1
|
Qu B, Zhang Z, Chen Y, Qian C, Kang T, Lin J, Chen L, Wu Z, Wang J, Zheng G, Qu X. A convergence analysis for projected fast iterative soft-thresholding algorithm under radial sampling MRI. J Magn Reson 2023; 351:107425. [PMID: 37060889 DOI: 10.1016/j.jmr.2023.107425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 02/27/2023] [Accepted: 03/17/2023] [Indexed: 05/29/2023]
Abstract
Radial sampling is a fast magnetic resonance imaging technique. Further imaging acceleration can be achieved with undersampling but how to reconstruct a clear image with fast algorithm is still challenging. Previous work has shown the advantage of removing undersampling image artifacts using the tight-frame sparse reconstruction model. This model was further solved with a projected fast iterative soft-thresholding algorithm (pFISTA). However, the convergence of this algorithm under radial sampling has not been clearly set up. In this work, the authors derived a theoretical convergence condition for this algorithm. This condition was approximated by estimating the maximal eigenvalue of reconstruction operators through the power iteration. Based on the condition, an optimal step size was further suggested to allow the fastest convergence. Verifications were made on the prospective in vivo data of static brain imaging and dynamic contrast-enhanced liver imaging, demonstrating that the recommended parameter allowed fast convergence in radial MRI.
Collapse
Affiliation(s)
- Biao Qu
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China
| | - Zuwen Zhang
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Yewei Chen
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Chen Qian
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Taishan Kang
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Jianzhong Lin
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Lihua Chen
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | | | | | - Gaofeng Zheng
- Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen, China.
| | - Xiaobo Qu
- Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
| |
Collapse
|
2
|
Shimron E, Perlman O. AI in MRI: Computational Frameworks for a Faster, Optimized, and Automated Imaging Workflow. Bioengineering (Basel) 2023; 10:bioengineering10040492. [PMID: 37106679 PMCID: PMC10135995 DOI: 10.3390/bioengineering10040492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 04/29/2023] Open
Abstract
Over the last decade, artificial intelligence (AI) has made an enormous impact on a wide range of fields, including science, engineering, informatics, finance, and transportation [...].
Collapse
Affiliation(s)
- Efrat Shimron
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
| | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| |
Collapse
|
3
|
Zhao Y, Yi Z, Liu Y, Chen F, Xiao L, Leong ATL, Wu EX. Calibrationless multi-slice Cartesian MRI via orthogonally alternating phase encoding direction and joint low-rank tensor completion. NMR Biomed 2022; 35:e4695. [PMID: 35032072 DOI: 10.1002/nbm.4695] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 10/06/2021] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
We propose a multi-slice acquisition with orthogonally alternating phase encoding (PE) direction and subsequent joint calibrationless reconstruction for accelerated multiple individual 2D slices or multi-slice 2D Cartesian MRI. Specifically, multi-slice multi-channel data are first acquired with random or uniform PE undersampling while orthogonally alternating PE direction between adjacent slices. They are then jointly reconstructed through a recently developed low-rank multi-slice Hankel tensor completion (MS-HTC) approach. The proposed acquisition and reconstruction strategy was evaluated with human brain MR data. It effectively suppressed aliasing artifacts even at high acceleration factor, outperforming the existing MS-HTC approach, where PE direction is the same between adjacent slices. More importantly, the new strategy worked robustly with uniform undersampling or random undersampling without any consecutive central k-space lines. In summary, our proposed multi-slice MRI strategy exploits both coil sensitivity and image content similarities across adjacent slices. Orthogonally alternating PE direction among slices substantially facilitates the low-rank completion process and improves image reconstruction quality. This new strategy is applicable to uniform and random PE undersampling. It can be easily implemented in practice for Cartesian parallel imaging of multiple individual 2D slices without any coil sensitivity calibration.
Collapse
Affiliation(s)
- Yujiao Zhao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Zheyuan Yi
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Yilong Liu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China
| | - Linfang Xiao
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Alex T L Leong
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| |
Collapse
|
4
|
Hong S, Hong K, Culver AE, Pathrose A, Allen BD, Wilcox JE, Lee DC, Kim D. Highly Accelerated Real-Time Free-Breathing Cine CMR for Patients With a Cardiac Implantable Electronic Device. Acad Radiol 2021; 28:1779-1786. [PMID: 32888766 DOI: 10.1016/j.acra.2020.07.041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 07/29/2020] [Accepted: 07/30/2020] [Indexed: 01/03/2023]
Abstract
RATIONALE AND OBJECTIVES To develop a 16-fold accelerated real-time, free-breathing cine cardiovascular magnetic resonance (CMR) pulse sequence with compressed sensing reconstruction and test whether it is capable of producing clinically acceptable summed visual scores (SVS) and accurate left ventricular ejection fraction (LVEF) in patients with a cardiac implantable electronic device (CIED). MATERIALS AND METHODS A 16-fold accelerated real-time cine CMR pulse sequence was developed using gradient echo readout, Cartesian k-space sampling, and compressed sensing. We scanned 13 CIED patients (mean age = 59 years; 9/4 males/females) using clinical standard, breath-hold cine and real-time, free-breathing cine. Two clinical readers performed a visual assessment of image quality in four categories (conspicuity of endocardial wall at end diastole, temporal fidelity of wall motion, any artifact level on the heart, noise) using a five-point Likert scale (1: worst; 3: clinically acceptable; 5: best). SVS was calculated as the sum of 4 individual scores, where 12 was defined as clinical acceptable. The Wilcoxon signed-rank test was performed to compare SVS, and the Bland-Altman analysis was conducted to evaluate the agreement of LVEF. RESULTS Median scan time was 3.7 times shorter for real-time (3.5 heartbeats per slice) than clinical standard (13 heartbeats per slice, excluding nonscanning time between successive breath-hold acquisitions). Median SVS was not significantly different between clinical standard (15.0) and real-time (14.5). The mean difference in LVEF was -2% (4.7% of mean), and the limits of agreement was 5.8% (13.5% of mean). CONCLUSION This study demonstrates that the proposed real-time cine method produces clinically acceptable SVS and relatively accurate LVEF in CIED patients.
Collapse
|
5
|
Koolstra K, Remis R. Learning a preconditioner to accelerate compressed sensing reconstructions in MRI. Magn Reson Med 2021; 87:2063-2073. [PMID: 34752655 PMCID: PMC9299023 DOI: 10.1002/mrm.29073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/13/2021] [Accepted: 10/20/2021] [Indexed: 01/07/2023]
Abstract
Purpose To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions. Methods A convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images. Each multichannel training example contains a simulated sampling mask, complex coil sensitivity maps, and two regularization parameter maps. The trained model was integrated in the preconditioned conjugate gradient (PCG) method as part of the split Bregman CS method. The acceleration performance was compared with that of a circulant PI‐CS preconditioner for varying undersampling factors, number of coil elements and anatomies. Results The learned preconditioner reduces the number of PCG iterations by a factor of 4, yielding a similar acceleration as an efficient circulant preconditioner. The method generalizes well to different sampling schemes, coil configurations and anatomies. Conclusion It is possible to learn adaptable preconditioners for PI and CS reconstructions that meet the performance of state‐of‐the‐art preconditioners. Further acceleration could be achieved by optimizing the network architecture and the training set. Such a preconditioner could also be integrated in fully learned reconstruction methods to accelerate the training process of unrolled networks.
Collapse
Affiliation(s)
- Kirsten Koolstra
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rob Remis
- Circuits & Systems Group, Electrical Engineering, Mathematics and Computer Science Faculty, Delft University of Technology, Delft, The Netherlands
| |
Collapse
|
6
|
Dwork N, O'Connor D, Baron CA, Johnson EMI, Kerr AB, Pauly JM, Larson PEZ. Utilizing the Wavelet Transform's Structure in Compressed Sensing. Signal Image Video Process 2021; 15:1407-1414. [PMID: 34531930 PMCID: PMC8439112 DOI: 10.1007/s11760-021-01872-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/11/2021] [Accepted: 02/08/2021] [Indexed: 06/13/2023]
Abstract
Compressed sensing has empowered quality image reconstruction with fewer data samples than previously thought possible. These techniques rely on a sparsifying linear transformation. The Daubechies wavelet transform is commonly used for this purpose. In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result. After inclusion of this affine transformation, we modify the resulting optimization problem to comply with the form of the Basis Pursuit Denoising problem. Finally, we show theoretically that this yields a lower bound on the error of the reconstruction and present results where solving this modified problem yields images of higher quality for the same sampling patterns using both magnetic resonance and optical images.
Collapse
Affiliation(s)
- Nicholas Dwork
- University of California, San Francisco, Department of Radiology and Biomedical Imaging
| | - Daniel O'Connor
- Department of Mathematics and Statistics, University of San Francisco
| | | | | | - Adam B Kerr
- Stanford University, Center for Cognitive and Neurobiological Imaging
| | - John M Pauly
- Stanford University, Department of Electrical Engineering
| | - Peder E Z Larson
- University of California, San Francisco, Department of Radiology and Biomedical Imaging
| |
Collapse
|
7
|
Zhu D, Ding H, Zviman MM, Halperin H, Schär M, Herzka DA. Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques. PLoS One 2021; 16:e0252777. [PMID: 34506496 PMCID: PMC8432823 DOI: 10.1371/journal.pone.0252777] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 05/23/2021] [Indexed: 11/18/2022] Open
Abstract
PURPOSE We aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates. METHODS Multi-volume 3D high-resolution cardiac images were acquired fully and undersampled retrospectively using 1) optimal CAIPIRINHA and 2) a variable density random (VDR) sampling. Data were reconstructed using 1) multi-volume sensitivity encoding (SENSE), 2) joint-sparsity SENSE and 3) model-based SENSE. Four metrics were calculated on 3 naïve swine and 8 normal human subjects over a whole left-ventricular region of interest: root-mean-square error (RMSE) of image signal intensity, RMSE of T2, the bias of mean T2, and standard deviation (SD) of T2. Fully sampled data and volume-by-volume SENSE with standard equally spaced undersampling were used as references. The Jaccard index calculated from one swine with acute myocardial infarction (MI) was used to demonstrate preservation of segmentation of edematous tissues with elevated T2. RESULTS In naïve swine and normal human subjects, all methods had similar performance when the net reduction factor (Rnet) <2.5. VDR sampling with model-based SENSE showed the lowest RMSEs (10.5%-14.2%) and SDs (+1.7-2.4 ms) of T2 when Rnet>2.5, while VDR sampling with the joint-sparsity SENSE had the lowest bias of mean T2 (0.0-1.1ms) when Rnet>3. The RMSEs of parametric T2 values (9.2%-24.6%) were larger than for image signal intensities (5.2%-18.4%). In the swine with MI, VDR sampling with either joint-sparsity or model-based SENSE showed consistently higher Jaccard index for all Rnet (0.71-0.50) than volume-by-volume SENSE (0.68-0.30). CONCLUSIONS Retrospective exploration of undersampling and reconstruction in 3D whole-heart T2 parametric mapping revealed that maps were more sensitive to undersampling than images, presenting a more stringent limiting factor on Rnet. The combination of VDR sampling patterns with model-based or joint-sparsity SENSE reconstructions were more robust for Rnet>3.
