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Zou L, Zheng Y, Chen J, Ding Y, Liu H, Liu Y, Xu J, Zheng H, Liu X. Myocardial First-Pass Perfusion With Increased Anatomic Coverage at 3 T Using Autocalibrated Multiband Imaging. J Magn Reson Imaging 2023; 57:178-188. [PMID: 35426192 DOI: 10.1002/jmri.28193] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 03/28/2022] [Accepted: 03/28/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Myocardial first-pass perfusion (FPP) imaging is a useful cardiac MRI method for the diagnosis of coronary artery disease. However, conventional 2D multislice FPP acquisitions usually have gaps between myocardium slices, which limits the overall assessment of myocardial ischemia. PURPOSE To increase the anatomic coverage of myocardial FPP imaging at 3 T by implementing both autocalibrated multiband (MB) acquisition and k-t space acceleration with compress sensing (CS) reconstruction, without the need for additional reference scans. STUDY TYPE Phantom and prospective human studies. PHANTOM/SUBJECTS A T1MES (T1 Mapping and ECV Standardization in cardiovascular magnetic resonance) phantom and 20 subjects (12 healthy subjects and 8 patients, 10 males, age 42 ± 16 years). FIELD STRENGTH/SEQUENCE A 3 T/saturation recovery prepared gradient echo sequence with contrast administration. ASSESSMENT Phantom experiments were performed to compare the performance of autocalibrated MB-FPP with k-t acceleration using slice-GRAPPA and CS reconstructions. In vivo experiments were performed to compare the performance of conventional FPP (2.5× acceleration) with autocalibrated MB + CS-FPP (6× acceleration). In phantom experiments, the error maps were calculated. In in vivo experiments, the contrast ratio (CR) and blurring were quantitatively measured, while image quality, perceived signal-to-noise ratio (SNR), and artifact level were qualitatively graded by three cardiologists on a 4-point scale. STATISTICAL TESTS Wilcoxon signed-rank test, paired t-test. A P value <0.05 was considered statistically significant. RESULTS In phantom experiments, residual artifact was reduced using the MB + CS-FPP reconstruction method compared with using the MB + slice-GRAPPA reconstruction method. In in vivo experiments, the proposed autocalibrated MB + CS-FPP method demonstrated significantly higher CR (3.52 ± 0.78 vs 2.91 ± 0.81) and had significantly better perceived SNR (2.69 ± 0.29 vs 2.48 ± 0.31) compared to the conventional sequence. Compared with conventional FPP, MB + CS-FPP doubled the spatial coverage (MB + CS-FPP vs conventional FPP) without compromising the image quality (2.69 ± 0.26 vs 2.60 ± 0.30) or increasing the artifact level (2.60 ± 0.26 vs 2.52 ± 0.31). CONCLUSION Autocalibrated MB + CS-FPP improved the myocardial coverage and achieved comparable image quality with the same spatial resolution and scan time as conventional FPP and is a promising technique for clinical myocardial perfusion imaging. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Lixian Zou
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, Guangdong, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | | | - Jialing Chen
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China
| | - Yu Ding
- UIHA America Inc, Houston, Texas, USA
| | - Hui Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
| | - Yubao Liu
- Medical Imaging Center, Shenzhen Hospital, Southern Medical University, Shenzhen, Guangdong, China
| | - Jian Xu
- UIHA America Inc, Houston, Texas, USA
| | - Hairong Zheng
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, Guangdong, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Xin Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, Guangdong, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
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Mazaheri Y, Kim N, Lakhman Y, Jafari R, Vargas A, Otazo R. Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters. NMR IN BIOMEDICINE 2022; 35:e4718. [PMID: 35226774 PMCID: PMC9203940 DOI: 10.1002/nbm.4718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/17/2022] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [Ktrans ], fractional volume of the extravascular extracellular space [ve ], and blood plasma volume fraction [vp ]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of Ktrans and ve , and less than 11% in the estimation of vp . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.
