Loading [a11y]/accessibility-menu.js

Xinglong Wu - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Xinglong Wu

Also published under: X. Wu

Publications

45

Citations

166

Publications by Year

20122025

Co-Authors:

Faryal BaigAbduselam Hamid BeshirYonghong ChengRiccardo ChiumeoPaolo S. Crovetti

Show All Co-Authors (47)

Xinglong Wu

Also published under: X. Wu

Affiliation

Department of Electronics

Information and Bioengineering

Politecnico di Milano, Milan, Italy

Publication Topics

Electromagnetic Compatibility,
Circuit Model,
Electromagnetic Interference,
Equivalent Circuit,
Common-mode Current,
Test Setup,
Common-mode Noise,
Electric Dipole,
Mode Conversion,
Printed Circuit Board,
Probe Test,
Switching Frequency

Biography

Xinglong Wu received the Bachelor's degree in electrical engineering (EE) from Xi'an Jiaotong University, Xi'an, China, in 2012 and the Double Master (M.Sc.) Degree in EE from Xi'an Jiaotong University and Politecnico di Milano, Milan, Italy, in 2015. He is currently working toward the Ph.D. degree in EE at Politecnico di Milano.,His research interests include distributed parameter circuit modeling for interference prediction, statistical techniques for electromagnetic compatibility (EMC), and system-level EMC.(Based on document published on 14 April 2017).

Publications

45

Citations

166

Publications by Year

20122025

Co-Authors:

Faryal Baig
Abduselam Hamid Beshir
Yonghong Cheng
Riccardo Chiumeo
Paolo S. Crovetti

Show All Co-Authors (47)

Author's Published Works

Search History

Showing 1-20 of 20 results

Conferences (11)

Journals (8)

Magazines (1)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Xi Peng(20)
Zhi-Pei Liang(8)
Dong Liang(7)
Fan Lam(5)
Shanshan Wang(4)
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China(2)
Neusoft Medical System, Shanghai, China(2)
Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, China(2)
Shenzhen Key Laboratory for MRI, Chinese Academy of Sciences, Shenzhen, Guangdong, China(2)
Department of Radiology, University of Iowa, USA(2)
IEEE Transactions on Biomedical Engineering(3)
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro(2)
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)(2)
IEEE Transactions on Medical Imaging(2)
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology(1)
IEEE(20)
Media(3)
Chicago, IL, USA(2)
Milan, Italy(2)
Barcelona, Spain(1)
Beijing, China(1)
Boston, MA, USA(1)
Image Reconstruction(11)
K-space Data(9)
Magnetic Resonance Imaging(9)
Reference Image(9)
Magnetic Resonance Spectroscopic Imaging(6)

Select All on Page

Sort By

Results

Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction

Yue Guan;Yudu Li;Ziwen Ke;Xi Peng;Ruihao Liu;Yao Li;Yiping P. Du;Zhi-Pei Liang

IEEE Transactions on Biomedical Engineering

Year: 2024 | Volume: 71, Issue: 7 | Journal Article |

HTML

Objective: To leverage machine learning (ML) for fast selection of optimal regularization parameter in constrained image reconstruction. Methods: Constrained image reconstruction is often formulated as a regularization problem and selecting a good regularization parameter value is an essential step. We solved this problem using an ML-based approach by leveraging the finding that for a specific con...Show More

Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction

Yue Guan;Yudu Li;Ziwen Ke;Xi Peng;Ruihao Liu;Yao Li;Yiping P. Du;Zhi-Pei Liang

IEEE Transactions on Biomedical Engineering

Year: 2024 | Volume: 71, Issue: 7 | Journal Article |

High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models

Ruiyang Zhao;Xi Peng;Varun A. Kelkar;Mark A. Anastasio;Fan Lam

IEEE Transactions on Biomedical Engineering

Year: 2024 | Volume: 71, Issue: 6 | Journal Article |

Cited by: Papers (2)

HTML

Objective: To develop a new method that integrates subspace and generative image models for high-dimensional MR image reconstruction. Methods: We proposed a formulation that synergizes a low-dimensional subspace model of high-dimensional images, an adaptive generative image prior serving as spatial constraints on the sequence of “contrast-weighted” images or spatial coefficients of the subspace mo...Show More

High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models

Ruiyang Zhao;Xi Peng;Varun A. Kelkar;Mark A. Anastasio;Fan Lam

IEEE Transactions on Biomedical Engineering

Year: 2024 | Volume: 71, Issue: 6 | Journal Article |

High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions

Fan Lam;Xi Peng;Zhi-Pei Liang

IEEE Signal Processing Magazine

Year: 2023 | Volume: 40, Issue: 2 | Magazine Article |

Cited by: Papers (5)

