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Xi Peng

Publications
20
Citations
885
Publications by Year
20102024
Co-Authors:
Show All Co-Authors (52)
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Xi Peng

Affiliation

Department of Radiology, University of Iowa, USA

Publication Topics

Biography

Xi Peng received the B.E. and M.S. degrees in measurement, control and instruments and the Ph.D. degree in electrical engineering from Wuhan University, Wuhan, China, in 2006, 2008, and 2012, respectively. He was a Visiting Scholar with the Beckman Institute, University of Illinois at Urbana–Champaign, Urbana, IL, USA. He is currently an Associate Professor with the Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. His current research interests include image reconstruction, parameter estimation, and signal processing for biomedical applications.(Based on document published on 2 October 2018).
Publications
20
Citations
885
Publications by Year
20102024
Co-Authors:
Show All Co-Authors (52)
Author's Published Works

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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 |
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

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)
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

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)
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

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)
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

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 |
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

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)
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

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

IEEE Access
Year: 2018 | Volume: 6 | Journal Article |
Cited by: Papers (39)
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 |
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

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 |
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

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 |
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

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)
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

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)
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

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)
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

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)
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

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 |
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

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)
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

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 |
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

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)
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

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)
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

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)
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

Year: 2010 | Conference Paper |

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