Collapse
Affiliation(s)
- Dan Zhu
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Haiyan Ding
- Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - M. Muz Zviman
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Radiology, Perelman School of Medicine of The University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Henry Halperin
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Michael Schär
- Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Daniel A. Herzka
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Laboratory of Cardiovascular Intervention, National Heart Lung and Blood Institute, NIH, Bethesda, Maryland, United States of America
- * E-mail:
| |
Collapse
|
8
|
Naresh NK, Malone L, Fujiwara T, Smith S, Lu Q, Twite MD, DiMaria MV, Fonseca BM, Browne LP, Barker AJ. Use of compressed sensing to reduce scan time and breath-holding for cardiac cine balanced steady-state free precession magnetic resonance imaging in children and young adults. Pediatr Radiol 2021; 51:1192-201. [PMID: 33566124 DOI: 10.1007/s00247-020-04952-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/31/2020] [Accepted: 12/20/2020] [Indexed: 11/27/2022]
Abstract
BACKGROUND Conventional pediatric volumetric MRI acquisitions of a short-axis stack typically require multiple breath-holds under anesthesia. OBJECTIVE Here, we aimed to validate a vendor-optimized compressed-sensing approach to reduce scan time during short-axis balanced steady-state free precession (bSSFP) cine imaging. MATERIALS AND METHODS Imaging was performed in 28 patients (16±9 years) in this study on a commercial 3-tesla (T) scanner using retrospective electrocardiogram-gated cine bSSFP. Cine short-axis images covering both ventricles were acquired with conventional parallel imaging and a vendor-optimized parallel imaging/compressed-sensing approach. Qualitative Likert scoring for blood-myocardial contrast, edge definition, and presence of artifact was performed by two experienced radiologists. Quantitative comparisons were performed including biventricular size and function. A paired t-test was used to detect significant differences (P<0.05). RESULTS Scan duration was 7±2 s/slice for conventional imaging (147±33 s total) vs. 4±2 s/slice for compressed sensing (83±28 s total). No significant differences were found with qualitative image scores for blood-myocardial contrast, edge definition, and presence of artifact. No significant differences were found in volumetric analysis between the two sequences. The number of breath-holds was 10±4 for conventional imaging and 5±3 for compressed sensing. CONCLUSION Compressed sensing allowed for a 50% reduction in the number of breath-holds and a 43% reduction in the total scan time without differences in the qualitative or quantitative measurements as compared to the conventional technique.
Collapse
|
9
|
Murad M, Jalil A, Bilal M, Ikram S, Ali A, Khan B, Mehmood K. Radial Undersampling-Based Interpolation Scheme for Multislice CSMRI Reconstruction Techniques. Biomed Res Int 2021; 2021:6638588. [PMID: 33954189 DOI: 10.1155/2021/6638588] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 04/05/2021] [Indexed: 11/18/2022]
Abstract
Magnetic Resonance Imaging (MRI) is an important yet slow medical imaging modality. Compressed sensing (CS) theory has enabled to accelerate the MRI acquisition process using some nonlinear reconstruction techniques from even 10% of the Nyquist samples. In recent years, interpolated compressed sensing (iCS) has further reduced the scan time, as compared to CS, by exploiting the strong interslice correlation of multislice MRI. In this paper, an improved efficient interpolated compressed sensing (EiCS) technique is proposed using radial undersampling schemes. The proposed efficient interpolation technique uses three consecutive slices to estimate the missing samples of the central target slice from its two neighboring slices. Seven different evaluation metrics are used to analyze the performance of the proposed technique such as structural similarity index measurement (SSIM), feature similarity index measurement (FSIM), mean square error (MSE), peak signal to noise ratio (PSNR), correlation (CORR), sharpness index (SI), and perceptual image quality evaluator (PIQE) and compared with the latest interpolation techniques. The simulation results show that the proposed EiCS technique has improved image quality and performance using both golden angle and uniform angle radial sampling patterns, with an even lower sampling ratio and maximum information content and using a more practical sampling scheme.
Collapse
|
10
|
Dwork N, Baron CA, Johnson EMI, O'Connor D, Pauly JM, Larson PEZ. Fast variable density Poisson-disc sample generation with directional variation for compressed sensing in MRI. Magn Reson Imaging 2021; 77:186-193. [PMID: 33232767 PMCID: PMC7878411 DOI: 10.1016/j.mri.2020.11.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 10/27/2020] [Accepted: 11/16/2020] [Indexed: 11/17/2022]
Abstract
We present a fast method for generating random samples according to a variable density poisson-disc distribution. A minimum parameter value is used to create a background grid array for keeping track of those points that might affect any new candidate point; this reduces the number of conflicts that must be checked before acceptance of a new point, thus reducing the number of computations required. We demonstrate the algorithm's ability to generate variable density poisson-disc sampling patterns according to a parameterized function, including patterns where the variations in density are a function of direction. We further show that these sampling patterns are appropriate for compressed sensing applications. Finally, we present a method to generate patterns with a specific acceleration rate.
Collapse
Affiliation(s)
- Nicholas Dwork
- Radiology and Biomedical Imaging, University of California in San Francisco, San Francisco, California, 94158, USA.
| | - Corey A Baron
- Center for Functional and Metabolic Mapping, Western University, Ontario, Canada
| | | | - Daniel O'Connor
- Mathematics and Statistics, University of San Francisco, San Francisco, CA, USA
| | - John M Pauly
- Electrical Engineering Department, Stanford University, Palo Alto, California, USA
| | - Peder E Z Larson
- Radiology and Biomedical Imaging, University of California in San Francisco, San Francisco, California, 94158, USA
| |
Collapse
|
11
|
Knoll F, Murrell T, Sriram A, Yakubova N, Zbontar J, Rabbat M, Defazio A, Muckley MJ, Sodickson DK, Zitnick CL, Recht MP. Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. Magn Reson Med 2020; 84:3054-3070. [PMID: 32506658 PMCID: PMC7719611 DOI: 10.1002/mrm.28338] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 04/28/2020] [Accepted: 04/30/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE To advance research in the field of machine learning for MR image reconstruction with an open challenge. METHODS We provided participants with a dataset of raw k-space data from 1,594 consecutive clinical exams of the knee. The goal of the challenge was to reconstruct images from these data. In order to strike a balance between realistic data and a shallow learning curve for those not already familiar with MR image reconstruction, we ran multiple tracks for multi-coil and single-coil data. We performed a two-stage evaluation based on quantitative image metrics followed by evaluation by a panel of radiologists. The challenge ran from June to December of 2019. RESULTS We received a total of 33 challenge submissions. All participants chose to submit results from supervised machine learning approaches. CONCLUSIONS The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.
Collapse
Affiliation(s)
- Florian Knoll
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016 United States
| | - Tullie Murrell
- Facebook AI Research, Menlo Park, CA, 94025 United States
| | - Anuroop Sriram
- Facebook AI Research, Menlo Park, CA, 94025 United States
| | | | - Jure Zbontar
- Facebook AI Research, Menlo Park, CA, 94025 United States
| | - Michael Rabbat
- Facebook AI Research, Menlo Park, CA, 94025 United States
| | - Aaron Defazio
- Facebook AI Research, Menlo Park, CA, 94025 United States
| | - Matthew J. Muckley
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016 United States
| | - Daniel K. Sodickson
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016 United States
| | | | - Michael P. Recht
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016 United States
| |
Collapse
|
12
|
Xu J, Pannetier N, Raj A. A dictionary-based graph-cut algorithm for MRI reconstruction. NMR Biomed 2020; 33:e4344. [PMID: 32618082 PMCID: PMC9164168 DOI: 10.1002/nbm.4344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 05/08/2020] [Accepted: 05/10/2020] [Indexed: 06/11/2023]
Abstract
PURPOSE Compressive sensing (CS)-based image reconstruction methods have proposed random undersampling schemes that produce incoherent, noise-like aliasing artifacts, which are easier to remove. The denoising process is critically assisted by imposing sparsity-enforcing priors. Sparsity is known to be induced if the prior is in the form of the Lp (0 ≤ p ≤ 1) norm. CS methods generally use a convex relaxation of these priors such as the L1 norm, which may not exploit the full power of CS. An efficient, discrete optimization formulation is proposed, which works not only on arbitrary Lp -norm priors as some non-convex CS methods do, but also on highly non-convex truncated penalty functions, resulting in a specific type of edge-preserving prior. These advanced features make the minimization problem highly non-convex, and thus call for more sophisticated minimization routines. THEORY AND METHODS The work combines edge-preserving priors with random undersampling, and solves the resulting optimization using a set of discrete optimization methods called graph cuts. The resulting optimization problem is solved by applying graph cuts iteratively within a dictionary, defined here as an appropriately constructed set of vectors relevant to brain MRI data used here. RESULTS Experimental results with in vivo data are presented. CONCLUSION The proposed algorithm produces better results than regularized SENSE or standard CS for reconstruction of in vivo data.
Collapse
Affiliation(s)
- Jiexun Xu
- Department of Computer Science, Cornell University, Ithaca, New York
| | - Nicolas Pannetier
- Department of Radiology, University of California, San Francisco, California
| | - Ashish Raj
- Department of Radiology, University of California, San Francisco, California
| |
Collapse
|
13
|
Yoon JH, Nickel MD, Peeters JM, Lee JM. Rapid Imaging: Recent Advances in Abdominal MRI for Reducing Acquisition Time and Its Clinical Applications. Korean J Radiol 2020; 20:1597-1615. [PMID: 31854148 PMCID: PMC6923214 DOI: 10.3348/kjr.2018.0931] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Accepted: 07/22/2019] [Indexed: 02/06/2023] Open
Abstract
Magnetic resonance imaging (MRI) plays an important role in abdominal imaging. The high contrast resolution offered by MRI provides better lesion detection and its capacity to provide multiparametric images facilitates lesion characterization more effectively than computed tomography. However, the relatively long acquisition time of MRI often detrimentally affects the image quality and limits its accessibility. Recent developments have addressed these drawbacks. Specifically, multiphasic acquisition of contrast-enhanced MRI, free-breathing dynamic MRI using compressed sensing technique, simultaneous multi-slice acquisition for diffusion-weighted imaging, and breath-hold three-dimensional magnetic resonance cholangiopancreatography are recent notable advances in this field. This review explores the aforementioned state-of-the-art techniques by focusing on their clinical applications and potential benefits, as well as their likely future direction.
