- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Optimal planar X-ray imaging soft tissue segmentation using a photon counting detector
摘要： A rigorous method for automated soft tissue segmentation using planar kilovoltage (kV) imaging, a photon counting detector (PCD), and a convolutional neural network is presented. The goal of the project was to determine the optimum number of energy bins in a PCD for soft tissue segmentation. Planar kV X-ray images of solid water (SW) phantoms with varying depth of cartilage were generated with a cone-beam analytical method and parallel-beam Monte Carlo simulations. Simulations were preformed using 2 to 5 PCD energy bins with equal photon fluence distribution. Simulated image signal to noise ratio (SNR) was varied between 10 to 250 measured after transmission through 4 cm of SW. Algorithms using non-linear as well as linear regression were used to predict the amount of cartilage for every pixel of the phantom. These algorithms were evaluated based on the mean squared error (MSE) between their prediction and the ground truth. The best algorithm was used to decompose randomly generated SW and cartilage images with an SNR of 100. These randomly generated images trained a U-Net convolutional neural network to segment the cartilage in the image. The results indicated the smallest MSE occurred for non-linear regression with 4 energy bins over all SNR. The trained U-Net was able to correctly segment all regions of cartilage for the smallest amount of cartilage used (4 mm) and segmented the region with > 99% categorical accuracy by pixel.
关键词： X-ray radiography and digital radiography (DR),Medical-image reconstruction methods and algorithms,computer-aided diagnosis
更新于2025-09-19 17:15:36
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[IEEE 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting - Boston, MA, USA (2018.7.8-2018.7.13)] 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting - Millimeter-Wave Synthetic Aperture Focusing for Packaging Inspection System
摘要： Synthetic aperture focusing technique is proposed for developing a packaging inspection system. The SAF algorithm is reformulated. The SAF reformulation is tested for a package inspection application with finite element simulation data.
关键词： synthetic aperture radar,Synthetic aperture focusing,and inspection,sectional imaging,image reconstruction,Fourier optics
更新于2025-09-19 17:15:36
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Prototype of an array SiPM-based scintillator Compton camera for radioactive materials detection
摘要： Purpose The Compton camera, which visualizes the distribution of gamma-ray sources based on the kinematics of Compton scattering, has advantage of wide field of view, broad range of energy and compact structure. Methods In this study, we proposed a prototype of Compton camera, which included array silicon photomultiplier (SiPM)-based position-sensitive detectors, data acquisition (DAQ) system and image reconstruction system. The detectors were composed of Ce-doped Gd3Al2Ga3O12 scintillator arrays and pixel Si-PM arrays. In DAQ system, symmetric charge division circuit, impedance bridge circuit and the delay coincidence algorithm were designed to record coincidence events. Simple back-projection algorithm and list-mode maximum likelihood expectation maximization algorithm were adopted for image reconstruction. The coordinate of longitude and latitude was used for image fusion. Results The performance of this Compton camera prototype system was evaluated. The results indicated that this system was able to locate a 137Cs point source within 20 s with the corresponding radiation dose of ~ 1.0 μSv/h. The angular resolution of point source was ~ 7° (FWHM), and the total energy resolution of 662 keV was 7.2%. Furthermore, we succeeded in separating two point sources of different energy [22Na (511 keV), 137Cs (662 keV)] in laboratory test. Conclusions This prototype of scintillator Compton camera offers capabilities for applications like source term investigation and radioactive materials detection.
关键词： Scintillator Compton camera,Image reconstruction,Compton imaging,Silicon photomultipliers
更新于2025-09-19 17:15:36
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FPGA Implementation of ECT Digital System for Imaging Conductive Materials
摘要： This paper presents the hardware implementation of a stand-alone Electrical Capacitance Tomography (ECT) system employing a Field Programmable Gate Array (FPGA). The image reconstruction algorithms of the ECT system demand intensive computation and fast processing of large number of measurements. The inner product of large vectors is the core of the majority of these algorithms. Therefore, a reconfigurable segmented parallel inner product architecture for the parallel matrix multiplication is proposed. In addition, hardware-software codesign targeting FPGA System-On-Chip (SoC) is applied to achieve high performance. The development of the hardware-software codesign is carried out via commercial tools to adjust the software algorithms and parameters of the system. The ECT system is used in this work to monitor the characteristic of the molten metal in the Lost Foam Casting (LFC) process. The hardware system consists of capacitive sensors, wireless nodes and FPGA module. The experimental results reveal high stability and accuracy when building the ECT system based on the FPGA architecture. The proposed system achieves high performance in terms of speed and small design density.