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Affiliation(s)
- Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nathanael Kim
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Yulia Lakhman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ramin Jafari
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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3
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Hu Z, Zhao C, Zhao X, Kong L, Yang J, Wang X, Liao J, Zhou Y. Joint reconstruction framework of compressed sensing and nonlinear parallel imaging for dynamic cardiac magnetic resonance imaging. BMC Med Imaging 2021; 21:182. [PMID: 34852771 PMCID: PMC8638482 DOI: 10.1186/s12880-021-00685-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 10/06/2021] [Indexed: 02/08/2023] Open
Abstract
Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we proposed a novel reconstruction framework that effectively combined compressed sensing and nonlinear parallel imaging technique for dynamic cardiac imaging. Specifically, the proposed method decouples the reconstruction process into two sequential steps: In the first step, a series of aliased dynamic images were reconstructed from the highly undersampled k-space data using compressed sensing; In the second step, nonlinear parallel imaging technique, i.e. nonlinear GRAPPA, was utilized to reconstruct the original dynamic images from the reconstructed k-space data obtained from the first step. In addition, we also proposed a tailored k-space down-sampling scheme that satisfies both the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. The proposed method was validated using four in vivo experiments of dynamic cardiac cine MRI with retrospective undersampling. Experimental results showed that the proposed method is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, compared with the competing k-t FOCUSS and k-t FOCUSS with sensitivity encoding methods, with the same numbers of measurements.
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Affiliation(s)
- Zhanqi Hu
- grid.452787.b0000 0004 1806 5224Shenzhen Children’s Hospital, Shenzhen, 518038 Guangdong China
| | - Cailei Zhao
- grid.452787.b0000 0004 1806 5224Shenzhen Children’s Hospital, Shenzhen, 518038 Guangdong China
| | - Xia Zhao
- grid.452787.b0000 0004 1806 5224Shenzhen Children’s Hospital, Shenzhen, 518038 Guangdong China
| | - Lingyu Kong
- grid.452787.b0000 0004 1806 5224Shenzhen Children’s Hospital, Shenzhen, 518038 Guangdong China
| | - Jun Yang
- grid.410726.60000 0004 1797 8419Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, 518055 Guangdong China
| | - Xiaoyan Wang
- grid.464483.90000 0004 1799 4419School of Physics and Electronic Engineering, Yuxi Normal University, Yuxi, 653100 Yunnan China
| | - Jianxiang Liao
- grid.452787.b0000 0004 1806 5224Shenzhen Children’s Hospital, Shenzhen, 518038 Guangdong China
| | - Yihang Zhou
- grid.414329.90000 0004 1764 7097Hong Kong Sanatorium and Hospital, 5 A Kung Ngam Village Road, Shau Kei Wan, Hong Kong, China
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4
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Wang PN, Velikina JV, Strigel RM, Henze Bancroft LC, Samsonov AA, Cashen TA, Wang K, Kelcz F, Johnson KM, Korosec FR, Ersoz A, Holmes JH. Comparison of data-driven and general temporal constraints on compressed sensing for breast DCE MRI. Magn Reson Med 2021; 85:3071-3084. [PMID: 33306217 DOI: 10.1002/mrm.28628] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 12/20/2022]
Abstract
PURPOSE Current breast DCE-MRI strategies provide high sensitivity for cancer detection but are known to be insufficient in fully capturing rapidly changing contrast kinetics at high spatial resolution across both breasts. Advanced acquisition and reconstruction strategies aim to improve spatial and temporal resolution and increase specificity for disease characterization. In this work, we evaluate the spatial and temporal fidelity of a modified data-driven low-rank-based model (known as MOCCO, model consistency condition) compressed-sensing (CS) reconstruction compared to CS with temporal total variation with radial acquisition for high spatial-temporal breast DCE MRI. METHODS Reconstruction performance was characterized using numerical simulations of a golden-angle stack-of-stars breast DCE-MRI acquisition at 5-second temporal resolution. Specifically, MOCCO was compared with CS total variation and conventional SENSE reconstructions. The temporal model for MOCCO was prelearned over the source data, whereas CS total variation was performed using a first-order temporal gradient sparsity transform. RESULTS The MOCCO reconstruction was able to capture rapid lesion kinetics while providing high image quality across a range of optimal regularization values. It also recovered kinetics in small lesions (1.5 mm) in line-profile analysis and error images, whereas g-factor maps showed relatively low and constant values with no significant artifacts. The CS-TV method demonstrated either recovery of high spatial resolution with reduced temporal accuracy using large regularization values, or recovery of rapid lesion kinetics with reduced image quality using low regularization values. CONCLUSION Simulations demonstrated that MOCCO with radial acquisition provides a robust imaging technique for improving temporal fidelity, while maintaining high spatial resolution and image quality in the setting of bilateral breast DCE MRI.