Collapse
Affiliation(s)
- Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
| | | | | | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
| |
Collapse
|
14
|
Malavé MO, Baron CA, Koundinyan SP, Sandino CM, Ong F, Cheng JY, Nishimura DG. Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model. Magn Reson Med 2020; 84:800-812. [PMID: 32011021 DOI: 10.1002/mrm.28177] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 12/04/2019] [Accepted: 12/27/2019] [Indexed: 12/28/2022]
Abstract
PURPOSE To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA). METHODS An end-to-end unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired during a CMRA sequence. The unrolled model incorporates a nonuniform FFT operator in TensorFlow to perform the data-consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm. The training set includes 6,000 3D iNAVs acquired from 7 different subjects and 11 scans using a variable-density (VD) cones trajectory. For testing, 3D iNAVs from 4 additional subjects are reconstructed using the unrolled model. To validate reconstruction accuracy, global and localized motion estimates from DL model-based 3D iNAVs are compared with those extracted from 3D iNAVs reconstructed with l 1 -ESPIRiT. Then, the high-resolution coronary MRA images motion corrected with autofocusing using the l 1 -ESPIRiT and DL model-based 3D iNAVs are assessed for differences. RESULTS 3D iNAVs reconstructed using the DL model-based approach and conventional l 1 -ESPIRiT generate similar global and localized motion estimates and provide equivalent coronary image quality. Reconstruction with the unrolled network completes in a fraction of the time compared to CPU and GPU implementations of l 1 -ESPIRiT (20× and 3× speed increases, respectively). CONCLUSIONS We have developed a deep neural network architecture to reconstruct undersampled 3D non-Cartesian VD cones iNAVs. Our approach decreases reconstruction time for 3D iNAVs, while preserving the accuracy of nonrigid motion information offered by them for correction.
Collapse
Affiliation(s)
- Mario O Malavé
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Corey A Baron
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Srivathsan P Koundinyan
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Christopher M Sandino
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Frank Ong
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Joseph Y Cheng
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA.,Department of Radiology, Stanford University, Stanford, CA
| | - Dwight G Nishimura
- Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA
| |
Collapse
|
15
|
Houriez--Gombaud-Saintonge S, Mousseaux E, Bargiotas I, De Cesare A, Dietenbeck T, Bouaou K, Redheuil A, Soulat G, Giron A, Gencer U, Craiem D, Messas E, Bollache E, Chenoune Y, Kachenoura N. Comparison of different methods for the estimation of aortic pulse wave velocity from 4D flow cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2019; 21:75. [PMID: 31829235 PMCID: PMC6907267 DOI: 10.1186/s12968-019-0584-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 10/22/2019] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Arterial pulse wave velocity (PWV) is associated with increased mortality in aging and disease. Several studies have shown the accuracy of applanation tonometry carotid-femoral PWV (Cf-PWV) and the relevance of evaluating central aorta stiffness using 2D cardiovascular magnetic resonance (CMR) to estimate PWV, and aortic distensibility-derived PWV through the theoretical Bramwell-Hill model (BH-PWV). Our aim was to compare various methods of aortic PWV (aoPWV) estimation from 4D flow CMR, in terms of associations with age, Cf-PWV, BH-PWV and left ventricular (LV) mass-to-volume ratio while evaluating inter-observer reproducibility and robustness to temporal resolution. METHODS We studied 47 healthy subjects (49.5 ± 18 years) who underwent Cf-PWV and CMR including aortic 4D flow CMR as well as 2D cine SSFP for BH-PWV and LV mass-to-volume ratio estimation. The aorta was semi-automatically segmented from 4D flow data, and mean velocity waveforms were estimated in 25 planes perpendicular to the aortic centerline. 4D flow CMR aoPWV was calculated: using velocity curves at two locations, namely ascending aorta (AAo) and distal descending aorta (DAo) aorta (S1, 2D-like strategy), or using all velocity curves along the entire aortic centreline (3D-like strategies) with iterative transit time (TT) estimates (S2) or a plane fitting of velocity curves systolic upslope (S3). For S1 and S2, TT was calculated using three approaches: cross-correlation (TTc), wavelets (TTw) and Fourier transforms (TTf). Intra-class correlation coefficients (ICC) and Bland-Altman biases (BA) were used to evaluate inter-observer reproducibility and effect of lower temporal resolution. RESULTS 4D flow CMR aoPWV estimates were significantly (p < 0.05) correlated to the CMR-independent Cf-PWV, BH-PWV, age and LV mass-to-volume ratio, with the strongest correlations for the 3D-like strategy using wavelets TT (S2-TTw) (R = 0.62, 0.65, 0.77 and 0.52, respectively, all p < 0.001). S2-TTw was also highly reproducible (ICC = 0.99, BA = 0.09 m/s) and robust to lower temporal resolution (ICC = 0.97, BA = 0.15 m/s). CONCLUSIONS Reproducible 4D flow CMR aoPWV estimates can be obtained using full 3D aortic coverage. Such 4D flow CMR stiffness measures were significantly associated with Cf-PWV, BH-PWV, age and LV mass-to-volume ratio, with a slight superiority of the 3D strategy using wavelets transit time (S2-TTw).
Collapse
Affiliation(s)
- Sophia Houriez--Gombaud-Saintonge
- Sorbonne Université, INSERM, CNRS, Laboratoire d’Imagerie Biomédicale (LIB), 75006 Paris, France
- ESME Sudria Research Lab, Paris, France
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| | | | - Ioannis Bargiotas
- CMLA, ENS Cachan, CNRS, Université Paris-Saclay, 94235 Cachan, France
| | - Alain De Cesare
- Sorbonne Université, INSERM, CNRS, Laboratoire d’Imagerie Biomédicale (LIB), 75006 Paris, France
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| | - Thomas Dietenbeck
- Sorbonne Université, INSERM, CNRS, Laboratoire d’Imagerie Biomédicale (LIB), 75006 Paris, France
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| | - Kevin Bouaou
- Sorbonne Université, INSERM, CNRS, Laboratoire d’Imagerie Biomédicale (LIB), 75006 Paris, France
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| | - Alban Redheuil
- Sorbonne Université, INSERM, CNRS, Laboratoire d’Imagerie Biomédicale (LIB), 75006 Paris, France
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| | | | - Alain Giron
- Sorbonne Université, INSERM, CNRS, Laboratoire d’Imagerie Biomédicale (LIB), 75006 Paris, France
| | - Umit Gencer
- Hopital Européen Georges Pompidou, Paris, France
| | - Damian Craiem
- Universidad Favaloro-CONICET, IMeTTyB, Buenos Aires, Argentina
| | | | - Emilie Bollache
- Sorbonne Université, INSERM, CNRS, Laboratoire d’Imagerie Biomédicale (LIB), 75006 Paris, France
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| | | | - Nadjia Kachenoura
- Sorbonne Université, INSERM, CNRS, Laboratoire d’Imagerie Biomédicale (LIB), 75006 Paris, France
- Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| |
Collapse
|
16
|
Holme HCM, Rosenzweig S, Ong F, Wilke RN, Lustig M, Uecker M. ENLIVE: An Efficient Nonlinear Method for Calibrationless and Robust Parallel Imaging. Sci Rep 2019; 9:3034. [PMID: 30816312 DOI: 10.1038/s41598-019-39888-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 02/04/2019] [Indexed: 12/05/2022] Open
Abstract
Robustness against data inconsistencies, imaging artifacts and acquisition speed are crucial factors limiting the possible range of applications for magnetic resonance imaging (MRI). Therefore, we report a novel calibrationless parallel imaging technique which simultaneously estimates coil profiles and image content in a relaxed forward model. Our method is robust against a wide class of data inconsistencies, minimizes imaging artifacts and is comparably fast, combining important advantages of many conceptually different state-of-the-art parallel imaging approaches. Depending on the experimental setting, data can be undersampled well below the Nyquist limit. Here, even high acceleration factors yield excellent imaging results while being robust to noise and the occurrence of phase singularities in the image domain, as we show on different data. Moreover, our method successfully reconstructs acquisitions with insufficient field-of-view. We further compare our approach to ESPIRiT and SAKE using spin-echo and gradient echo MRI data from the human head and knee. In addition, we show its applicability to non-Cartesian imaging on radial FLASH cardiac MRI data. Using theoretical considerations, we show that ENLIVE can be related to a low-rank formulation of blind multi-channel deconvolution, explaining why it inherently promotes low-rank solutions.
Collapse
|
17
|
Jun Y, Eo T, Shin H, Kim T, Lee HJ, Hwang D. Parallel imaging in time-of-flight magnetic resonance angiography using deep multistream convolutional neural networks. Magn Reson Med 2019; 81:3840-3853. [DOI: 10.1002/mrm.27656] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Revised: 11/27/2018] [Accepted: 12/14/2018] [Indexed: 12/18/2022]
Affiliation(s)
- Yohan Jun
- School of Electrical and Electronic Engineering; Yonsei University; Seoul Korea
| | - Taejoon Eo
- School of Electrical and Electronic Engineering; Yonsei University; Seoul Korea
| | - Hyungseob Shin
- School of Electrical and Electronic Engineering; Yonsei University; Seoul Korea
| | - Taeseong Kim
- School of Electrical and Electronic Engineering; Yonsei University; Seoul Korea
| | - Ho-Joon Lee
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital; Yonsei University College of Medicine; Seoul Republic of Korea
- Department of Radiology; Inje University College of Medicine, Haeundae Paik Hospital; Busan Republic of Korea
| | - Dosik Hwang
- School of Electrical and Electronic Engineering; Yonsei University; Seoul Korea
| |
Collapse
|
18
|
Chen C, Liu Y, Schniter P, Jin N, Craft J, Simonetti O, Ahmad R. Sparsity adaptive reconstruction for highly accelerated cardiac MRI. Magn Reson Med 2019; 81:3875-3887. [PMID: 30666694 DOI: 10.1002/mrm.27671] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 01/02/2019] [Accepted: 01/03/2019] [Indexed: 01/22/2023]
Abstract
PURPOSE To enable parameter-free, accelerated cardiovascular magnetic resonance (CMR). METHODS Regularized reconstruction methods, such as compressed sensing (CS), can significantly accelerate MRI data acquisition but require tuning of regularization weights. In this work, a technique, called Sparsity adaptive Composite Recovery (SCoRe) that exploits sparsity in multiple, disparate sparsifying transforms is presented. A data-driven adjustment of the relative contributions of different transforms yields a parameter-free CS recovery process. SCoRe is validated in a dynamic digital phantom as well as in retrospectively and prospectively undersampled cine CMR data. RESULTS The results from simulation and 6 retrospectively undersampled datasets indicate that SCoRe with auto-tuned regularization weights yields lower root-mean-square error (RMSE) and higher structural similarity index (SSIM) compared to state-of-the-art CS methods. In 45 prospectively undersampled datasets acquired from 15 volunteers, the image quality was scored by 2 expert reviewers, with SCoRe receiving a higher average score (p < 0.01) compared to other CS methods. CONCLUSIONS SCoRe enables accelerated cine CMR from highly undersampled data. In contrast to other acceleration techniques, SCoRe adapts regularization weights based on noise power and level of sparsity in each transform, yielding superior performance without admitting any free parameters.
