The Experts below are selected from a list of 249 Experts worldwide ranked by ideXlab platform
Jerry L. Prince - One of the best experts on this subject based on the ideXlab platform.
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ISBI - TruHARP: single breath-hold MRI for high resolution cardiac motion and strain quantification
Proceedings. IEEE International Symposium on Biomedical Imaging, 2009Co-Authors: Harsh K. Agarwal, Khaled Z. Abd-elmoniem, Jerry L. PrinceAbstract:MRI techniques for tissue motion and strain quantifications have limited resolution because of interference from the conjugate echo or spectral peak in Fourier space. Methods have been proposed to remove this interference in order to improve resolution; however, these methods are clinically impractical due to long image acquisition time. In this paper, we propose TruHARP, an MRI motion and strain quantification methodology that involves a novel single breath-hold imaging protocol. In post-processing, TruHARP separates the spectral peaks in the Acquired Datasets, enabling high resolution motion and strain quantification. The impact of high resolution on circumferential and radial strain is studied using a realistic simulation and the improvement in strain maps is demonstrated in an in-vivo human study.
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TruHARP: single breath-hold MRI for high resolution cardiac motion and strain quantification
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009Co-Authors: Harsh K. Agarwal, Khaled Z. Abd-elmoniem, Jerry L. PrinceAbstract:MRI techniques for tissue motion and strain quantifications have limited resolution because of interference from the conjugate echo or spectral peak in Fourier space. Methods have been proposed to remove this interference in order to improve resolution; however, these methods are clinically impractical due to long image acquisition time. In this paper, we propose TruHARP, an MRI motion and strain quantification methodology that involves a novel single breath-hold imaging protocol. In post-processing, TruHARP separates the spectral peaks in the Acquired Datasets, enabling high resolution motion and strain quantification. The impact of high resolution on circumferential and radial strain is studied using a realistic simulation and the improvement in strain maps is demonstrated in an in-vivo human study.
Harsh K. Agarwal - One of the best experts on this subject based on the ideXlab platform.
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ISBI - TruHARP: single breath-hold MRI for high resolution cardiac motion and strain quantification
Proceedings. IEEE International Symposium on Biomedical Imaging, 2009Co-Authors: Harsh K. Agarwal, Khaled Z. Abd-elmoniem, Jerry L. PrinceAbstract:MRI techniques for tissue motion and strain quantifications have limited resolution because of interference from the conjugate echo or spectral peak in Fourier space. Methods have been proposed to remove this interference in order to improve resolution; however, these methods are clinically impractical due to long image acquisition time. In this paper, we propose TruHARP, an MRI motion and strain quantification methodology that involves a novel single breath-hold imaging protocol. In post-processing, TruHARP separates the spectral peaks in the Acquired Datasets, enabling high resolution motion and strain quantification. The impact of high resolution on circumferential and radial strain is studied using a realistic simulation and the improvement in strain maps is demonstrated in an in-vivo human study.
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TruHARP: single breath-hold MRI for high resolution cardiac motion and strain quantification
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009Co-Authors: Harsh K. Agarwal, Khaled Z. Abd-elmoniem, Jerry L. PrinceAbstract:MRI techniques for tissue motion and strain quantifications have limited resolution because of interference from the conjugate echo or spectral peak in Fourier space. Methods have been proposed to remove this interference in order to improve resolution; however, these methods are clinically impractical due to long image acquisition time. In this paper, we propose TruHARP, an MRI motion and strain quantification methodology that involves a novel single breath-hold imaging protocol. In post-processing, TruHARP separates the spectral peaks in the Acquired Datasets, enabling high resolution motion and strain quantification. The impact of high resolution on circumferential and radial strain is studied using a realistic simulation and the improvement in strain maps is demonstrated in an in-vivo human study.
Motoyuki Sato - One of the best experts on this subject based on the ideXlab platform.
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IGARSS - Robust Subsurface Velocity Change Detection Method with Yakumo Multistatic GPR System
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Kazutaka Kikuta, Li Yi, Motoyuki SatoAbstract:To detect pavement damage of airport taxiway caused by cracks inside layer, we applied a novel method to common midpoint (CMP) dataset obtained by multistatic ground penetrating radar (GPR) system YAKUMO. Thin cracks within asphalt layers cause slight variation to the electromagnetic (EM) signal propagation velocity. The proposed method can show higher-resolution images of velocity changes of two different time set data with a small number of CMP traces. YAKUMO can acquire several parallel distributed CMP Datasets at each position while moving the system linearly in real time, which makes it possible to apply this method to large-scale inspections. In this research, we Acquired Datasets at an airport taxi-way model with artificial damages at several times over a year. The Datasets are analyzed with the proposed method to find out how the conditions of pavement changes by pressurization and time course. The results show that the method can detect not only overall change but also local area changes.
