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Article ; Online: Accelerated Black-Blood Cine MR Imaging with Low-Rank and Sparsity Constraints.

Sun, Aiqi / Lu, Hengfa / Wu, Peng / Zhao, Bo

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

2023 Volume 2023, Page(s) 1–4

Abstract: Black-blood MRI is a promising imaging technique for assessing vascular diseases (e.g., stroke). Vessel wall dynamic characterization using black-blood cine MRI has been recognized as an effective tool for studying vascular diseases. However, acquiring ... ...

Abstract	Black-blood MRI is a promising imaging technique for assessing vascular diseases (e.g., stroke). Vessel wall dynamic characterization using black-blood cine MRI has been recognized as an effective tool for studying vascular diseases. However, acquiring time-resolved 3D vessel wall images often requires a long acquisition time, which limits its clinical utility. In this work, we develop a new method to achieve rapid, time-resolved 3D black-blood cine MRI. Specifically, the proposed method performs (k, t)-space undersampling to accelerate the volumetric data acquisition process. Moreover, it utilizes an image reconstruction method with low-rank and sparsity constraints to enable high-quality image reconstruction from highly-undersampled data. We validate the performance of the proposed method with 3D in vivo black-blood cine MRI experiments and show representative results to demonstrate the utility of the proposed method.
MeSH term(s)	Humans ; Image Interpretation, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Magnetic Resonance Imaging, Cine/methods ; Image Processing, Computer-Assisted/methods ; Stroke
Language	English
Publishing date	2023-12-11
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC40787.2023.10340783
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Improved Balanced Steady-State Free Precession Based MR Fingerprinting with Deep Autoencoders.

Lu, Hengfa / Ye, Huihui / Zhao, Bo

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

2022 Volume 2022, Page(s) 3029–3034

Abstract: Magnetic Resonance (MR) Fingerprinting is an emerging transient-state imaging paradigm, which enables the quantization of multiple MR tissue parameters in a single experiment. Balanced steady-state free precession (bSSFP)-based MR Fingerprinting has ... ...

Abstract	Magnetic Resonance (MR) Fingerprinting is an emerging transient-state imaging paradigm, which enables the quantization of multiple MR tissue parameters in a single experiment. Balanced steady-state free precession (bSSFP)-based MR Fingerprinting has excellent signal-to-noise characteristics and also allows for acquiring both tissue parameter maps and field inhomogeneity maps. However, field inhomogeneity often results in complex magnetization evolutions in bSSFP-based MR Fingerprinting, which creates significant challenges in image reconstruction. In this paper, we introduce a new method to address the image reconstruction problem. The proposed method incorporates a low-dimensional nonlinear manifold learned from an ensemble of magnetization evolutions using a deep autoencoder. It provides much better representation power than a low-dimensional linear subspace in capturing complex magnetization evolutions. We formulate the image reconstruction problem with this nonlinear model and solve the resulting optimization problem using an algorithm based on variable splitting and the alternating direction method of multipliers. We evaluate the performance of the proposed method using numerical experiments and demonstrate that it significantly outperforms the state-of-art method using a linear subspace model.
MeSH term(s)	Algorithms ; Brain ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Magnetic Resonance Spectroscopy
Language	English
Publishing date	2022-09-09
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC48229.2022.9871003
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Book ; Online: Accelerated MR Fingerprinting with Low-Rank and Generative Subspace Modeling

Lu, Hengfa / Ye, Huihui / Wald, Lawrence L. / Zhao, Bo

2023

Abstract: Magnetic Resonance (MR) Fingerprinting is an emerging multi-parametric quantitative MR imaging technique, for which image reconstruction methods utilizing low-rank and subspace constraints have achieved state-of-the-art performance. However, this class ... ...

Abstract	Magnetic Resonance (MR) Fingerprinting is an emerging multi-parametric quantitative MR imaging technique, for which image reconstruction methods utilizing low-rank and subspace constraints have achieved state-of-the-art performance. However, this class of methods often suffers from an ill-conditioned model-fitting issue, which degrades the performance as the data acquisition lengths become short and/or the signal-to-noise ratio becomes low. To address this problem, we present a new image reconstruction method for MR Fingerprinting, integrating low-rank and subspace modeling with a deep generative prior. Specifically, the proposed method captures the strong spatiotemporal correlation of contrast-weighted time-series images in MR Fingerprinting via a low-rank factorization. Further, it utilizes an untrained convolutional generative neural network to represent the spatial subspace of the low-rank model, while estimating the temporal subspace of the model from simulated magnetization evolutions generated based on spin physics. Here the architecture of the generative neural network serves as an effective regularizer for the ill-conditioned inverse problem without additional spatial training data that are often expensive to acquire. The proposed formulation results in a non-convex optimization problem, for which we develop an algorithm based on variable splitting and alternating direction method of multipliers.We evaluate the performance of the proposed method with numerical simulations and in vivo experiments and demonstrate that the proposed method outperforms the state-of-the-art low-rank and subspace reconstruction.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing
Subject code	006
Publishing date	2023-05-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: An efficient approach to optimal experimental design for magnetic resonance fingerprinting with B-splines.