Collapse
Affiliation(s)
- Chong Chen
- Biomedical Engineering, The Ohio State University, Columbus, Ohio
| | - Yingmin Liu
- Davis Heart & Lung Research Institute, The Ohio State University, Columbus, Ohio
| | - Philip Schniter
- Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio
| | - Ning Jin
- Cardiovascular MR R&D, Siemens Medical Solutions USA Inc., Columbus, Ohio
| | - Jason Craft
- Internal Medicine, The Ohio State University, Columbus, Ohio
| | - Orlando Simonetti
- Davis Heart & Lung Research Institute, The Ohio State University, Columbus, Ohio.,Internal Medicine, The Ohio State University, Columbus, Ohio.,Radiology, The Ohio State University, Columbus, Ohio
| | - Rizwan Ahmad
- Biomedical Engineering, The Ohio State University, Columbus, Ohio.,Davis Heart & Lung Research Institute, The Ohio State University, Columbus, Ohio.,Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio
| |
Collapse
|
19
|
Koolstra K, van Gemert J, Börnert P, Webb A, Remis R. Accelerating compressed sensing in parallel imaging reconstructions using an efficient circulant preconditioner for cartesian trajectories. Magn Reson Med 2019; 81:670-685. [PMID: 30084505 PMCID: PMC6283050 DOI: 10.1002/mrm.27371] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 04/30/2018] [Accepted: 04/30/2018] [Indexed: 01/25/2023]
Abstract
PURPOSE Design of a preconditioner for fast and efficient parallel imaging (PI) and compressed sensing (CS) reconstructions for Cartesian trajectories. THEORY PI and CS reconstructions become time consuming when the problem size or the number of coils is large, due to the large linear system of equations that has to be solved inℓ 1 andℓ 2 -norm based reconstruction algorithms. Such linear systems can be solved efficiently using effective preconditioning techniques. METHODS In this article we construct such a preconditioner by approximating the system matrix of the linear system, which comprises the data fidelity and includes total variation and wavelet regularization, by a matrix that is block circulant with circulant blocks. Due to this structure, the preconditioner can be constructed quickly and its inverse can be evaluated fast using only two fast Fourier transformations. We test the performance of the preconditioner for the conjugate gradient method as the linear solver, integrated into the well-established Split Bregman algorithm. RESULTS The designed circulant preconditioner reduces the number of iterations required in the conjugate gradient method by almost a factor of 5. The speed up results in a total acceleration factor of approximately 2.5 for the entire reconstruction algorithm when implemented in MATLAB, while the initialization time of the preconditioner is negligible. CONCLUSION The proposed preconditioner reduces the reconstruction time for PI and CS in a Split Bregman implementation without compromising reconstruction stability and can easily handle large systems since it is Fourier-based, allowing for efficient computations.
Collapse
Affiliation(s)
- Kirsten Koolstra
- C. J. Gorter Center for High Field MRI, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Jeroen van Gemert
- Circuits & Systems Group, Electrical Engineering, Mathematics and Computer Science FacultyDelft University of TechnologyDelftThe Netherlands
| | - Peter Börnert
- C. J. Gorter Center for High Field MRI, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
- Philips Research HamburgHamburgGermany
| | - Andrew Webb
- C. J. Gorter Center for High Field MRI, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Rob Remis
- Circuits & Systems Group, Electrical Engineering, Mathematics and Computer Science FacultyDelft University of TechnologyDelftThe Netherlands
| |
Collapse
|
20
|
Abstract
In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms has been proposed which can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically been considered in isolation from the reconstruction rule and the anatomy under consideration. In this paper, we propose a learning-based framework for optimizing MRI subsampling patterns for a specific reconstruction rule and anatomy, considering both the noiseless and noisy settings. Our learning algorithm has access to a representative set of training signals, and searches for a sampling pattern that performs well on average for the signals in this set. We present a novel parameter-free greedy mask selection method and show it to be effective for a variety of reconstruction rules and performance metrics. Moreover, we also support our numerical findings by providing a rigorous justification of our framework via statistical learning theory.
Collapse
|
21
|
Liu J, Koskas L, Faraji F, Kao E, Wang Y, Haraldsson H, Kefayati S, Zhu C, Ahn S, Laub G, Saloner D. Highly accelerated intracranial 4D flow MRI: evaluation of healthy volunteers and patients with intracranial aneurysms. MAGMA 2018; 31:295-307. [PMID: 28785850 PMCID: PMC5803461 DOI: 10.1007/s10334-017-0646-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Revised: 07/27/2017] [Accepted: 07/28/2017] [Indexed: 02/01/2023]
Abstract
OBJECTIVES To evaluate an accelerated 4D flow MRI method that provides high temporal resolution in a clinically feasible acquisition time for intracranial velocity imaging. MATERIALS AND METHODS Accelerated 4D flow MRI was developed by using a pseudo-random variable-density Cartesian undersampling strategy (CIRCUS) with the combination of k-t, parallel imaging and compressed sensing image reconstruction techniques (k-t SPARSE-SENSE). Four-dimensional flow data were acquired on five healthy volunteers and eight patients with intracranial aneurysms using CIRCUS (acceleration factor of R = 4, termed CIRCUS4) and GRAPPA (R = 2, termed GRAPPA2) as the reference method. Images with three times higher temporal resolution (R = 12, CIRCUS12) were also reconstructed from the same acquisition as CIRCUS4. Qualitative and quantitative image assessment was performed on the images acquired with different methods, and complex flow patterns in the aneurysms were identified and compared. RESULTS Four-dimensional flow MRI with CIRCUS was achieved in 5 min and allowed further improved temporal resolution of <30 ms. Volunteer studies showed similar qualitative and quantitative evaluation obtained with the proposed approach compared to the reference (overall image scores: GRAPPA2 3.2 ± 0.6; CIRCUS4 3.1 ± 0.7; CIRCUS12 3.3 ± 0.4; difference of the peak velocities: -3.83 ± 7.72 cm/s between CIRCUS4 and GRAPPA2, -1.72 ± 8.41 cm/s between CIRCUS12 and GRAPPA2). In patients with intracranial aneurysms, the higher temporal resolution improved capturing of the flow features in intracranial aneurysms (pathline visualization scores: GRAPPA2 2.2 ± 0.2; CIRCUS4 2.5 ± 0.5; CIRCUS12 2.7 ± 0.6). CONCLUSION The proposed rapid 4D flow MRI with a high temporal resolution is a promising tool for evaluating intracranial aneurysms in a clinically feasible acquisition time.
Collapse
Affiliation(s)
- Jing Liu
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA.
| | - Louise Koskas
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Farshid Faraji
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Evan Kao
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Yan Wang
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Henrik Haraldsson
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Sarah Kefayati
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | - Chengcheng Zhu
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
| | | | | | - David Saloner
- Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry St, Suite 350, San Francisco, CA, 94107, USA
- Radiology Service, VA Medical Center, San Francisco, CA, USA
| |
Collapse
|
22
|
Gordon JW, Hansen RB, Shin PJ, Feng Y, Vigneron DB, Larson PEZ. 3D hyperpolarized C-13 EPI with calibrationless parallel imaging. J Magn Reson 2018; 289:92-99. [PMID: 29476930 PMCID: PMC5856653 DOI: 10.1016/j.jmr.2018.02.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 02/11/2018] [Accepted: 02/12/2018] [Indexed: 05/08/2023]
Abstract
With the translation of metabolic MRI with hyperpolarized 13C agents into the clinic, imaging approaches will require large volumetric FOVs to support clinical applications. Parallel imaging techniques will be crucial to increasing volumetric scan coverage while minimizing RF requirements and temporal resolution. Calibrationless parallel imaging approaches are well-suited for this application because they eliminate the need to acquire coil profile maps or auto-calibration data. In this work, we explored the utility of a calibrationless parallel imaging method (SAKE) and corresponding sampling strategies to accelerate and undersample hyperpolarized 13C data using 3D blipped EPI acquisitions and multichannel receive coils, and demonstrated its application in a human study of [1-13C]pyruvate metabolism.
Collapse
Affiliation(s)
- Jeremy W Gordon
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States.
| | - Rie B Hansen
- Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Peter J Shin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
| | - Yesu Feng
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
| | - Daniel B Vigneron
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
| |
Collapse
|
23
|
Levine E, Hargreaves B. On-the-Fly Adaptive ${k}$ -Space Sampling for Linear MRI Reconstruction Using Moment-Based Spectral Analysis. IEEE Trans Med Imaging 2018; 37:557-567. [PMID: 29408784 PMCID: PMC5840375 DOI: 10.1109/tmi.2017.2766131] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
In high-dimensional magnetic resonance imaging applications, time-consuming, sequential acquisition of data samples in the spatial frequency domain ( -space) can often be accelerated by accounting for dependencies in linear reconstruction, at the cost of noise amplification that depends on the sampling pattern. Common examples are support-constrained, parallel, and dynamic MRI, and -space sampling strategies are primarily driven by image-domain metrics that are expensive to compute for arbitrary sampling patterns. It remains challenging to provide systematic and computationally efficient automatic designs of arbitrary multidimensional Cartesian sampling patterns that mitigate noise amplification, given the subspace to which the object is confined. To address this problem, this paper introduces a theoretical framework that describes local geometric properties of the sampling pattern and relates them to the spread in the eigenvalues of the information matrix described by its first two spectral moments. This new criterion is then used for very efficient optimization of complex multidimensional sampling patterns that does not require reconstructing images or explicitly mapping noise amplification. Experiments with in vivo data show strong agreement between this criterion and traditional, comprehensive image-domain- and -space-based metrics, indicating the potential of the approach for computationally efficient (on-the-fly), automatic, and adaptive design of sampling patterns.
Collapse
|
24
|
Hamilton J, Franson D, Seiberlich N. Recent advances in parallel imaging for MRI. Prog Nucl Magn Reson Spectrosc 2017; 101:71-95. [PMID: 28844222 PMCID: PMC5927614 DOI: 10.1016/j.pnmrs.2017.04.002] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 04/09/2017] [Accepted: 04/17/2017] [Indexed: 05/22/2023]
Abstract
Magnetic Resonance Imaging (MRI) is an essential technology in modern medicine. However, one of its main drawbacks is the long scan time needed to localize the MR signal in space to generate an image. This review article summarizes some basic principles and recent developments in parallel imaging, a class of image reconstruction techniques for shortening scan time. First, the fundamentals of MRI data acquisition are covered, including the concepts of k-space, undersampling, and aliasing. It is demonstrated that scan time can be reduced by sampling a smaller number of phase encoding lines in k-space; however, without further processing, the resulting images will be degraded by aliasing artifacts. Nearly all modern clinical scanners acquire data from multiple independent receiver coil arrays. Parallel imaging methods exploit properties of these coil arrays to separate aliased pixels in the image domain or to estimate missing k-space data using knowledge of nearby acquired k-space points. Three parallel imaging methods-SENSE, GRAPPA, and SPIRiT-are described in detail, since they are employed clinically and form the foundation for more advanced methods. These techniques can be extended to non-Cartesian sampling patterns, where the collected k-space points do not fall on a rectangular grid. Non-Cartesian acquisitions have several beneficial properties, the most important being the appearance of incoherent aliasing artifacts. Recent advances in simultaneous multi-slice imaging are presented next, which use parallel imaging to disentangle images of several slices that have been acquired at once. Parallel imaging can also be employed to accelerate 3D MRI, in which a contiguous volume is scanned rather than sequential slices. Another class of phase-constrained parallel imaging methods takes advantage of both image magnitude and phase to achieve better reconstruction performance. Finally, some applications are presented of parallel imaging being used to accelerate MR Spectroscopic Imaging.