HTML

Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limited by a number of long-standing technical challenges due to the high dimensionality and low signal-to-noise ratio (SNR). Recent technological developments integrating physics-based modeling and data-dr...Show More

High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions

Fan Lam;Xi Peng;Zhi-Pei Liang

IEEE Signal Processing Magazine

Year: 2023 | Volume: 40, Issue: 2 | Magazine Article |

Accelerated MRI Reconstruction With Separable and Enhanced Low-Rank Hankel Regularization

Xinlin Zhang;Hengfa Lu;Di Guo;Zongying Lai;Huihui Ye;Xi Peng;Bo Zhao;Xiaobo Qu

IEEE Transactions on Medical Imaging

Year: 2022 | Volume: 41, Issue: 9 | Journal Article |

Cited by: Papers (21)

HTML

Magnetic resonance imaging serves as an essential tool for clinical diagnosis, however, suffers from a long acquisition time. Sparse sampling effectively saves this time but images need to be faithfully reconstructed from undersampled data. Among the existing reconstruction methods, the structured low-rank methods have advantages in robustness to the sampling patterns and lower error. However, the...Show More

Accelerated MRI Reconstruction With Separable and Enhanced Low-Rank Hankel Regularization

Xinlin Zhang;Hengfa Lu;Di Guo;Zongying Lai;Huihui Ye;Xi Peng;Bo Zhao;Xiaobo Qu

IEEE Transactions on Medical Imaging

Year: 2022 | Volume: 41, Issue: 9 | Journal Article |

Accelerated image reconstruction with separable Hankel regularization in parallel MRI

Xinlin Zhang;Zi Wang;Xi Peng;Qin Xu;Di Guo;Xiaobo Qu

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2021 | Conference Paper |

HTML

Magnetic resonance imaging has been widely adopted in clinical diagnose, however, it suffers from relatively long data acquisition time. Sparse sampling with reconstruction can speed up the data acquisition duration. As the state-of-the-art magnetic resonance imaging methods, the structured low rank reconstruction approaches embrace the advantage of holding low reconstruction errors and permit fle...Show More

Accelerated image reconstruction with separable Hankel regularization in parallel MRI

Xinlin Zhang;Zi Wang;Xi Peng;Qin Xu;Di Guo;Xiaobo Qu

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Year: 2021 | Conference Paper |

Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models

Fan Lam;Yahang Li;Xi Peng

IEEE Transactions on Medical Imaging

Year: 2020 | Volume: 39, Issue: 3 | Journal Article |

Cited by: Papers (35)

HTML

Magnetic resonance spectroscopic imaging (MRSI) is a powerful molecular imaging modality but has very limited speed, resolution, and SNR tradeoffs. Construction of a low-dimensional model to effectively reduce the dimensionality of the imaging problem has recently shown great promise in improving these tradeoffs. This paper presents a new approach to model and reconstruct the spectroscopic signals...Show More

Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models

Fan Lam;Yahang Li;Xi Peng

IEEE Transactions on Medical Imaging

Year: 2020 | Volume: 39, Issue: 3 | Journal Article |

Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement

Hong Zheng;Kun Zeng;Di Guo;Jiaxi Ying;Yu Yang;Xi Peng;Feng Huang;Zhong Chen;Xiaobo Qu

Year: 2018 | Volume: 6 | Journal Article |

Cited by: Papers (39)

HTML

In magnetic resonance imaging (MRI), the super-resolution technology has played a great role in improving image quality. The aim of this paper is to improve edges of brain MRI by incorporating the gradient information of another contrast high-resolution image. Multi-contrast images are assumed to possess the same gradient direction in a local pattern. We proposed to establish a relation model of g...Show More

Multi-Contrast Brain MRI Image Super-Resolution With Gradient-Guided Edge Enhancement

Hong Zheng;Kun Zeng;Di Guo;Jiaxi Ying;Yu Yang;Xi Peng;Feng Huang;Zhong Chen;Xiaobo Qu

Year: 2018 | Volume: 6 | Journal Article |

Accelerating magnetic resonance imaging via deep learning

Shanshan Wang;Zhenghang Su;Leslie Ying;Xi Peng;Shun Zhu;Feng Liang;Dagan Feng;Dong Liang

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

Year: 2016 | Conference Paper |

Cited by: Papers (510) | Patents (5)

HTML

This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restor...Show More

Accelerating magnetic resonance imaging via deep learning

Shanshan Wang;Zhenghang Su;Leslie Ying;Xi Peng;Shun Zhu;Feng Liang;Dagan Feng;Dong Liang

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

Year: 2016 | Conference Paper |

An easily-achieved time-domain beamformer for ultrafast ultrasound imaging based on compressive sensing

