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2 results on '"Wu, Rongli"'

1. Effects of reconstruction technique on the quality of abdominal CT angiography: A comparison between forward projected model-based iterative reconstruction solution (FIRST) and conventional reconstruction methods.

Author

Wu, Rongli, Hori, Masatoshi, Onishi, Hiromitsu, Nakamoto, Atsushi, Fukui, Hideyuki, Ota, Takashi, Tomiyama, Noriyuki, Nishida, Takuya, Enchi, Yukihiro, and Satoh, Kazuhiko

Subjects
*COMPUTED tomography, *ABDOMINAL radiography, *ANGIOGRAPHY, *ABDOMINAL aorta, *SIGNAL-to-noise ratio
Abstract

Purpose: This study aimed to compare the quality of abdominal CT angiography (CTA) images obtained using conventional reconstruction algorithms with those obtained using a novel iterative algorithm (forward projected model-based iterative reconstruction solution, FIRST) for evaluating arteries in the abdomen.Materials and Methods: Abdominal CTA images from 60 patients (M:F = 27:33; mean age, 62.4 ± 16.7 years) were reconstructed using three algorithms - filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR 3D), and FIRST. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in the abdominal aorta, celiac trunk, superior mesenteric, renal, and right hepatic arteries were objectively evaluated via region-of-interest analysis and compared using the one-way analysis of variance test. Two radiologists independently scored and selected the best (score 3), second best (score 2), and the worst (score 1) images based on the visual image quality of peripheral arteries. Wilcoxon signed rank test was used for comparing image quality scores.Results: FIRST and AIDR 3D significantly reduced image noise compared with FBP (P < 0.001). SNR and CNR were significantly higher with AIDR 3D and FIRST than with FBP reconstruction (P < 0.001), with FIRST displaying the highest CNR (14.31 ± 4.17) for the right hepatic artery than the other two methods (P < 0.05). Both radiologists scored FIRST images as having the best image quality among the three methods for peripheral abdominal artery evaluation (3.0 ± 0.0, P < 0.001).Conclusion: FIRST reconstruction yielded superior abdominal CTA images as compared with FBP or AIDR 3D reconstruction. [ABSTRACT FROM AUTHOR]

Published
2018
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2. Utilizing Unlabeled Data to Detect Electricity Fraud in AMI: A Semisupervised Deep Learning Approach.

Author

Hu, Tianyu, Guo, Qinglai, Shen, Xinwei, Sun, Hongbin, Wu, Rongli, and Xi, Haoning

Subjects
*FRAUD, *DEEP learning, *SUPERVISED learning, *ELECTRICITY, *ARCHITECTURE
Abstract

As nontechnical losses in power systems have recently become a global concern, electricity fraud detection models attracted increasing academic interest. The wide application of smart meters has offered more possibility to detecting fraud from user’s consumption patterns. However, the performances of existing consumption-based electricity fraud detection models are still not satisfactory enough for practice, partly due to their limited ability to handle high-dimensional data. In this paper, a deep-learning-based model is developed for detecting electricity fraud in the advanced metering infrastructure, namely, the multitask feature extracting fraud detector (MFEFD). The deep architecture has brought MFEFD a powerful ability to handle high-dimensional input, through which consumption patterns inside load profiles can be effectively extracted. Another challenge is that the insufficiency of labeled data has restricted the generalization of existing models since they are mostly based on supervised learning and labeled data. MFEFD is trained in a semisupervised manner, in which multitask training was implemented to combine the supervised and unsupervised training, so that both the knowledge from unlabeled and labeled data can be made use of. Real-world-data-based case studies have demonstrated MFEFD’s high detection performance, robustness, privacy preservation, and practicability. [ABSTRACT FROM AUTHOR]

Published
2019
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