Collapse
Affiliation(s)
- Jesse Hamilton
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Dominique Franson
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Nicole Seiberlich
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
| |
Collapse
|
25
|
Hilbert T, Nguyen D, Thiran J, Krueger G, Kober T, Bieri O. True constructive interference in the steady state (trueCISS). Magn Reson Med 2017; 79:1901-1910. [DOI: 10.1002/mrm.26836] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 06/01/2017] [Accepted: 06/22/2017] [Indexed: 11/11/2022]
Affiliation(s)
- Tom Hilbert
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare AGLausanne Switzerland
- LTS5, École Polytechnique Fédérale de LausanneLausanne Switzerland
- Department of RadiologyUniversity Hospital (CHUV)Lausanne Switzerland
| | - Damien Nguyen
- Division of Radiological PhysicsDepartment of Radiology, University Hospital Basel, University of BaselBasel Switzerland
- Department of Biomedical EngineeringUniversity of BaselBasel Switzerland
| | - Jean‐Philippe Thiran
- LTS5, École Polytechnique Fédérale de LausanneLausanne Switzerland
- Department of RadiologyUniversity Hospital (CHUV)Lausanne Switzerland
| | - Gunnar Krueger
- LTS5, École Polytechnique Fédérale de LausanneLausanne Switzerland
- Department of RadiologyUniversity Hospital (CHUV)Lausanne Switzerland
- Siemens Medical Solutions USABoston Massachusetts USA
| | - Tobias Kober
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare AGLausanne Switzerland
- LTS5, École Polytechnique Fédérale de LausanneLausanne Switzerland
- Department of RadiologyUniversity Hospital (CHUV)Lausanne Switzerland
| | - Oliver Bieri
- Division of Radiological PhysicsDepartment of Radiology, University Hospital Basel, University of BaselBasel Switzerland
- Department of Biomedical EngineeringUniversity of BaselBasel Switzerland
| |
Collapse
|
26
|
Saucedo A, Lefkimmiatis S, Rangwala N. Improved Computational Efficiency of Locally Low Rank MRI Reconstruction Using Iterative Random Patch Adjustments. IEEE Trans Med Imaging 2017; 36:1209-1220. [PMID: 28141518 DOI: 10.1109/tmi.2017.2659742] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper presents and analyzes an alternative formulation of the locally low-rank (LLR) regularization framework for magnetic resonance image (MRI) reconstruction. Generally, LLR-based MRI reconstruction techniques operate by dividing the underlying image into a collection of matrices formed from image patches. Each of these matrices is assumed to have low rank due to the inherent correlations among the data, whether along the coil, temporal, or multi-contrast dimensions. The LLR regularization has been successful for various MRI applications, such as parallel imaging and accelerated quantitative parameter mapping. However, a major limitation of most conventional implementations of the LLR regularization is the use of multiple sets of overlapping patches. Although the use of overlapping patches leads to effective shift-invariance, it also results in high-computational load, which limits the practical utility of the LLR regularization for MRI. To circumvent this problem, alternative LLR-based algorithms instead shift a single set of non-overlapping patches at each iteration, thereby achieving shift-invariance and avoiding block artifacts. A novel contribution of this paper is to provide a mathematical framework and justification of LLR regularization with iterative random patch adjustments (LLR-IRPA). This method is compared with a state-of-the-art LLR regularization algorithm based on overlapping patches, and it is shown experimentally that results are similar but with the advantage of much reduced computational load. We also present theoretical results demonstrating the effective shift invariance of the LLR-IRPA approach, and we show reconstruction examples and comparisons in both retrospectively and prospectively undersampled MRI acquisitions, and in T1 parameter mapping.
Collapse
|
27
|
Chang CH, Yu X, Ji JX. Compressed sensing MRI reconstruction from 3D multichannel data using GPUs. Magn Reson Med 2017; 78:2265-2274. [PMID: 28198568 DOI: 10.1002/mrm.26636] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 01/01/2017] [Accepted: 01/18/2017] [Indexed: 11/08/2022]
Abstract
PURPOSE To accelerate iterative reconstructions of compressed sensing (CS) MRI from 3D multichannel data using graphics processing units (GPUs). METHODS The sparsity of MRI signals and parallel array receivers can reduce the data acquisition requirements. However, iterative CS reconstructions from data acquired using an array system may take a significantly long time, especially for a large number of parallel channels. This paper presents an efficient method for CS-MRI reconstruction from 3D multichannel data using GPUs. In this method, CS reconstructions were simultaneously processed in a channel-by-channel fashion on the GPU, in which the computations of multiple-channel 3D-CS reconstructions are highly parallelized. The final image was then produced by a sum-of-squares method on the central processing unit. Implementation details including algorithm, data/memory management, and parallelization schemes are reported in the paper. RESULTS Both simulated data and in vivo MRI array data were tested. The results showed that the proposed method can significantly improve the image reconstruction efficiency, typically shortening the runtime by a factor of 30. CONCLUSIONS Using low-cost GPUs and an efficient algorithm allowed the 3D multislice compressive-sensing reconstruction to be performed in less than 1 s. The rapid reconstructions are expected to help bring high-dimensional, multichannel parallel CS MRI closer to clinical applications. Magn Reson Med 78:2265-2274, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
Collapse
Affiliation(s)
- Ching-Hua Chang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA
| | - Xiangdong Yu
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA
| | - Jim X Ji
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA
| |
Collapse
|
28
|
Madankan R, Stefan W, Fahrenholtz SJ, MacLellan CJ, Hazle JD, Stafford RJ, Weinberg JS, Rao G, Fuentes D. Accelerated magnetic resonance thermometry in the presence of uncertainties. Phys Med Biol 2017; 62:214-245. [PMID: 27991449 DOI: 10.1088/1361-6560/62/1/214] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
A model-based information theoretic approach is presented to perform the task of magnetic resonance (MR) thermal image reconstruction from a limited number of observed samples on k-space. The key idea of the proposed approach is to optimally detect samples of k-space that are information-rich with respect to a model of the thermal data acquisition. These highly informative k-space samples can then be used to refine the mathematical model and efficiently reconstruct the image. The information theoretic reconstruction was demonstrated retrospectively in data acquired during MR-guided laser induced thermal therapy (MRgLITT) procedures. The approach demonstrates that locations with high-information content with respect to a model-based reconstruction of MR thermometry may be quantitatively identified. These information-rich k-space locations are demonstrated to be useful as a guide for k-space undersampling techniques. The effect of interactively increasing the predicted number of data points used in the subsampled model-based reconstruction was quantified using the L2-norm of the distance between the subsampled and fully sampled reconstruction. Performance of the proposed approach was also compared with uniform rectilinear subsampling and variable-density Poisson disk subsampling techniques. The proposed subsampling scheme resulted in accurate reconstructions using a small fraction of k-space points, suggesting that the reconstruction technique may be useful in improving the efficiency of thermometry data temporal resolution.
Collapse
Affiliation(s)
- R Madankan
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | | | | | | | | | | | | | | |
Collapse
|
29
|
Jiang X, Ding H, Zhang H, Li C. Study on compressed sensing reconstruction algorithm of medical image based on curvelet transform of image block. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.04.062] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
|
30
|
Feng L, Benkert T, Block KT, Sodickson DK, Otazo R, Chandarana H. Compressed sensing for body MRI. J Magn Reson Imaging 2016; 45:966-987. [PMID: 27981664 DOI: 10.1002/jmri.25547] [Citation(s) in RCA: 172] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 10/25/2016] [Indexed: 12/18/2022] Open
Abstract
The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This article presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and nonlinear reconstruction. The second section reviews state-of-the-art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the article discusses current challenges and future opportunities. LEVEL OF EVIDENCE 5 J. Magn. Reson. Imaging 2017;45:966-987.
Collapse
Affiliation(s)
- Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Thomas Benkert
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Kai Tobias Block
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Ricardo Otazo
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| |
Collapse
|
31
|
Abstract
The goal of this paper was to review the effectiveness of using 7-T MRI to study neuroimaging biomarkers for Alzheimer's disease (AD). The authors reviewed the literature for articles published to date on the use of 7-T MRI to study AD. Thus far, there are 3 neuroimaging biomarkers for AD that have been studied using 7-T MRI in AD tissue: 1) neuroanatomical atrophy; 2) molecular characterization of hypointensities; and 3) microinfarcts. Seven-Tesla MRI has had mixed results when used to study the 3 aforementioned neuroimaging biomarkers for AD. First, in the detection of neuroanatomical atrophy, 7-T MRI has exciting potential. Historically, noninvasive imaging of neuroanatomical atrophy during AD has been limited by suboptimal resolution. However, now there is compelling evidence that the high resolution of 7-T MRI may help overcome this hurdle. Second, in detecting the characterization of hypointensities, 7-T MRI has had varied success. PET scans will most likely continue to lead in the noninvasive imaging of amyloid plaques; however, there is emerging evidence that 7-T MRI can accurately detect iron deposits within activated microglia, which may help shed light on the role of the immune system in AD pathogenesis. Finally, in the detection of microinfarcts, 7-T MRI may also play a promising role, which may help further elucidate the relationship between cerebrovascular health and AD progression.
Collapse
Affiliation(s)
| | - Maged Goubran
- Radiology, Stanford University School of Medicine, Stanford, California
| | | | - Michael M Zeineh
- Radiology, Stanford University School of Medicine, Stanford, California
| |
Collapse
|
32
|
Liu Y, Zhan Z, Cai JF, Guo D, Chen Z, Qu X. Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging. IEEE Trans Med Imaging 2016; 35:2130-2140. [PMID: 27071164 DOI: 10.1109/tmi.2016.2550080] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Compressed sensing (CS) has exhibited great potential for accelerating magnetic resonance imaging (MRI). In CS-MRI, we want to reconstruct a high-quality image from very few samples in a short time. In this paper, we propose a fast algorithm, called projected iterative soft-thresholding algorithm (pISTA), and its acceleration pFISTA for CS-MRI image reconstruction. The proposed algorithms exploit sparsity of the magnetic resonance (MR) images under the redundant representation of tight frames. We prove that pISTA and pFISTA converge to a minimizer of a convex function with a balanced tight frame sparsity formulation. The pFISTA introduces only one adjustable parameter, the step size, and we provide an explicit rule to set this parameter. Numerical experiment results demonstrate that pFISTA leads to faster convergence speeds than the state-of-art counterpart does, while achieving comparable reconstruction errors. Moreover, reconstruction errors incurred by pFISTA appear insensitive to the step size.