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Affiliation(s)
- Ping N Wang
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Julia V Velikina
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Roberta M Strigel
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Leah C Henze Bancroft
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Alexey A Samsonov
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Ty A Cashen
- Global MR Applications & Workflow, GE Healthcare, Madison, Wisconsin, USA
| | - Kang Wang
- Global MR Applications & Workflow, GE Healthcare, Madison, Wisconsin, USA
| | - Frederick Kelcz
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Frank R Korosec
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Ali Ersoz
- MR Engineering, GE Healthcare, Waukesha, Wisconsin, USA
| | - James H Holmes
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
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5
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Knight SP, Meaney JF, Fagan AJ. DCE‐MRI protocol for constraining absolute pharmacokinetic modeling errors within specific accuracy limits. Med Phys 2019; 46:3592-3602. [DOI: 10.1002/mp.13635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 04/30/2019] [Accepted: 05/21/2019] [Indexed: 01/01/2023] Open
Affiliation(s)
- Silvin P. Knight
- School of Medicine Trinity College University of Dublin Dublin Ireland
- National Centre for Advanced Medical Imaging (CAMI) St James's Hospital Dublin Ireland
| | - James F. Meaney
- School of Medicine Trinity College University of Dublin Dublin Ireland
- National Centre for Advanced Medical Imaging (CAMI) St James's Hospital Dublin Ireland
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6
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Joy A, Jacob M, Paul JS. Directionality guided non linear diffusion compressed sensing MR image reconstruction. Magn Reson Med 2019; 82:2326-2342. [PMID: 31364204 DOI: 10.1002/mrm.27895] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 05/13/2019] [Accepted: 06/14/2019] [Indexed: 11/09/2022]
Abstract
PURPOSE Address the shortcomings of edge-preserving filters to preserve the complex nature of edges, by adapting the direction of diffusion to the local variations in intensity function on a subpixel level, thereby achieving a reconstruction accuracy superior to that of data-driven learning-based approaches. THEORY AND METHODS Rate of diffusion for edges is found to vary in accordance with their gradient direction. Therefore, the edge preservation is strongly dependent on the direction in which the gradient is computed. Since the directionality of edges varies at different regions of the image, the proposed technique computes the gradients in all possible angular directions and uses a spatial-frequency-based deviation measure to choose the most reliable edges from the images diffused along different directions. RESULTS The proposed method is compared with the state-of-the-art data-driven learning-based techniques of block matching and 3D filtering (BM3D), patch-based nonlocal operator (PANO), and dictionary learning MRI (DLMRI). Best results are obtained when directionality of edges is estimated from a prior optimized k-space and shows an improvement in peak signal-to-noise ratio (PSNR) measures by a factor of 2.36 dB, 1.92 dB, and 1.59 dB over BM3D, PANO, and dictionary learning MRI, respectively. CONCLUSION The proposed technique prevents the emphasis of false edges and better captures the structural details by a locally varying directionality-guided diffusion to make the error lower than that of the state-of-the-art reconstruction techniques. In addition, a highly parallelizable form of the proposed model promises a significant gain in the reconstruction speed for practical implementations.
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Affiliation(s)
- Ajin Joy
- Medical Image Computing and Signal Processing Laboratory, Indian Institute of Information Technology and Management-Kerala, Trivandrum, India
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa
| | - Joseph Suresh Paul
- Medical Image Computing and Signal Processing Laboratory, Indian Institute of Information Technology and Management-Kerala, Trivandrum, India
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7
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Pineda FD, Easley TO, Karczmar GS. Dynamic field-of-view imaging to increase temporal resolution in the early phase of contrast media uptake in breast DCE-MRI: A feasibility study. Med Phys 2018; 45:1050-1058. [PMID: 29314060 PMCID: PMC6028013 DOI: 10.1002/mp.12747] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Revised: 12/14/2017] [Accepted: 12/15/2017] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To increase diagnostic accuracy of breast MRI by increasing temporal resolution and more accurately sampling the early kinetics of contrast media uptake. We tested the feasibility of accelerating bilateral breast DCE-MRI by reducing the FOV, allowing aliasing, and unfolding the resulting images. METHODS Previous experience with an "ultrafast" protocol for bilateral breast DCE-MRI (6-10 s temporal resolution) showed that the number of significantly enhancing voxels is very low in the first 30-45 s after contrast media injection. This suggests that overlap