Scope Crafts, Evan / Lu, Hengfa / Ye, Huihui / Wald, Lawrence L / Zhao, Bo

Magnetic resonance in medicine

2022 Volume 88, Issue 1, Page(s) 239–253

Abstract: Purpose: To introduce a computationally efficient approach to optimizing the data acquisition parameters of MR Fingerprinting experiments with the Cramér-Rao bound.: Methods: This paper presents a new approach to the optimal experimental design (OED) ...

Abstract	Purpose: To introduce a computationally efficient approach to optimizing the data acquisition parameters of MR Fingerprinting experiments with the Cramér-Rao bound. Methods: This paper presents a new approach to the optimal experimental design (OED) problem for MR Fingerprinting, which leverages an early observation that the optimized data acquisition parameters of MR Fingerprinting experiments are highly structured. Specifically, the proposed approach captures the desired structure by representing the sequences of data acquisition parameters with a special class of piecewise polynomials known as B-splines. This incorporates low-dimensional spline subspace constraints into the OED problem, which significantly reduces the search space of the problem, thereby improving the computational efficiency. With the rich B-spline representations, the proposed approach also allows for incorporating prior knowledge on the structure of different acquisition parameters, which facilitates the experimental design. Results: The effectiveness of the proposed approach was evaluated using numerical simulations, phantom experiments, and in vivo experiments. The proposed approach achieves a two-order-of-magnitude improvement of the computational efficiency over the state-of-the-art approaches, while providing a comparable signal-to-noise ratio efficiency benefit. It enables an optimal experimental design problem for MR Fingerprinting with a typical acquisition length to be solved in approximately 1 min. Conclusions: The proposed approach significantly improves the computational efficiency of the optimal experimental design for MR Fingerprinting, which enhances its practical utility for a variety of quantitative MRI applications.
MeSH term(s)	Algorithms ; Brain/diagnostic imaging ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Magnetic Resonance Spectroscopy ; Phantoms, Imaging ; Research Design ; Signal-To-Noise Ratio
Language	English
Publishing date	2022-03-07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	605774-3
ISSN	1522-2594 ; 0740-3194
ISSN (online)	1522-2594
ISSN	0740-3194
DOI	10.1002/mrm.29212
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Article ; Online: A guaranteed convergence analysis for the projected fast iterative soft-thresholding algorithm in parallel MRI.

Zhang, Xinlin / Lu, Hengfa / Guo, Di / Bao, Lijun / Huang, Feng / Xu, Qin / Qu, Xiaobo

Medical image analysis

2021 Volume 69, Page(s) 101987

Abstract: Sparse sampling and parallel imaging techniques are two effective approaches to alleviate the lengthy magnetic resonance imaging (MRI) data acquisition problem. Promising data recoveries can be obtained from a few MRI samples with the help of sparse ... ...

Abstract	Sparse sampling and parallel imaging techniques are two effective approaches to alleviate the lengthy magnetic resonance imaging (MRI) data acquisition problem. Promising data recoveries can be obtained from a few MRI samples with the help of sparse reconstruction models. To solve the optimization models, proper algorithms are indispensable. The pFISTA, a simple and efficient algorithm, has been successfully extended to parallel imaging. However, its convergence criterion is still an open question. Besides, the existing convergence criterion of single-coil pFISTA cannot be applied to the parallel imaging pFISTA, which, therefore, imposes confusions and difficulties on users about determining the only parameter - step size. In this work, we provide the guaranteed convergence analysis of the parallel imaging version pFISTA to solve the two well-known parallel imaging reconstruction models, SENSE and SPIRiT. Along with the convergence analysis, we provide recommended step size values for SENSE and SPIRiT reconstructions to obtain fast and promising reconstructions. Experiments on in vivo brain images demonstrate the validity of the convergence criterion.
MeSH term(s)	Algorithms ; Brain/diagnostic imaging ; Humans ; Image Enhancement ; Image Interpretation, Computer-Assisted ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Reproducibility of Results
Language	English
Publishing date	2021-02-01
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2021.101987
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Article ; Online: Accelerated MRI Reconstruction With Separable and Enhanced Low-Rank Hankel Regularization.

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

IEEE transactions on medical imaging

2022 Volume 41, Issue 9, Page(s) 2486–2498

Abstract: 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 ... ...

Abstract	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 structured low-rank methods use the 2D or higher dimension k-space data to build a huge block Hankel matrix, leading to considerable time and memory consumption. To reduce the size of the Hankel matrix, we proposed to separably construct multiple small Hankel matrices from rows and columns of the k-space and then constrain the low-rankness on these small matrices. This separable model can significantly reduce the computational time but ignores the correlation existed in inter- and intra-row or column, resulting in increased reconstruction error. To improve the reconstructed image without obviously increasing the computation, we further introduced the self-consistency of k-space and virtual coil prior. Besides, the proposed separable model can be extended into other imaging scenarios which hold exponential characteristics in the parameter dimension. The in vivo experimental results demonstrated that the proposed method permits the lowest reconstruction error with a fast reconstruction. The proposed approach requires only 4% of the state-of-the-art STDLR-SPIRiT runtime for parallel imaging reconstruction, and achieves the fastest computational speed in parameter imaging reconstruction.
MeSH term(s)	Algorithms ; Image Enhancement/methods ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods
Language	English
Publishing date	2022-08-31
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2022.3164472
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Article ; Online: Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning.