Collapse
|
33
|
Roy CW, Seed M, Macgowan CK. Accelerated MRI of the fetal heart using compressed sensing and metric optimized gating. Magn Reson Med 2016; 77:2125-2135. [PMID: 27254315 DOI: 10.1002/mrm.26290] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 05/05/2016] [Accepted: 05/06/2016] [Indexed: 01/03/2023]
Abstract
PURPOSE To develop and validate a method for accelerated time-resolved imaging of the fetal heart using a combination of compressed sensing (CS) and metric optimized gating (MOG). THEORY AND METHODS Joint optimization of CS and MOG reconstructions was used to suppress competing artifact from random undersampling and ungated cardiac motion. Retrospectively and prospectively undersampled adult and fetal data were used to validate the proposed reconstruction algorithm qualitatively based on visual assessment, and quantitatively based on reconstruction error, blur, and MOG timing error. RESULTS Excellent agreement was observed between the fully sampled and retrospectively undersampled reconstructions, up to an undersampling factor of four. Visually, differences between ECG and MOG reconstructions of adult data were negligible. This was consistent with quantitative comparisons of reconstruction error (RMSEECG = 0.07-0.13; RMSEMOG = 0.08-0.13), and image blur (BECG = 1.03-1.20; BMOG = 1.03-1.20). The calculated MOG timing error (2-42 ms) was comparable to the acquired temporal resolution (∼60 ms). Quantitative evaluation of retrospectively undersampled (R = 2-8) fetal data (RMSEMOG = 0.06-0.12; BMOG = 1.04-1.27) was comparable to the adult volunteer results. CONCLUSION CS-MOG for dynamic imaging of the fetal heart was developed and validated. Using CS-MOG, images were obtained up to four times faster than conventional acquisitions. Magn Reson Med 77:2125-2135, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
Collapse
Affiliation(s)
- Christopher W Roy
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Division of Physiology and Experimental Medicine, The Hospital for Sick Children, Toronto, Canada
| | - Mike Seed
- Division of Pediatric Cardiology, The Hospital for Sick Children, Toronto, Canada.,Departments of Pediatrics and Diagnostic Imaging, University of Toronto, Toronto, Canada
| | - Christopher K Macgowan
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Division of Physiology and Experimental Medicine, The Hospital for Sick Children, Toronto, Canada
| |
Collapse
|
34
|
Levine E, Daniel B, Vasanawala S, Hargreaves B, Saranathan M. 3D Cartesian MRI with compressed sensing and variable view sharing using complementary poisson-disc sampling. Magn Reson Med 2016; 77:1774-1785. [PMID: 27097596 DOI: 10.1002/mrm.26254] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Revised: 02/22/2016] [Accepted: 04/01/2016] [Indexed: 12/13/2022]
Abstract
PURPOSE To enable robust, high spatio-temporal-resolution three-dimensional Cartesian MRI using a scheme incorporating a novel variable density random k-space sampling trajectory allowing flexible and retrospective selection of the temporal footprint with compressed sensing (CS). METHODS A complementary Poisson-disc k-space sampling trajectory was designed to allow view sharing and varying combinations of reduced view sharing with CS from the same prospective acquisition. These schemes were used for two-point Dixon-based dynamic contrast-enhanced MRI (DCE-MRI) of the breast and abdomen. Results were validated in vivo with a novel approach using variable-flip-angle data, which was retrospectively accelerated using the same methods but offered a ground truth. RESULTS In breast DCE-MRI, the temporal footprint could be reduced 2.3-fold retrospectively without introducing noticeable artifacts, improving depiction of rapidly enhancing lesions. Further, experiments with variable-flip-angle data showed that reducing view sharing improved accuracy in reconstruction and T1 mapping. In abdominal MRI, 2.3-fold and 3.6-fold reductions in temporal footprint allowed reduced motion artifacts. CONCLUSION The complementary-Poisson-disc k-space sampling trajectory allowed a retrospective spatiotemporal resolution tradeoff using CS and view sharing, imparting robustness to motion and contrast enhancement. The technique was also validated using a novel approach of fully acquired variable-flip-angle acquisition. Magn Reson Med 77:1774-1785, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
Collapse
Affiliation(s)
- Evan Levine
- Lucas Center, Departments of Electrical Engineering and Radiology, Stanford University, Stanford, California, USA
| | - Bruce Daniel
- Lucas Center, Departments of Electrical Engineering and Radiology, Stanford University, Stanford, California, USA
| | - Shreyas Vasanawala
- Lucas Center, Departments of Electrical Engineering and Radiology, Stanford University, Stanford, California, USA
| | - Brian Hargreaves
- Lucas Center, Departments of Electrical Engineering and Radiology, Stanford University, Stanford, California, USA
| | - Manojkumar Saranathan
- Lucas Center, Departments of Electrical Engineering and Radiology, Stanford University, Stanford, California, USA
| |
Collapse
|
35
|
Feng Y, Gordon JW, Shin PJ, von Morze C, Lustig M, Larson PEZ, Ohliger MA, Carvajal L, Tropp J, Pauly JM, Vigneron DB. Development and testing of hyperpolarized (13)C MR calibrationless parallel imaging. J Magn Reson 2016; 262:1-7. [PMID: 26679288 PMCID: PMC4864033 DOI: 10.1016/j.jmr.2015.10.018] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2015] [Revised: 10/22/2015] [Accepted: 10/23/2015] [Indexed: 05/12/2023]
Abstract
A calibrationless parallel imaging technique developed previously for (1)H MRI was modified and tested for hyperpolarized (13)C MRI for applications requiring large FOV and high spatial resolution. The technique was demonstrated with both retrospective and prospective under-sampled data acquired in phantom and in vivo rat studies. A 2-fold acceleration was achieved using a 2D symmetric EPI readout equipped with random blips on the phase encode dimension. Reconstructed images showed excellent qualitative agreement with fully sampled data. Further acceleration can be achieved using acquisition schemes that incorporate multi-dimensional under-sampling.
Collapse
Affiliation(s)
- Yesu Feng
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Jeremy W Gordon
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Peter J Shin
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Cornelius von Morze
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Michael Lustig
- Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, CA, USA
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Michael A Ohliger
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - Lucas Carvajal
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA
| | | | - John M Pauly
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Daniel B Vigneron
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, CA, USA.
| |
Collapse
|
36
|
Hsiao A, Yousaf U, Alley MT, Lustig M, Chan FP, Newman B, Vasanawala SS. Improved quantification and mapping of anomalous pulmonary venous flow with four-dimensional phase-contrast MRI and interactive streamline rendering. J Magn Reson Imaging 2015; 42:1765-76. [PMID: 25914149 PMCID: PMC4843111 DOI: 10.1002/jmri.24928] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 04/07/2015] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Cardiac MRI is routinely performed for quantification of shunt flow in patients with anomalous pulmonary veins, but can be technically-challenging to perform. Four-dimensional phase-contrast (4D-PC) MRI has potential to simplify this exam. We sought to determine whether 4D-PC may be a viable clinical alternative to conventional 2D phase-contrast MR imaging. METHODS With institutional review board approval and HIPAA-compliance, we retrospectively identified all patients with anomalous pulmonary veins who underwent cardiac MRI at either 1.5 Tesla (T) or 3T with parallel-imaging compressed-sensing (PI-CS) 4D-PC between April, 2011 and October, 2013. A total of 15 exams were included (10 male, 5 female). Algorithms for interactive streamline visualization were developed and integrated into in-house software. Blood flow was measured at the valves, pulmonary arteries and veins, cavae, and any associated shunts. Pulmonary veins were mapped to their receiving atrial chamber with streamlines. The intraobserver, interobserver, internal consistency of flow measurements, and consistency with conventional MRI were then evaluated with Pearson correlation and Bland-Altman analysis. RESULTS Triplicate measurements of blood flow from 4D-PC were highly consistent, particularly at the aortic and pulmonary valves (cv 2-3%). Flow measurements were reproducible by a second observer (ρ = 0.986-0.999). Direct measurements of shunt volume from anomalous veins and intracardiac shunts matched indirect estimates from the outflow valves (ρ = 0.966). Measurements of shunt fraction using 4D-PC using any approach were more consistent with ventricular volumetric displacements than conventional 2D-PC (ρ = 0.972-0.991 versus 0.929). CONCLUSION Shunt flow may be reliably quantified with 4D-PC MRI, either indirectly or with detailed delineation of flow from multiple shunts. The 4D-PC may be a more accurate alternative to conventional MRI.
Collapse
Affiliation(s)
- Albert Hsiao
- Department of Radiology, University of California, San Diego, California, USA
| | - Ufra Yousaf
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Marcus T. Alley
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Michael Lustig
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, California, USA
| | - Frandics Pak Chan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Beverley Newman
- Department of Radiology, Stanford University, Stanford, California, USA
| | | |
Collapse
|
37
|
Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly JM, Vasanawala SS, Lustig M. ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn Reson Med 2015; 71:990-1001. [PMID: 23649942 DOI: 10.1002/mrm.24751] [Citation(s) in RCA: 716] [Impact Index Per Article: 79.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
PURPOSE Parallel imaging allows the reconstruction of images from undersampled multicoil data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and GRAPPA, which makes use of learned correlations in k-space. The purpose of this work is to clarify their relationship and to develop and evaluate an improved algorithm. THEORY AND METHODS A theoretical analysis shows: (1) The correlations in k-space are encoded in the null space of a calibration matrix. (2) Both approaches restrict the solution to a subspace spanned by the sensitivities. (3) The sensitivities appear as the main eigenvector of a reconstruction operator computed from the null space. The basic assumptions and the quality of the sensitivity maps are evaluated in experimental examples. The appearance of additional eigenvectors motivates an extended SENSE reconstruction with multiple maps, which is compared to existing methods. RESULTS The existence of a null space and the high quality of the extracted sensitivities are confirmed. The extended reconstruction combines all advantages of SENSE with robustness to certain errors similar to GRAPPA. CONCLUSION In this article the gap between both approaches is finally bridged. A new autocalibration technique combines the benefits of both.
