Purpose: Decorrelated Compounding (DC) for synthetic aperture ultrasound can reduce speckle variation in images, suggesting enhanced detectability of low-contrast targets in tissue including thermal lesions produced by focused ultrasound (FUS). The DC imaging method has primarily been investigated in simulation and in phantom studies. This work investigates the feasibility of the DC method in monitoring thermal therapy via image guidance and non-invasive thermometry based on the change in backscattered energy (CBE).
Methods: Ex vivo porcine tissue was exposed to FUS exposures at acoustic powers of 5 W and 1 W, with peak pressure amplitudes of 0.64 MPa and 0.27 MPa respectively. During FUS exposure, RF echo data frames was acquired using a 7.8 MHz linear array probe and a Verasonics Vantage ultrasound scanner (Verasonics Inc., Redmond, WA). RF echo data was taken to produce B-mode images, as reference images. Synthetic aperture RF echo data was also acquired and processed using delay-and-sum (DAS), a combination of spatial and frequency compounding referred to as Traditional Compounding (TC), and the proposed DC imaging methods. Image quality was assessed using the contrast-to-noise ratio (CNR) at the FUS beam focus, and the speckle SNR (sSNR) of the background region as preliminary metrics. A calibrated thermocouple was placed near the FUS beam focus for temperature measurements and calibrations using the CBE method.
Results: The DC imaging method significantly improved image quality to detect low contrast thermal lesions in treated ex vivo porcine tissue in comparison to other imaging methods. In comparison to B-mode imaging, the lesion CNR measured using the DC imaging was shown to improve up to a factor of approximately 5.5. The corresponding sSNR improved by a factor of approximately 4.2 in comparison to B-mode imaging. CBE calculation using the DC imaging method yielded more precise measurements of the backscattered energy compared to other imaging methods studied.
Conclusions: The despeckling performance of the DC imaging method significantly improves the lesion CNR in comparison to B-mode imaging. This suggests that the proposed method can detect low-contrast thermal lesions induced by FUS therapy that are not detectable using standard B-mode imaging. Furthermore, the signal change at the focal point were more precisely measured by DC imaging, and the signal change in response to FUS exposure follows the temperature profile more closely than changes measured using B-mode, as well as synthetic aperture DAS and TC images. These suggest that DC imaging can potentially be used with the CBE method to improve non-invasive thermometry.
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http://dx.doi.org/10.1016/j.ultras.2023.107098 | DOI Listing |
Sensors (Basel)
December 2024
ENSTA Bretagne, Lab-STICC, UMR CNRS 6285, 29806 Brest, France.
Satellite SAR (synthetic aperture radar) imagery offers global coverage and all-weather recording capabilities, making it valuable for applications like remote sensing and maritime surveillance. However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Wireless Sensing and Imaging Laboratory & 6G Research Laboratory, SRM University AP, Amaravati 522502, India.
This study presents a numerical modeling approach that utilizes millimeter-wave (mm-Wave) Frequency-Modulated Continuous-Wave (FMCW) radar to reconstruct and classify five weapon types: grenades, knives, guns, iron rods, and wrenches. A dataset of 1000 images of these weapons was collected from various online sources and subsequently used to generate 3605 samples in the MATLAB (R2022b) environment for creating reflectivity-added images. Background reflectivity was considered to range from 0 to 0.
View Article and Find Full Text PDFUltrason Imaging
January 2025
Biomedical Ultrasound Imaging Laboratory, Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology, Madras, Chennai, India.
Imaging depth remains a restriction for Synthetic Aperture (SA) approaches, even though SA techniques have been shown to overcome some of the drawbacks of Conventional Focused Beamforming (CFB) technique. This limitation is attributed to lesser energy per transmit in SA techniques compared to that of CFB technique. Therefore, in this paper, a systematic investigation is done to evaluate the improvement in imaging depth and image quality of B-mode ultrasound images in the case of SA technique using PZT transducer by boosting the input voltage to the transducer, while measuring the acoustic exposure parameters recommended in international standards.
View Article and Find Full Text PDFNat Commun
January 2025
State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, China.
Complex-valued neural networks process both amplitude and phase information, in contrast to conventional artificial neural networks, achieving additive capabilities in recognizing phase-sensitive data inherent in wave-related phenomena. The ever-increasing data capacity and network scale place substantial demands on underlying computing hardware. In parallel with the successes and extensive efforts made in electronics, optical neuromorphic hardware is promising to achieve ultra-high computing performances due to its inherent analog architecture and wide bandwidth.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Earth Observation Science, Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Overijssel, The Netherlands.
Accurate global glacier mapping is critical for understanding climate change impacts. Despite its importance, automated glacier mapping at a global scale remains largely unexplored. Here we address this gap and propose Glacier-VisionTransformer-U-Net (GlaViTU), a convolutional-transformer deep learning model, and five strategies for multitemporal global-scale glacier mapping using open satellite imagery.
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