dimensional (2D) convolutional neural networks (CNNs) for automatic liver ROI segmentation from reconstructed B-mode ultrasound images and one-dimensional (1D) CNNs for liver fibrosis stage classification based on the frequency spectra (amplitude, phase, and power) of the segmented ROI signals. The Fourier transform was used to obtain the three kinds of frequency spectra. Two classical 2D CNNs were employed for liver ROI segmentation: U-Net and Attention U-Net. ROI spectrum signals were normalized and augmented using a sliding window technique. Ultrasound RF signals collected (with a 3-MHz transducer) from 613 participants (Group A) were included for liver ROI segmentation and those from 237 participants (Group B) for liver fibrosis stage classification, with a liver biopsy as the reference standard (Fibrosis stage: F0 = 27, F1 = 49, F2 = 51, F3 = 49, F4 = 61). In the test set of Group A, U-Net and Attention U-Net yielded Dice similarity coefficients of 95.05% and 94.68%, respectively. In the test set of Group B, the 1D CNN performed the best when using ROI phase spectrum signals to evaluate liver fibrosis stages ≥F1 (area under the receive operating characteristic curve, AUC: 0.957; accuracy: 89.19%; sensitivity: 85.17%; specificity: 93.75%), ≥F2 (AUC: 0.808; accuracy: 83.34%; sensitivity: 87.50%; specificity: 78.57%), and ≥F4 (AUC: 0.876; accuracy: 85.71%; sensitivity: 77.78%; specificity: 94.12%), and when using the power spectrum signals to evaluate ≥F3 (AUC: 0.729; accuracy: 77.14%; sensitivity: 77.27%; specificity: 76.92%). The experimental results demonstrated the feasibility of both the 2D and 1D CNNs in liver parenchyma detection and liver fibrosis characterization. The proposed methods have provided a new strategy for liver fibrosis assessment based on ultrasound RF signals, especially for early fibrosis detection. The findings of this study shed light on deep learning analysis of ultrasound RF signals in the frequency domain with automatic ROI segmentation. Full article

(This article belongs to the Special Issue Advances in Ultrasound Imaging and Sensing for the Clinician, Researcher, and Educator)

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12 pages, 1846 KiB

Open AccessArticle

Levator Scapulae Stiffness Measurement Reliability in Individuals with and without Chronic Neck Pain by Experienced and Novel Examiners

by Umut Varol, Juan Antonio Valera-Calero, Elena Sánchez-Jiménez, César Fernández-de-las-Peñas, Ricardo Ortega-Santiago, Mateusz D. Kobylarz and Marcos José Navarro-Santana

Sensors 2024, 24(1), 277; https://doi.org/10.3390/s24010277 - 3 Jan 2024

Cited by 2 | Viewed by 1608

Abstract

The levator scapulae muscle is a key structure in the etiopathology of neck and shoulder musculoskeletal pain. Although previous studies used shear-wave elastography (SWE) for characterizing this muscle elasticity, limited evidence assessed the inter-examiner reliability of this procedure. This study aimed to analyze the inter-examiner reliability for calculating Young’s modulus and shear wave speed in a cohort of participants with and without chronic neck pain. A diagnostic accuracy study was conducted, acquiring a set of SWE images at the C5 level in participants with and without neck pain (n = 34 and 33, respectively) by two examiners (one experienced and one novel). After blinding the participants’ identity, examiner involved, and side, the stiffness indicators were calculated by an independent rater in a randomized order. Intra-class correlation coefficients (ICC), standard error of measurement, minimal detectable changes, and coefficient of variation were calculated. Both cohorts had comparable sociodemographic characteristics (p > 0.05). No significant levator scapulae elasticity differences were found between genders, sides, or cohorts (all, p > 0.05). Inter-examiner reliability for calculating Young’s modulus and shear wave speed was moderate-to-good for assessing asymptomatic individuals (ICC = 0.714 and 0.779, respectively), while poor-to-moderate in patients with neck pain (ICC = 0.461 and 0.546, respectively). The results obtained in this study support the use of this procedure for assessing asymptomatic individuals. However, reliability estimates were unacceptable to support its use for assessing elasticity in patients with chronic neck pain. Future studies might consider that the shear wave speed is more sensitive to detect real changes in comparison with Young’s modulus. Full article

(This article belongs to the Special Issue Advances in Ultrasound Imaging and Sensing for the Clinician, Researcher, and Educator)

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