Purpose: This study aims to investigate and compare three nonplanar (NP) slicing algorithms. The algorithms aim to control the layer thickness variation (LTV), which is a common issue in supportless fabrication of fre...
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Surgical tool tip localization and tracking are essential components of surgical and interventional procedures. The cross sections of tool tips can be considered as acoustic point sources to achieve these tasks with d...
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This paper proposes an Active-Clamp Forward-Flyback (ACFF) converter with three planar transformers to achieve 4 kW output power. As autonomous driving technology rapidly advances, the demand for higher output power f...
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In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship&q...
Purpose: Ultrasound (US) elastography is a technique for non-invasive quantification of material properties, such as stiffness, from ultrasound images of deforming tissue. The material properties are calculated by sol...
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Purpose: Ultrasound (US) elastography is a technique for non-invasive quantification of material properties, such as stiffness, from ultrasound images of deforming tissue. The material properties are calculated by solving the inverse problem on the measured displacement field from the ultrasound images. The limitations of traditional inverse problem techniques in US elastography are either slow and computationally intensive (iterative techniques) or sensitive to measurement noise and dependent on full displacement field data (direct techniques). Thus, we develop and validate a deep learning approach for solving the inverse problem in US elastography. This involves recovering the spatial modulus distribution of the elastic modulus from one component of the US-measured displacement field. Approach: We present a U-Net-based deep learning neural network to address the inverse problem in ultrasound elastography. This approach diverges from traditional methods by focusing on a data-driven model. The neural network is trained using data generated from a forward finite element model. This simulation incorporates variations in the displacement fields that correspond to the elastic modulus distribution, allowing the network to learn without the need for extensive real-world measurement data. The inverse problem of predicting the modulus spatial distribution from ultrasound-measured displacement fields is addressed using a trained neural network. The neural network is evaluated with mean squared error (MSE) and mean absolute percentage error (MAPE) metrics. To extend our model to practical purposes, we conduct phantom experiments and also apply our model to clinical data. Results: Our simulated results indicate that our deep learning (DL) model effectively reconstructs modulus distributions, as evidenced by low MSE and MAPE evaluation metrics. We obtain a mean MAPE of 0.32% for a hard inclusion and 0.39% for a soft inclusion. Similarly, in our phantom studies, the predicted mo
Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) enhances the interpretability and performance of AI systems. This research comprehensively analyzes this integration, classifying approaches into th...
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The discrete current controllers can generally ensure high-performance machine control. However, for high-speed machines associated with low sampling-to-fundamental frequency ratios (SFR), the unavoidable disturbances...
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