Collapse
Affiliation(s)
- Martin Uecker
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | | | | | | | | | | | | | | |
Collapse
|
38
|
Wilson NE, Burns BL, Iqbal Z, Thomas MA. Correlated spectroscopic imaging of calf muscle in three spatial dimensions using group sparse reconstruction of undersampled single and multichannel data. Magn Reson Med 2015; 74:1199-208. [PMID: 26382049 DOI: 10.1002/mrm.25988] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 08/23/2015] [Accepted: 08/24/2015] [Indexed: 12/22/2022]
Abstract
PURPOSE To implement a 5D (three spatial + two spectral) correlated spectroscopic imaging sequence for application to human calf. THEORY AND METHODS Nonuniform sampling was applied across the two phase encoded dimensions and the indirect spectral dimension of an echo planar-correlated spectroscopic imaging sequence. Reconstruction was applied that minimized the group sparse mixed ℓ2,1-norm of the data. Multichannel data were compressed using a sensitivity map-based approach with a spatially dependent transform matrix and utilized the self-sparsity of the individual coil images to simplify the reconstruction. RESULTS Single channel data with 8× and 16× undersampling are shown in the calf of a diabetic patient. A 15-channel scan with 12× undersampling of a healthy volunteer was reconstructed using 5 virtual channels and compared to a fully sampled single slice scan. Group sparse reconstruction faithfully reconstructs the lipid cross peaks much better than ℓ1 minimization. CONCLUSION COSY spectra can be acquired over a 3D spatial volume with scan time under 15 min using echo planar readout with highly undersampled data and group sparse reconstruction.
Collapse
Affiliation(s)
- Neil E Wilson
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Brian L Burns
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - Zohaib Iqbal
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA
| | - M Albert Thomas
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA
| |
Collapse
|
39
|
Pandit P, Rivoire J, King K, Li X. Accelerated T1ρ acquisition for knee cartilage quantification using compressed sensing and data-driven parallel imaging: A feasibility study. Magn Reson Med 2015; 75:1256-61. [PMID: 25885368 DOI: 10.1002/mrm.25702] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Revised: 02/25/2015] [Accepted: 02/27/2015] [Indexed: 01/20/2023]
Abstract
PURPOSE Quantitative T1ρ imaging is beneficial for early detection for osteoarthritis but has seen limited clinical use due to long scan times. In this study, we evaluated the feasibility of accelerated T1ρ mapping for knee cartilage quantification using a combination of compressed sensing (CS) and data-driven parallel imaging (ARC-Autocalibrating Reconstruction for Cartesian sampling). METHODS A sequential combination of ARC and CS, both during data acquisition and reconstruction, was used to accelerate the acquisition of T1ρ maps. Phantom, ex vivo (porcine knee), and in vivo (human knee) imaging was performed on a GE 3T MR750 scanner. T1ρ quantification after CS-accelerated acquisition was compared with non CS-accelerated acquisition for various cartilage compartments. RESULTS Accelerating image acquisition using CS did not introduce major deviations in quantification. The coefficient of variation for the root mean squared error increased with increasing acceleration, but for in vivo measurements, it stayed under 5% for a net acceleration factor up to 2, where the acquisition was 25% faster than the reference (only ARC). CONCLUSION To the best of our knowledge, this is the first implementation of CS for in vivo T1ρ quantification. These early results show that this technique holds great promise in making quantitative imaging techniques more accessible for clinical applications.
Collapse
Affiliation(s)
- Prachi Pandit
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Julien Rivoire
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | | | - Xiaojuan Li
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| |
Collapse
|
40
|
Bhave S, Lingala SG, Johnson CP, Magnotta VA, Jacob M. Accelerated whole-brain multi-parameter mapping using blind compressed sensing. Magn Reson Med 2015; 75:1175-86. [PMID: 25850952 DOI: 10.1002/mrm.25722] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2014] [Revised: 02/22/2015] [Accepted: 03/12/2015] [Indexed: 01/16/2023]
Abstract
PURPOSE To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain T1ρ and T2 mapping. METHODS BCS models the evolution of magnetization at every pixel as a sparse linear combination of bases in a dictionary. Unlike compressed sensing, the dictionary and the sparse coefficients are jointly estimated from undersampled data. Large number of non-orthogonal bases in BCS accounts for more complex signals than low rank representations. The low degree of freedom of BCS, attributed to sparse coefficients, translates to fewer artifacts at high acceleration factors (R). RESULTS From 2D retrospective undersampling experiments, the mean square errors in T1ρ and T2 maps were observed to be within 0.1% up to R = 10. BCS was observed to be more robust to patient-specific motion as compared to other compressed sensing schemes and resulted in minimal degradation of parameter maps in the presence of motion. Our results suggested that BCS can provide an acceleration factor of 8 in prospective 3D imaging with reasonable reconstructions. CONCLUSION BCS considerably reduces scan time for multiparameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current compressed sensing and principal component analysis-based techniques.
Collapse
Affiliation(s)
- Sampada Bhave
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa, USA
| | - Sajan Goud Lingala
- Department of Electrical Engineering, University of Southern California, California, USA
| | | | | | - Mathews Jacob
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa, USA
| |
Collapse
|
41
|
Abstract
In Magnetic Resonance Imaging (MRI) data samples are collected in the spatial frequency domain (k-space), typically by time-consuming line-by-line scanning on a Cartesian grid. Scans can be accelerated by simultaneous acquisition of data using multiple receivers (parallel imaging), and by using more efficient non-Cartesian sampling schemes. To understand and design k-space sampling patterns, a theoretical framework is needed to analyze how well arbitrary sampling patterns reconstruct unsampled k-space using receive coil information. As shown here, reconstruction from samples at arbitrary locations can be understood as approximation of vector-valued functions from the acquired samples and formulated using a Reproducing Kernel Hilbert Space (RKHS) with a matrix-valued kernel defined by the spatial sensitivities of the receive coils. This establishes a formal connection between approximation theory and parallel imaging. Theoretical tools from approximation theory can then be used to understand reconstruction in k-space and to extend the analysis of the effects of samples selection beyond the traditional image-domain g-factor noise analysis to both noise amplification and approximation errors in k-space. This is demonstrated with numerical examples.
Collapse
Affiliation(s)
- Vivek Athalye
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720
| | - Michael Lustig
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720
| | - Martin Uecker
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720
| |
Collapse
|
42
|
Zhou Z, Wang J, Balu N, Li R, Yuan C. STEP: Self-supporting tailored k-space estimation for parallel imaging reconstruction. Magn Reson Med 2015; 75:750-61. [PMID: 25762509 DOI: 10.1002/mrm.25663] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Revised: 01/05/2015] [Accepted: 01/29/2015] [Indexed: 11/08/2022]
Abstract
PURPOSE A new subspace-based iterative reconstruction method, termed Self-supporting Tailored k-space Estimation for Parallel imaging reconstruction (STEP), is presented and evaluated in comparison to the existing autocalibrating method SPIRiT and calibrationless method SAKE. THEORY AND METHODS In STEP, two tailored schemes including k-space partition and basis selection are proposed to promote spatially variant signal subspace and incorporated into a self-supporting structured low rank model to enforce properties of locality, sparsity, and rank deficiency, which can be formulated into a constrained optimization problem and solved by an iterative algorithm. Simulated and in vivo datasets were used to investigate the performance of STEP in terms of overall image quality and detail structure preservation. RESULTS The advantage of STEP on image quality is demonstrated by retrospectively undersampled multichannel Cartesian data with various patterns. Compared with SPIRiT and SAKE, STEP can provide more accurate reconstruction images with less residual aliasing artifacts and reduced noise amplification in simulation and in vivo experiments. In addition, STEP has the capability of combining compressed sensing with arbitrary sampling trajectory. CONCLUSION Using k-space partition and basis selection can further improve the performance of parallel imaging reconstruction with or without calibration signals.
Collapse
Affiliation(s)
- Zechen Zhou
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Jinnan Wang
- Vascular Imaging Lab, Department of Radiology, University of Washington, Seattle, Washington, USA.,Philips Research North America, Briarcliff Manor, New York, USA
| | - Niranjan Balu
- Vascular Imaging Lab, Department of Radiology, University of Washington, Seattle, Washington, USA
| | - Rui Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Chun Yuan
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.,Vascular Imaging Lab, Department of Radiology, University of Washington, Seattle, Washington, USA
| |
Collapse
|
43
|
Ahmad R, Xue H, Giri S, Ding Y, Craft J, Simonetti OP. Variable density incoherent spatiotemporal acquisition (VISTA) for highly accelerated cardiac MRI. Magn Reson Med 2014; 74:1266-78. [PMID: 25385540 DOI: 10.1002/mrm.25507] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2013] [Revised: 09/30/2014] [Accepted: 10/06/2014] [Indexed: 11/07/2022]
Abstract
PURPOSE For the application of compressive sensing to parallel MRI, Poisson disk sampling (PDS) has been shown to generate superior results compared with random sampling methods. However, due to its limited flexibility to incorporate additional constraints, PDS is not readily extendible to dynamic applications. Here, we propose and validate a pseudo-random sampling technique that allows incorporating constraints specific to dynamic imaging. METHODS The proposed sampling scheme, called variable density incoherent spatiotemporal acquisition (VISTA), is based on constrained minimization of Riesz energy on a spatiotemporal grid. Data from both a digital phantom and real-time cine were used to compare VISTA with uniform interleaved sampling (UIS) and variable density random sampling (VRS). The image quality was assessed qualitatively and quantitatively. RESULTS VISTA improved the trade-off between noise and sharpness. Also, VISTA produced diagnostic quality images at an acceleration rate of 15, whereas UIS and VRS images degraded below the diagnostic threshold at lower acceleration rates. CONCLUSIONS VISTA generates spatiotemporal sampling patterns with high levels of uniformity and incoherence, while maintaining a constant temporal resolution. Using a small pilot study, VISTA was shown to produce diagnostic quality images at acceleration rates up to 15.
Collapse
Affiliation(s)
- Rizwan Ahmad
- Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio, USA
| | - Hui Xue
- National Institutes of Health, Bethesda, Maryland, USA
| | | | - Yu Ding
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA
| | - Jason Craft
- Department of Internal Medicine, Division of Cardiovascular Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Orlando P Simonetti
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio, USA.,Department of Internal Medicine, Division of Cardiovascular Medicine, The Ohio State University, Columbus, Ohio, USA.,Department of Radiology, The Ohio State University, Columbus, Ohio, USA
| |
Collapse
|
44
|
Abstract
Various works have used magnetic resonance imaging (MRI) tissue classification extensively to study a number of neurological and psychiatric disorders. Various noise characteristics and other artifacts make this classification a challenging task. Instead of splitting the procedure into different steps, we extend a previous work to develop a unified Bayesian hierarchical model, which addresses both the partial volume effect and intensity non-uniformity, the two major acquisition artifacts, simultaneously. We adopted a normal mixture model with the means and variances depending on the tissue types of voxels to model the observed intensity values. We modeled the relationship among the components of the index vector of tissue types by a hidden Markov model, which captures the spatial similarity of voxels. Furthermore, we addressed the partial volume effect by construction of a higher resolution image in which each voxel is divided into subvoxels. Finally, We achieved the bias field correction by using a Gaussian Markov random field model with a band precision matrix designed in light of image filtering. Sparse matrix methods and parallel computations based on conditional independence are exploited to improve the speed of the Markov chain Monte Carlo simulation. The unified model provides more accurate tissue classification results for both simulated and real data sets.