Qu, Xiaobo / Huang, Yihui / Lu, Hengfa / Qiu, Tianyu / Guo, Di / Agback, Tatiana / Orekhov, Vladislav / Chen, Zhong

Angewandte Chemie (International ed. in English)

2020 Volume 59, Issue 26, Page(s) 10297–10300

Abstract: Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof-of-concept of the application of deep learning and neural networks for high-quality, ...

Abstract	Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof-of-concept of the application of deep learning and neural networks for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.
Language	English
Publishing date	2020-04-15
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2011836-3
ISSN	1521-3773 ; 1433-7851
ISSN (online)	1521-3773
ISSN	1433-7851
DOI	10.1002/anie.201908162
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Book ; Online: Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization

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

2021

Abstract: The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time. However, the low-rank structured ... ...

Abstract	The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time. However, the low-rank structured approaches demand considerable memory consumption and are time-consuming due to a noticeable number of matrix operations performed on the huge-size block Hankel-like matrix. In this work, we proposed a novel framework to utilize the low-rank property but meanwhile to achieve faster reconstructions and promising results. The framework allows us to enforce the low-rankness of Hankel matrices constructing from 1D vectors instead of 2D matrices from 1D vectors and thus avoid the construction of huge block Hankel matrix for 2D k-space matrices. Moreover, under this framework, we can easily incorporate other information, such as the smooth phase of the image and the low-rankness in the parameter dimension, to further improve the image quality. We built and validated two models for parallel and parameter magnetic resonance imaging experiments, respectively. Our retrospective in-vivo results indicate that the proposed approaches enable faster reconstructions than the state-of-the-art approaches, e.g., about 8x faster than STDLRSPIRiT, and faithful removal of undersampling artifacts. Comment: 17 pages, 17 figures
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Electrical Engineering and Systems Science - Signal Processing
Subject code	006
Publishing date	2021-07-24
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A Guaranteed Convergence Analysis for the Projected Fast Iterative Soft-Thresholding Algorithm in Parallel MRI

Zhang, Xinlin / Lu, Hengfa / Guo, Di / Bao, Lijun / Huang, Feng / Xu, Qin / Qu, Xiaobo

2019

Abstract: The boom of non-uniform sampling and compressed sensing techniques dramatically alleviates the lengthy data acquisition problem of magnetic resonance imaging. Sparse reconstruction, thanks to its fast computation and promising performance, has attracted ... ...

Abstract	The boom of non-uniform sampling and compressed sensing techniques dramatically alleviates the lengthy data acquisition problem of magnetic resonance imaging. Sparse reconstruction, thanks to its fast computation and promising performance, has attracted researchers to put numerous efforts on it and has been adopted in commercial scanners. To perform sparse reconstruction, choosing a proper algorithm is essential in providing satisfying results and saving time in tuning parameters. The pFISTA, a simple and efficient algorithm for sparse reconstruction, has been successfully extended to parallel imaging. However, its convergence criterion is still an open question. And the existing convergence criterion of single-coil pFISTA cannot be applied to the parallel imaging pFISTA, which, therefore, imposes confusions and difficulties on users about determining the only parameter - step size. In this work, we provide the guaranteed convergence analysis of the parallel imaging version pFISTA to solve the two well-known parallel imaging reconstruction models, SENSE and SPIRiT. Along with the convergence analysis, we provide recommended step size values for SENSE and SPIRiT reconstructions to obtain fast and promising reconstructions. Experiments on in vivo brain images demonstrate the validity of the convergence criterion. Besides, experimental results show that compared to using backtracking and power iteration to determine the step size, our recommended step size achieves more than five times acceleration in reconstruction time in most tested cases. Comment: Main text: 13 pages, 10 figures. Supporting material: 5 pages, 5 figures
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Mathematics - Optimization and Control ; Physics - Medical Physics
Subject code	006
Publishing date	2019-09-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Qu, Xiaobo / Huang, Yihui / Lu, Hengfa / Qiu, Tianyu / Guo, Di / Orekhov, Vladislav / Chen, Zhong

2019

Abstract: Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept of harnessing deep learning and neural network for high-quality, reliable, ...

Abstract	Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept of harnessing deep learning and neural network for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signal, which lifts the prohibiting demand for large volume of realistic training data usually required in the deep learning approach. Comment: 4 figures
Keywords	Physics - Medical Physics ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; Mathematics - Spectral Theory ; Physics - Biological Physics
Publishing date	2019-04-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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