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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
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
High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models
Ruiyang Zhao;Xi Peng;Varun A. Kelkar;Mark A. Anastasio;Fan Lam
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 Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions
IEEE Signal Processing Magazine
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
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
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 image reconstruction with separable Hankel regularization in parallel MRI
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
Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models
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
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
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
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
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
Reference guided CS-MRI with gradient orientation priors
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
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
MR Image Reconstruction with Convolutional Characteristic Constraint (CoCCo)
IEEE Signal Processing Letters
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
Accurate T2 mapping with sparsity and linear predictability filtering
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
Adaptive Dictionary Learning in Sparse Gradient Domain for Image Recovery
Qiegen Liu;Shanshan Wang;Leslie Ying;Xi Peng;Yanjie Zhu;Dong Liang
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
Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations
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
Interventional MRI with sparse sampling using union-of-subspaces
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
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
Spatiotemporal denoising of MR spectroscopic imaging data by low-rank approximations
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
Reference-driven MR image reconstruction with sparsity and support constraints
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
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 |
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
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