Ultrasonic Through-Transmission Measurements of Human Musculoskeletal and Fat Properties

Bi, Dongsheng; Shi, Lingwei; Liu, Chengcheng; Li, Boyi; Liu, Ying; Le, Lawrence H.; Luo, Jingchun; Wang, Sijia; Ta, Dean

doi:10.1016/j.ultrasmedbio.2022.09.007

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…In addition to detecting structural defects, the ultrasonic non-destructive testing methods also can be used to measure the thickness [ 5 , 6 , 7 ] or elasticity [ 8 ] of the material. Furthermore, ultrasonic flaw detection technique has been applied to food [ 9 ] and medical fields [ 10 , 11 , 12 ]. As described above, ultrasonic non-destructive inspection methods are capable of detecting defects on several structures and can be used for various purposes.…”

Section: Introductionmentioning

confidence: 99%

Optimal Transducer Placement for Deep Learning-Based Non-Destructive Evaluation

Kim

Han

2023

Sensors

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In this study, the Convolution Neural Network (CNN) algorithm is applied for non-destructive evaluation of aluminum panels. A method of classifying the locations of defects is proposed by exciting an aluminum panel to generate ultrasonic Lamb waves, measuring data with a sensor array, and then deep learning the characteristics of 2D imaged, reflected waves from defects. For the purpose of a better performance, the optimal excitation location and sensor locations are investigated. To ensure the robustness of the training model and extract the feature effectively, experimental data are collected by slightly changing the excitation frequency and shifting the location of the defect. The high classification accuracy for each defect location can be achieved. It is found that the proposed algorithm is also successfully applied even when a bar is attached to the panel.

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Section: Introductionmentioning

confidence: 99%

Optimal Transducer Placement for Deep Learning-Based Non-Destructive Evaluation

Kim

Han

2023

Sensors

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“…The measurement resolution ranges from a few centimeters to tens of nanometers, depending on the ultrasonic wavelength. Ultrasonic sensors have been used in various applications, such as distance measurement [1][2][3], positioning [4][5][6], non-destructive testing [7,8], and human physical inspection [9][10][11], because of their diverse measurement resolution. In particular, ultrasonic sensors see widespread use to obtain distance measurements in air because they are less expensive than other sensors and can measure with high accuracy, even with simple hardware configurations.…”

Section: Introductionmentioning

confidence: 99%

Improved Frequency Sweep Keying CDMA Using Faster R-CNN for Extended Ultrasonic Crosstalk Reduction

Park,

Baek

2023

Sensors

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Ultrasonic sensors are inexpensive and provide highly accurate measurements, even with simple hardware configurations, facilitating their use in various fields. When multiple ultrasonic sensors exist in the measurement space, crosstalk occurs due to other nodes, which leads to incorrect measurements. Crosstalk includes not only receiving homogeneous signals from other nodes, but also overlapping by other signals and interference by heterogeneous signals. This paper proposes using frequency sweep keying modulation to provide robustness against overlap and a faster region-based convolutional neural network (R-CNN) demodulator to reduce the interference caused by heterogeneous signals. The demodulator works by training Faster R-CNN with the spectrograms of various received signals and classifying the received signals using Faster R-CNN. Experiments implementing an ultrasonic crosstalk environment showed that, compared to on–off keying (OOK), phase-shift keying (PSK), and frequency-shift keying (FSK), the proposed method can implement CDMA even with shorter codes and is robust against overlap. Compared to correlation-based frequency sweep keying, the time-of-flight error was reduced by approximately 75%. While the existing demodulators did not consider heterogeneous signals, the proposed method ignored approximately 99% of the OOK and PSK signals and approximately 79% of the FSK signals. The proposed method performed better than the existing methods and is expected to be used in various applications.

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Validity of an ultrasound device to measure bone mineral density

Puranda,

Edwards,

Weber

et al. 2024

Clinical Anatomy

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This study aims to examine the validity and reliability of the UltraScan650™, a portable ultrasound device, used to measure BMD at the 1/3rd radius position. Fifty‐two female first responders and healthcare providers were assessed using DXA (forearm, femur, lumbar, and total body) and the UltraScan650™. Fat and lean mass were also assessed using the DXA. Pearson correlations, Bland–Altman plots, t‐tests, and linear regressions were used to assess validity. Intra‐class correlation (ICC) coefficients were used to assess reliability. Inter‐rater reliability and repeatability were good (ICC = 0.896 [0.818; 0.942], p < 0.001) and excellent (ICC = 0.917 [0.785; 0.989], p < 0.001), respectively. BMD as measured by the UltraScan650™ was weakly correlated to the DXA (r = 0.382 [0.121; 0.593], p = 0.0052). Bland–Altman plots revealed that the UltraScan650™ underestimated BMD (−0.0569 g/cm2), this was confirmed with a significant paired t‐test (p < 0.001). A linear regression was performed (0.4744 × UltraScan650™ + 0.4170) to provide more information as to the issue of agreement. Bland–Altman plots revealed a negligible bias, supported by a paired t‐test (p = 0.9978). Pearson's correlation revealed a significant relationship (r = −0.771 [−0.862; −0.631], p < 0.0001) between adjusted UltraScan650™—DXA and the average of the two scans (i.e., adjusted UltraScan650™ and DXA), suggesting a proportional constant error and proportional constant variability in measurements of BMD from the UltraScan650™. The UltraScan650™ is not a valid alternative to DXA for diagnostic purposes; however, the UltraScan650™ could be used as a screening tool in the clinical and research setting given the linear transformation is employed.

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Ultrasonic Through-Transmission Measurements of Human Musculoskeletal and Fat Properties

Cited by 6 publications

References 51 publications

Optimal Transducer Placement for Deep Learning-Based Non-Destructive Evaluation

Optimal Transducer Placement for Deep Learning-Based Non-Destructive Evaluation

Improved Frequency Sweep Keying CDMA Using Faster R-CNN for Extended Ultrasonic Crosstalk Reduction

Validity of an ultrasound device to measure bone mineral density

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