The Experts below are selected from a list of 816564 Experts worldwide ranked by ideXlab platform
Young Jun Choi - One of the best experts on this subject based on the ideXlab platform.
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prediction of pseudoprogression in patients with glioblastomas using the initial and final area under the curves ratio derived from dynamic contrast enhanced t1 weighted perfusion mr imaging
American Journal of Neuroradiology, 2013Co-Authors: Young Jun ChoiAbstract:BACKGROUND AND PURPOSE: Dynamic contrast-enhanced T1-weighted perfusion MR imaging is much less susceptible to artifacts, and its high spatial resolution allows accurate characterization of the vascular microenvironment of the lesion. The purpose of this study was to test the predictive value of the initial and final area under the Time Signal-intensity curves ratio derived from dynamic contrast-enhanced perfusion MR imaging to differentiate pseudoprogression from early tumor progression in patients with glioblastomas. MATERIALS AND METHODS: Seventy-nine consecutive patients who showed new or enlarged, contrast-enhancing lesions within the radiation field after concurrent chemoradiotherapy were assessed by use of conventional and dynamic contrast-enhanced perfusion MR imaging. The bimodal histogram parameters of the area under the Time Signal-intensity curves ratio, which included the mean area under the Time Signal-intensity curves ratio at a higher curve (mAUCRH), 3 cumulative histogram parameters (AUCR50, AUCR75, and AUCR90), and the area under the Time Signal-intensity curves ratio at mode (AUCRmode), were calculated and correlated with the final pathologic or clinical diagnosis. The best predictor for differentiation of pseudoprogression from early tumor progression was determined by receiver operating characteristic curve analyses. RESULTS: Seventy-nine study patients were subsequently classified as having pseudoprogression (n=37, 46.8%) or early tumor progression (n=42, 53.2%). There were statistically significant differences of mAUCRH, AUCR50, AUCR75, AUCR90, and AUCRmode between the 2 groups (P CONCLUSIONS: A bimodal histogram analysis of the area under the Time Signal-intensity curves ratio derived from dynamic contrast-enhanced perfusion MR imaging can be a potential, noninvasive imaging biomarker for monitoring early treatment response in patients with glioblastomas.
Choong Gon Choi - One of the best experts on this subject based on the ideXlab platform.
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recurrent glioblastoma optimum area under the curve method derived from dynamic contrast enhanced t1 weighted perfusion mr imaging
Radiology, 2013Co-Authors: Won Jung Chung, Choong Gon ChoiAbstract:The ratio of the initial to the final area under the Time–Signal intensity curve can be used as a potential noninvasive imaging biomarker for differentiating recurrent glioblastoma multiforme from radiation necrosis.
Won Jung Chung - One of the best experts on this subject based on the ideXlab platform.
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recurrent glioblastoma optimum area under the curve method derived from dynamic contrast enhanced t1 weighted perfusion mr imaging
Radiology, 2013Co-Authors: Won Jung Chung, Choong Gon ChoiAbstract:The ratio of the initial to the final area under the Time–Signal intensity curve can be used as a potential noninvasive imaging biomarker for differentiating recurrent glioblastoma multiforme from radiation necrosis.
Kehu Yang - One of the best experts on this subject based on the ideXlab platform.
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simo channel estimation using space Time Signal subspace projection and soft information
IEEE Transactions on Signal Processing, 2012Co-Authors: Shu Cai, Tad Matsumoto, Kehu YangAbstract:We consider the channel estimation of a Time-slotted wireless communication system with a mobile user and a base station, where the base station employs an M-element (M >; 1) antenna array. The uplink single-input multiple-output (SIMO) channel is usually estimated by training sequence within each Time slot. To improve the estimation performance, the channel estimate is often refined by projecting it to the corresponding spatial Signal subspace. However, this projection will not work when the number of resolvable multipath rays is larger than that of the antenna array elements, which makes the channel matrix full row rank. In this paper, we formulate the channel estimation under the space-Time Signal model for this full-row-rank case, and propose a new method by space-Time Signal subspace projection using both training and unknown data sequences. To further improve the accuracy of the channel estimate, the soft information fed back from the decoder can be used. By involving this soft information, we propose another new channel estimation method. This method approximately follows the maximum likelihood (ML) criterion and is therefore referred to as the approximated ML channel estimation. Numerical results show that these methods can be performed separately or jointly to improve the performance of channel estimation by training sequences.
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a Signal subspace based subband approach to space Time adaptive processing for mobile communications
IEEE Transactions on Signal Processing, 2001Co-Authors: Kehu Yang, Yimin Zhang, Yoshihiko MizuguchiAbstract:In this paper, we present a novel space-Time Signal subspace-based subband approach to space-Time adaptive processing (STAP) that has been shown to be an effective method to suppress both the intersymbol interference (ISI) and the cochannel interference (CCI) in mobile communications. We first study the performance of STAP and make clear the conditions of perfect processing (i.e., perfect equalization of the desired user Signal and perfect suppression of CCI Signals). Based on the polyphase representation and the subspace analysis of the Signal channels, we propose a space-Time Signal subspace-based subband approach to STAP, namely the subband STAP, which highly improves the convergence rate without loss of the steady-state performance. Simulation results show its effectiveness under the procedure of Signal subspace estimation and detection.
F Weichenmeier - One of the best experts on this subject based on the ideXlab platform.
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international comparative field evaluation of a traffic responsive Signal control strategy in three cities
Transportation Research Part A-policy and Practice, 2006Co-Authors: Elias B Kosmatopoulos, Markos Papageorgiou, Christiane Bielefeldt, Vaya Dinopoulou, R Morris, J Mueck, A Richards, F WeichenmeierAbstract:The recently developed network-wide real-Time Signal control strategy TUC has been implemented in three traffic networks with quite different traffic and control infrastructure characteristics: Chania, Greece (23 junctions); Southampton, UK (53 junctions); and Munich, Germany (25 junctions), where it has been compared to the respective resident real-Time Signal control strategies TASS, SCOOT and BALANCE. After a short outline of TUC, the paper describes the three application networks; the application, demonstration and evaluation conditions; as well as the comparative evaluation results. The main conclusions drawn from this high-effort inter-European undertaking is that TUC is an easy-to-implement, inter-operable, low-cost real-Time Signal control strategy whose performance, after very limited fine-tuning, proved to be better or, at least, similar to the ones achieved by long-standing strategies that were in most cases very well fine-tuned over the years in the specific networks.