Congzhi Wang;Xi Peng;Dong Liang;Yang Xiao;Weibao Qiu;Ming Qian;Hairong Zheng

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2015 | Conference Paper |

HTML

In ultrafast ultrasound imaging technique, how to maintain the high frame rate, and at the same time to improve the image quality as far as possible, has become a significant issue. Several novel beamforming methods based on compressive sensing (CS) theory have been proposed in previous literatures, but all have their own limitations, such as the excessively large memory consumption and the errors...Show More

An easily-achieved time-domain beamformer for ultrafast ultrasound imaging based on compressive sensing

Congzhi Wang;Xi Peng;Dong Liang;Yang Xiao;Weibao Qiu;Ming Qian;Hairong Zheng

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2015 | Conference Paper |

Reference guided CS-MRI with gradient orientation priors

Xi Peng;Qingyong Zhu;Shanshan Wang;Dong Liang

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2015 | Conference Paper |

HTML

The theory of Compressed sensing (CS) provides a systematic framework for MR image reconstruction from under-sampled k-space data. However, severe aliasing artifacts still occurs in the case of high acceleration. Thereupon, an extensive body of works investigates exploiting additional prior information extracted from a reference image which can be acquired with relative ease in many MR application...Show More

Reference guided CS-MRI with gradient orientation priors

Xi Peng;Qingyong Zhu;Shanshan Wang;Dong Liang

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year: 2015 | Conference Paper |

Parallel imaging via sparse representation over a learned dictionary

Shanshan Wang;Xi Peng;Pei Dong;Leslie Ying;David Dagan Feng;Dong Liang

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

Year: 2015 | Conference Paper |

Cited by: Papers (6)

HTML

This paper proposes an adaptive reconstruction method for parallel imaging (PI) via sparse representation over a learned dictionary and also a corresponding dictionary learning based PI (DL-PI) algorithm. DL-PI adopts the “divide and conquer” strategy to solve the ℓ2-DL reconstruction formulation, with dictionary learning to capture the structure information and a Taylor approximation to update th...Show More

Parallel imaging via sparse representation over a learned dictionary

Shanshan Wang;Xi Peng;Pei Dong;Leslie Ying;David Dagan Feng;Dong Liang

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

Year: 2015 | Conference Paper |

MR Image Reconstruction with Convolutional Characteristic Constraint (CoCCo)

Xi Peng;Dong Liang

IEEE Signal Processing Letters

Year: 2015 | Volume: 22, Issue: 8 | Journal Article |

Cited by: Papers (12)

HTML

The problem of recovering an image from limited or sparsely sampled Fourier measurements occurs in the application of magnetic resonance imaging. To address this problem, we propose a novel MR image reconstruction method with convolutional characteristic constraints. We first estimate the convolutional characteristics using standard compressed sensing method in a parallel fashion. Then we use the ...Show More

MR Image Reconstruction with Convolutional Characteristic Constraint (CoCCo)

Xi Peng;Dong Liang

IEEE Signal Processing Letters

Year: 2015 | Volume: 22, Issue: 8 | Journal Article |

Accurate T2 mapping with sparsity and linear predictability filtering

Xi Peng;Leslie Ying;Xin Liu;Dong Liang

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

Year: 2014 | Conference Paper |

Cited by: Papers (4)

HTML

T2 mapping provides a quantitative manner to access tissue structure, composition, water content and iron levels. Nevertheless, due to the relative long scanning time, its practical usage is limited. This paper addresses this problem using a novel iterative nonlinear filtering method to achieve sparse sampling reconstruction. Specifically two filters are involved. One is the soft thresholding oper...Show More

Accurate T2 mapping with sparsity and linear predictability filtering

Xi Peng;Leslie Ying;Xin Liu;Dong Liang

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

Year: 2014 | Conference Paper |

Adaptive Dictionary Learning in Sparse Gradient Domain for Image Recovery

Qiegen Liu;Shanshan Wang;Leslie Ying;Xi Peng;Yanjie Zhu;Dong Liang

IEEE Transactions on Image Processing

Year: 2013 | Volume: 22, Issue: 12 | Journal Article |

Cited by: Papers (94)

HTML

Image recovery from undersampled data has always been challenging due to its implicit ill-posed nature but becomes fascinating with the emerging compressed sensing (CS) theory. This paper proposes a novel gradient based dictionary learning method for image recovery, which effectively integrates the popular total variation (TV) and dictionary learning technique into the same framework. Specifically...Show More

Adaptive Dictionary Learning in Sparse Gradient Domain for Image Recovery

Qiegen Liu;Shanshan Wang;Leslie Ying;Xi Peng;Yanjie Zhu;Dong Liang

IEEE Transactions on Image Processing

Year: 2013 | Volume: 22, Issue: 12 | Journal Article |

Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations

Hien M. Nguyen;Xi Peng;Minh N. Do;Zhi-Pei Liang

IEEE Transactions on Biomedical Engineering

Year: 2013 | Volume: 60, Issue: 1 | Journal Article |

Cited by: Papers (125) | Patents (2)