Collapse
|
45
|
Liu J, Dyverfeldt P, Acevedo-Bolton G, Hope M, Saloner D. Highly accelerated aortic 4D flow MR imaging with variable-density random undersampling. Magn Reson Imaging 2014; 32:1012-20. [PMID: 24846341 DOI: 10.1016/j.mri.2014.05.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2013] [Revised: 05/10/2014] [Accepted: 05/12/2014] [Indexed: 11/20/2022]
Abstract
PURPOSE To investigate an effective time-resolved variable-density random undersampling scheme combined with an efficient parallel image reconstruction method for highly accelerated aortic 4D flow MR imaging with high reconstruction accuracy. MATERIALS AND METHODS Variable-density Poisson-disk sampling (vPDS) was applied in both the phase-slice encoding plane and the temporal domain to accelerate the time-resolved 3D Cartesian acquisition of flow imaging. In order to generate an improved initial solution for the iterative self-consistent parallel imaging method (SPIRiT), a sample-selective view sharing reconstruction for time-resolved random undersampling (STIRRUP) was introduced. The performance of different undersampling and image reconstruction schemes were evaluated by retrospectively applying those to fully sampled data sets obtained from three healthy subjects and a flow phantom. RESULTS Undersampling pattern based on the combination of time-resolved vPDS, the temporal sharing scheme STIRRUP, and parallel imaging SPIRiT, were able to achieve 6-fold accelerated 4D flow MRI with high accuracy using a small number of coils (N=5). The normalized root mean square error between aorta flow waveforms obtained with the acceleration method and the fully sampled data in three healthy subjects was 0.04±0.02, and the difference in peak-systolic mean velocity was -0.29±2.56cm/s. CONCLUSION Qualitative and quantitative evaluation of our preliminary results demonstrate that time-resolved variable-density random sampling is efficient for highly accelerating 4D flow imaging while maintaining image reconstruction accuracy.
Collapse
|
46
|
Shi X, Ma X, Wu W, Huang F, Yuan C, Guo H. Parallel imaging and compressed sensing combined framework for accelerating high-resolution diffusion tensor imaging using inter-image correlation. Magn Reson Med 2014; 73:1775-85. [PMID: 24824404 DOI: 10.1002/mrm.25290] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2013] [Revised: 04/02/2014] [Accepted: 04/23/2014] [Indexed: 11/10/2022]
Abstract
PURPOSE Increasing acquisition efficiency is always a challenge in high-resolution diffusion tensor imaging (DTI), which has low signal-to-noise ratio and is sensitive to reconstruction artifacts. In this study, a parallel imaging (PI) and compressed sensing (CS) combined framework is proposed, which features motion error correction, PI calibration, and sparsity model using inter-image correlation tailored for high-resolution DTI. THEORY AND METHODS The proposed method, named anisotropic sparsity SPIRiT, consists of three steps: (i) motion-induced phase error estimation, (ii) initial CS reconstruction and PI kernel calibration, and (iii) final reconstruction combining PI and CS. Inter-image correlation of diffusion-weighted images are used through anisotropic signals for improved sparsity. A specific implementation based on multishot variable density spiral DTI is used to demonstrate the method. RESULTS The proposed reconstruction method was compared with CG-SENSE, CS-based joint reconstruction, and PI and CS combined methods with L1 and joint sparsity regularization, in brain DTI experiments at acceleration factors of 3 to 5. Both qualitative and quantitative results demonstrated that the proposed method resulted in better preserved image quality and more accurate DTI parameters than other methods. CONCLUSION The proposed method can accelerate high-resolution DTI acquisition effectively by using the sharable information among different diffusion encoding directions.
Collapse
Affiliation(s)
- Xinwei Shi
- Department of Electrical Engineering, Stanford University, Stanford, California, USA; Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | | | | | | | | | | |
Collapse
|
47
|
Xue H, Inati S, Sørensen TS, Kellman P, Hansen MS. Distributed MRI reconstruction using Gadgetron-based cloud computing. Magn Reson Med 2014; 73:1015-25. [PMID: 24687458 DOI: 10.1002/mrm.25213] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Revised: 01/28/2014] [Accepted: 02/18/2014] [Indexed: 11/08/2022]
Abstract
PURPOSE To expand the open source Gadgetron reconstruction framework to support distributed computing and to demonstrate that a multinode version of the Gadgetron can be used to provide nonlinear reconstruction with clinically acceptable latency. METHODS The Gadgetron framework was extended with new software components that enable an arbitrary number of Gadgetron instances to collaborate on a reconstruction task. This cloud-enabled version of the Gadgetron was deployed on three different distributed computing platforms ranging from a heterogeneous collection of commodity computers to the commercial Amazon Elastic Compute Cloud. The Gadgetron cloud was used to provide nonlinear, compressed sensing reconstruction on a clinical scanner with low reconstruction latency (eg, cardiac and neuroimaging applications). RESULTS The proposed setup was able to handle acquisition and 11 -SPIRiT reconstruction of nine high temporal resolution real-time, cardiac short axis cine acquisitions, covering the ventricles for functional evaluation, in under 1 min. A three-dimensional high-resolution brain acquisition with 1 mm(3) isotropic pixel size was acquired and reconstructed with nonlinear reconstruction in less than 5 min. CONCLUSION A distributed computing enabled Gadgetron provides a scalable way to improve reconstruction performance using commodity cluster computing. Nonlinear, compressed sensing reconstruction can be deployed clinically with low image reconstruction latency.
Collapse
Affiliation(s)
- Hui Xue
- Magnetic Resonance Technology Program, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | | | | | | | | |
Collapse
|
48
|
Liu J, Saloner D. Accelerated MRI with CIRcular Cartesian UnderSampling (CIRCUS): a variable density Cartesian sampling strategy for compressed sensing and parallel imaging. Quant Imaging Med Surg 2014; 4:57-67. [PMID: 24649436 DOI: 10.3978/j.issn.2223-4292.2014.02.01] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2014] [Accepted: 02/12/2014] [Indexed: 11/14/2022]
Abstract
PURPOSE This study proposes and evaluates a novel method for generating efficient undersampling patterns for 3D Cartesian acquisition with compressed sensing (CS) and parallel imaging (PI). METHODS Image quality achieved with schemes that accelerate data acquisition, including CS and PI, are sensitive to the design of the specific undersampling scheme used. Ideally random sampling is required to recover MR images from undersampled data with CS. In practice, pseudo-random sampling schemes are usually applied. Radial or spiral sampling either for Cartesian or non-Cartesian acquisitions has been using because of its favorable features such as interleaving flexibility. In this study, we propose to undersample data on the ky-kz plane of the 3D Cartesian acquisition by circularly selecting sampling points in a way that maintains the features of both random and radial or spiral sampling. RESULTS The proposed sampling scheme is shown to outperform conventional random and radial or spiral samplings for 3D Cartesian acquisition and is found to be comparable to advanced variable-density Poisson-Disk sampling (vPDS) while retaining interleaving flexibility for dynamic imaging, based on the results with retrospective undersampling. Our preliminary results with the prospective implementation of the proposed undersampling strategy demonstrated its favorable features. CONCLUSIONS The proposed undersampling patterns for 3D Cartesian acquisition possess the desirable properties of randomization and radial or spiral trajectories. It provides easy implementation, flexible sampling, and high accuracy of image reconstruction with CS and PI.
Collapse
Affiliation(s)
- Jing Liu
- 1 Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA ; 2 Radiology Service, VA Medical Center, San Francisco, California, USA
| | - David Saloner
- 1 Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA ; 2 Radiology Service, VA Medical Center, San Francisco, California, USA
| |
Collapse
|
49
|
Ong F, Uecker M, Tariq U, Hsiao A, Alley MT, Vasanawala SS, Lustig M. Robust 4D flow denoising using divergence-free wavelet transform. Magn Reson Med 2014; 73:828-42. [PMID: 24549830 DOI: 10.1002/mrm.25176] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2013] [Revised: 12/23/2013] [Accepted: 01/20/2014] [Indexed: 11/11/2022]
Abstract
PURPOSE To investigate four-dimensional flow denoising using the divergence-free wavelet (DFW) transform and compare its performance with existing techniques. THEORY AND METHODS DFW is a vector-wavelet that provides a sparse representation of flow in a generally divergence-free field and can be used to enforce "soft" divergence-free conditions when discretization and partial voluming result in numerical nondivergence-free components. Efficient denoising is achieved by appropriate shrinkage of divergence-free wavelet and nondivergence-free coefficients. SureShrink and cycle spinning are investigated to further improve denoising performance. RESULTS DFW denoising was compared with existing methods on simulated and phantom data and was shown to yield better noise reduction overall while being robust to segmentation errors. The processing was applied to in vivo data and was demonstrated to improve visualization while preserving quantifications of flow data. CONCLUSION DFW denoising of four-dimensional flow data was shown to reduce noise levels in flow data both quantitatively and visually.
Collapse
Affiliation(s)
- Frank Ong
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | | | | | | | | | | | | |
Collapse
|
50
|
Hsiao A, Tariq U, Alley MT, Lustig M, Vasanawala SS. Inlet and outlet valve flow and regurgitant volume may be directly and reliably quantified with accelerated, volumetric phase-contrast MRI. J Magn Reson Imaging 2014; 41:376-85. [PMID: 24677253 DOI: 10.1002/jmri.24578] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2013] [Accepted: 12/28/2013] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To determine whether it is feasible to use solely an accelerated 4D phase-contrast magnetic resonance imaging (4D-PC MRI) acquisition to quantify net and regurgitant flow volume through each of the cardiac valves. MATERIALS AND METHODS Accelerated, 4D-PC MRI examinations performed between March 2010 through June 2011 as part of routine MRI examinations for congenital, structural heart disease were retrospectively reviewed and analyzed using valve-tracking visualization and quantification algorithms developed in Java and OpenGL. Excluding patients with transposition or single ventricle physiology, a total of 34 consecutive pediatric patients (19 male, 15 female; mean age 6.9 years; age range 10 months to 15 years) were identified. 4D-PC flow measurements were compared at each valve and against routine measurements from conventional cardiac MRI using Bland-Altman and Pearson correlation analysis. RESULTS Inlet and outlet valve net flow were highly correlated between all valves (P = 0.940-0.985). The sum of forward flow at the outlet valve and regurgitant flow at the inlet valve were consistent with volumetric displacements in each ventricle (P = 0.939-0.948). These were also highly consistent with conventional planar MRI measurements with net flow (P = 0.923-0.935) and regurgitant fractions (P = 0.917-0.972) at the outlet valve and ventricular volumes (P = 0.925-0.965). CONCLUSION It is possible to obtain consistent measurements of net and regurgitant blood flow across the inlet and outlet valves relying solely on accelerated 4D-PC. This may facilitate more efficient clinical quantification of valvular regurgitation.
Collapse
Affiliation(s)
- Albert Hsiao
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | | | | | | |
Collapse
|