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Robust Subsurface Velocity Change Detection Method with Yakumo Multistatic GPR System
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019Co-Authors: Kazutaka Kikuta, Li Yi, Motoyuki SatoAbstract:To detect pavement damage of airport taxiway caused by cracks inside layer, we applied a novel method to common midpoint (CMP) dataset obtained by multistatic ground penetrating radar (GPR) system YAKUMO. Thin cracks within asphalt layers cause slight variation to the electromagnetic (EM) signal propagation velocity. The proposed method can show higher-resolution images of velocity changes of two different time set data with a small number of CMP traces. YAKUMO can acquire several parallel distributed CMP Datasets at each position while moving the system linearly in real time, which makes it possible to apply this method to large-scale inspections. In this research, we Acquired Datasets at an airport taxi-way model with artificial damages at several times over a year. The Datasets are analyzed with the proposed method to find out how the conditions of pavement changes by pressurization and time course. The results show that the method can detect not only overall change but also local area changes.
Khaled Z. Abd-elmoniem - One of the best experts on this subject based on the ideXlab platform.
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ISBI - TruHARP: single breath-hold MRI for high resolution cardiac motion and strain quantification
Proceedings. IEEE International Symposium on Biomedical Imaging, 2009Co-Authors: Harsh K. Agarwal, Khaled Z. Abd-elmoniem, Jerry L. PrinceAbstract:MRI techniques for tissue motion and strain quantifications have limited resolution because of interference from the conjugate echo or spectral peak in Fourier space. Methods have been proposed to remove this interference in order to improve resolution; however, these methods are clinically impractical due to long image acquisition time. In this paper, we propose TruHARP, an MRI motion and strain quantification methodology that involves a novel single breath-hold imaging protocol. In post-processing, TruHARP separates the spectral peaks in the Acquired Datasets, enabling high resolution motion and strain quantification. The impact of high resolution on circumferential and radial strain is studied using a realistic simulation and the improvement in strain maps is demonstrated in an in-vivo human study.
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TruHARP: single breath-hold MRI for high resolution cardiac motion and strain quantification
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009Co-Authors: Harsh K. Agarwal, Khaled Z. Abd-elmoniem, Jerry L. PrinceAbstract:MRI techniques for tissue motion and strain quantifications have limited resolution because of interference from the conjugate echo or spectral peak in Fourier space. Methods have been proposed to remove this interference in order to improve resolution; however, these methods are clinically impractical due to long image acquisition time. In this paper, we propose TruHARP, an MRI motion and strain quantification methodology that involves a novel single breath-hold imaging protocol. In post-processing, TruHARP separates the spectral peaks in the Acquired Datasets, enabling high resolution motion and strain quantification. The impact of high resolution on circumferential and radial strain is studied using a realistic simulation and the improvement in strain maps is demonstrated in an in-vivo human study.
Alessandro Giusti - One of the best experts on this subject based on the ideXlab platform.
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Learning Long-Range Perception Using Self-Supervision From Short-Range Sensors and Odometry
IEEE Robotics and Automation Letters, 2019Co-Authors: Mirko Nava, Jérôme Guzzi, Omar R. Chavez-garcia, Luca M. Gambardella, Alessandro GiustiAbstract:We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera). We assume that the former is directly related to some piece of information to be perceived (such as the presence of an obstacle in a given position), whereas the latter is information rich but hard to interpret directly. We instantiate and implement the approach on a small mobile robot to detect obstacles at various distances using the video stream of the robot's forward-pointing camera, by training a convolutional neural network on automatically-Acquired Datasets. We quantitatively evaluate the quality of the predictions on unseen scenarios, qualitatively evaluate robustness to different operating conditions, and demonstrate usage as the sole input of an obstacle-avoidance controller. We additionally instantiate the approach on a different simulated scenario with complementary characteristics, to exemplify the generality of our contribution.