HTML

This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where signal-to-noise ratio (SNR) has been a critical problem. A new scheme is proposed, which exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other due to linear predictability. Denoising is performed by arranging the measured data in approp...Show More

Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations

Hien M. Nguyen;Xi Peng;Minh N. Do;Zhi-Pei Liang

IEEE Transactions on Biomedical Engineering

Year: 2013 | Volume: 60, Issue: 1 | Journal Article |

Interventional MRI with sparse sampling using union-of-subspaces

S. Derin Babacan;Fan Lam;Xi Peng;Minh N. Do;Zhi-Pei Liang

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

Year: 2012 | Conference Paper |

Cited by: Papers (1)

HTML

A significant problem in interventional magnetic resonance imaging is limited imaging speed. This paper addresses this problem using a new signal model known as union-of-subspaces. This model enables an effective use of sparse sampling and prior information to significantly improve the imaging speed. The proposed method has been validated using simulations on real interventional imaging data, and ...Show More

Interventional MRI with sparse sampling using union-of-subspaces

S. Derin Babacan;Fan Lam;Xi Peng;Minh N. Do;Zhi-Pei Liang

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

Year: 2012 | Conference Paper |

Reference-guided sparsifying transform design for compressive sensing MRI

S. Derin Babacan;Xi Peng;Xian-Pei Wang;Minh N. Do;Zhi-Pei Liang

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2011 | Conference Paper |

Cited by: Papers (11) | Patents (2)

HTML

Compressive sensing (CS) MRI aims to accurately reconstruct images from undersampled k-space data. Most CS methods employ analytical sparsifying transforms such as total-variation and wavelets to model the unknown image and constrain the solution space during reconstruction. Recently, nonparametric dictionary-based methods for CS-MRI reconstruction have shown significant improvements over the clas...Show More

Reference-guided sparsifying transform design for compressive sensing MRI

S. Derin Babacan;Xi Peng;Xian-Pei Wang;Minh N. Do;Zhi-Pei Liang

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2011 | Conference Paper |

Spatiotemporal denoising of MR spectroscopic imaging data by low-rank approximations

Hien M. Nguyen;Xi Peng;Minh N. Do;Zhi-Pei Liang

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2011 | Conference Paper |

Cited by: Papers (8)

HTML

This paper addresses the denoising problem associated with magnetic resonance spectroscopic imaging (MRSI), where low signal-to-noise ratio (SNR) has been a critical problem. A new scheme is proposed, which exploits two low-rank structures that exist in MRSI data, one due to partial separability and the other is due to linear predictability. Experimental results from practical data demonstrate tha...Show More

Spatiotemporal denoising of MR spectroscopic imaging data by low-rank approximations

Hien M. Nguyen;Xi Peng;Minh N. Do;Zhi-Pei Liang

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2011 | Conference Paper |

Reference-driven MR image reconstruction with sparsity and support constraints

Xi Peng;Hui-Qian Du;Fan Lam;S. Derin Babacan;Zhi-Pei Liang

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2011 | Conference Paper |

Cited by: Papers (10)

HTML

The problem of reconstructing an MR image from limited (and sparsely sampled) k-space data in the presence of a reference image occurs in various applications, including interventional imaging and dynamic contrast-enhanced imaging. This paper addresses the problem using a dictionary composed of three types of basis functions: reference-weighted harmonic functions, wavelets, and pixel/voxel indicat...Show More

Reference-driven MR image reconstruction with sparsity and support constraints

Xi Peng;Hui-Qian Du;Fan Lam;S. Derin Babacan;Zhi-Pei Liang

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2011 | Conference Paper |

Correction of field inhomogeneity effects on limited k-space MRSI data using anatomical constraints

Xi Peng;Hien Nguyen;Justin Haldar;Diego Hernando;Xian-Pei Wang;Zhi-Pei Liang

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

Year: 2010 | Conference Paper |

Cited by: Papers (2) | Patents (1)

HTML

Magnetic field inhomogeneity is a long-standing problem in magnetic resonance imaging (MRI) and spectroscopic imaging (MRSI). Specifically, in MRSI, field inhomogeneity, if not corrected, can cause frequency shifts, line broadening, and lineshape distortions in the spectral peaks. This paper addresses the problem of correcting the field inhomogeneity effects on limited k-space MRSI data. A penaliz...Show More

Correction of field inhomogeneity effects on limited k-space MRSI data using anatomical constraints

Xi Peng;Hien Nguyen;Justin Haldar;Diego Hernando;Xian-Pei Wang;Zhi-Pei Liang

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

Year: 2010 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.