关键词： electrical tomography,image reconstruction algorithm,capacitance measurements,FPGA,LFC
更新于2025-09-19 17:15:36
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Low-Complexity Power-Balancing-Point Based Optimization for Photovoltaic Differential Power Processing
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical ?lter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coef?cient domain where each coef?cient measures the existence of Schmittlet-like structures in the image. By estimating their signi?cance via the perturbation-based noise model, the best-?tting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-?tting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The ?nal validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： image analysis,image reconstruction,image representations,image edge analysis,digital ?lters,Adaptive ?lters,image enhancement,synthetic aperture radar (SAR)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - CAOL 2019 Cover Page
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical ?lter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coef?cient domain where each coef?cient measures the existence of Schmittlet-like structures in the image. By estimating their signi?cance via the perturbation-based noise model, the best-?tting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-?tting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The ?nal validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： image analysis,image reconstruction,image representations,image edge analysis,digital ?lters,Adaptive ?lters,image enhancement,synthetic aperture radar (SAR)
更新于2025-09-19 17:13:59
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A Phase Calibration Method for Millimeter-Wave Up-Converter Using Electro-Optic Sampling
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： Adaptive filters,digital filters,image analysis,image reconstruction,image representations,image edge analysis,image enhancement,synthetic aperture radar (SAR)
更新于2025-09-19 17:13:59
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Dual-Core Photonic Crystal Fiber-Based Plasmonic RI Sensor in the Visible to Near-IR Operating Band
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： Adaptive filters,digital filters,image analysis,image reconstruction,image representations,image edge analysis,image enhancement,synthetic aperture radar (SAR)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Spatially Resolved Material Quality Prediction Via Constrained Deep Learning
摘要： Positron emission tomography (PET) images are typically reconstructed with an in-plane pixel size of approximately 4 mm for cancer imaging. The objective of this work was to evaluate the effect of using smaller pixels on general oncologic lesion-detection. A series of observer studies was performed using experimental phantom data from the Utah PET Lesion Detection Database, which modeled whole-body FDG PET cancer imaging of a 92 kg patient. The data comprised 24 scans over 4 days on a Biograph mCT time-of-flight (TOF) PET/CT scanner, with up to 23 lesions (diam. 6–16 mm) distributed throughout the phantom each day. Images were reconstructed with 2.036 mm and 4.073 mm pixels using ordered-subsets expectation-maximization (OSEM) both with and without point spread function (PSF) modeling and TOF. Detection performance was assessed using the channelized non-prewhitened numerical observer with localization receiver operating characteristic (LROC) analysis. Tumor localization performance and the area under the LROC curve were then analyzed as functions of the pixel size. In all cases, the images with ～2 mm pixels provided higher detection performance than those with ～4 mm pixels. The degree of improvement from the smaller pixels was larger than that offered by PSF modeling for these data, and provided roughly half the benefit of using TOF. Key results were confirmed by two human observers, who read subsets of the test data. This study suggests that a significant improvement in tumor detection performance for PET can be attained by using smaller voxel sizes than commonly used at many centers. The primary drawback is a 4-fold increase in reconstruction time and data storage requirements.
关键词： PET/CT reconstruction,PET/CT,image reconstruction,Image quality assessment
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Photovoltaic Inverter Momentary Cessation: Recovery Process is Key
摘要： Positron emission tomography (PET) images are typically reconstructed with an in-plane pixel size of approximately 4 mm for cancer imaging. The objective of this work was to evaluate the effect of using smaller pixels on general oncologic lesion-detection. A series of observer studies was performed using experimental phantom data from the Utah PET Lesion Detection Database, which modeled whole-body FDG PET cancer imaging of a 92 kg patient. The data comprised 24 scans over 4 days on a Biograph mCT time-of-flight (TOF) PET/CT scanner, with up to 23 lesions (diam. 6–16 mm) distributed throughout the phantom each day. Images were reconstructed with 2.036 mm and 4.073 mm pixels using ordered-subsets expectation-maximization (OSEM) both with and without point spread function (PSF) modeling and TOF. Detection performance was assessed using the channelized non-prewhitened numerical observer with localization receiver operating characteristic (LROC) analysis. Tumor localization performance and the area under the LROC curve were then analyzed as functions of the pixel size. In all cases, the images with ～2 mm pixels provided higher detection performance than those with ～4 mm pixels. The degree of improvement from the smaller pixels was larger than that offered by PSF modeling for these data, and provided roughly half the benefit of using TOF. Key results were confirmed by two human observers, who read subsets of the test data. This study suggests that a significant improvement in tumor detection performance for PET can be attained by using smaller voxel sizes than commonly used at many centers. The primary drawback is a 4-fold increase in reconstruction time and data storage requirements.
关键词： Image quality assessment,PET/CT reconstruction,PET/CT,image reconstruction
更新于2025-09-19 17:13:59