of enhancing voxels in aliased images will be very infrequent. Therefore, aliased images can be acquired during the first 30-45 s after contrast media injection and unfolded to produce full-FOV images with few errors. In a proof-of-principle test, aliased images were simulated from the first 30 s of full-FOV acquisitions. Cases with relatively dense early enhancement were selected to test this method in a worst-case scenario. In an initial test, an FOV of 60% the size of the full FOV was simulated. To reduce the probability of errors due to overlapping voxels in aliased images, we then tested a dynamic FOV approach. The FOV was progressively increased so that enhancing voxels could not overlap at multiple time-points, and areas where enhancing voxels overlapped at a given time-point could be unfolded by interpolating between the preceding and subsequent time-points (acquired with different FOVs). The simulated FOV sizes for each of the time-points were 31%, 44%, and 77% of the full FOV. Subtraction images (post- minus precontrast) were generated for aliased images and filtered to select significantly enhancing voxels. Comparison of early, highly aliased images, with later, less aliased images then helped to identify the true locations of enhancing voxels. RESULTS In the initial aliasing simulations, an average of 2.9% of the enhancing voxels above the chest wall overlapped in the aliased images (range 0.1%-6.7%). The similarity between simulated unfolded images and the correct full-FOV images, evaluated using CW-SSIM (complex wavelet similarity index), was 0.50 ± 0.26, 0.76 ± 0.09, and 0.80 ± 0.10 for the first, second, and third time-point, respectively (numbers closer to 1 indicate more similar images). For the dynamic FOV tests, an average of 11% of the enhancing voxels above the chest wall overlapped (range 0%-40%) due to greater aliasing at early time-points. Despite more voxels overlapping, the CW-SSIM values for the data acquired with dynamic FOVs were 0.64 ± 0.25, 0.93 ± 0.04, and 0.97 ± 0.02 for the first, second, and third time-points, respectively. CONCLUSIONS Dynamic FOV imaging allows accelerated bilateral breast DCE-MRI during the early contrast media uptake phase. This method relies on the sparsity of enhancement at the early phases of DCE-MRI of the breast. The results of simulations suggest that dynamic FOV imaging and unfolding produces images that are very close to fully sampled images, and allows temporal resolution as high as 2 s per image.
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Affiliation(s)
| | - Ty O Easley
- Department of RadiologyThe University of ChicagoChicagoIL60637USA
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Jimenez JE, Strigel RM, Johnson KM, Henze Bancroft LC, Reeder SB, Block WF. Feasibility of high spatiotemporal resolution for an abbreviated 3D radial breast MRI protocol. Magn Reson Med 2018; 80:1452-1466. [PMID: 29446125 DOI: 10.1002/mrm.27137] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 01/24/2018] [Accepted: 01/25/2018] [Indexed: 12/11/2022]
Abstract
PURPOSE To develop a volumetric imaging technique with 0.8-mm isotropic resolution and 10-s/volume rate to detect and analyze breast lesions in a bilateral, dynamic, contrast-enhanced MRI exam. METHODS A local low-rank temporal reconstruction approach that also uses parallel imaging and spatial compressed sensing was designed to create rapid volumetric frame rates during a contrast-enhanced breast exam (vastly undersampled isotropic projection [VIPR] spatial compressed sensing with temporal local low-rank [STELLR]). The dynamic-enhanced data are subtracted in k-space from static mask data to increase sparsity for the local low-rank approach to maximize temporal resolution. A T1 -weighted 3D radial trajectory (VIPR iterative decomposition with echo asymmetry and least squares estimation [IDEAL]) was modified to meet the data acquisition requirements of the STELLR approach. Additionally, the unsubtracted enhanced data are reconstructed using compressed sensing and IDEAL to provide high-resolution fat/water separation. The feasibility of the approach and the dual reconstruction methodology is demonstrated using a 16-channel breast coil and a 3T MR scanner in 6 patients. RESULTS The STELLR temporal performance of subtracted data matched the expected temporal perfusion enhancement pattern in small and large vascular structures. Differential enhancement within heterogeneous lesions is demonstrated with corroboration from a basic reconstruction using a strict 10-second temporal footprint. Rapid acquisition, reliable fat suppression, and high spatiotemporal resolution are presented, despite significant data undersampling. CONCLUSION The STELLR reconstruction approach of 3D radial sampling with mask subtraction provides a high-performance imaging technique for characterizing enhancing structures within the breast. It is capable of maintaining temporal fidelity, while visualizing breast lesions with high detail over a large FOV to include both breasts.
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Affiliation(s)
- Jorge E Jimenez
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin
| | - Roberta M Strigel
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin.,Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Carbone Cancer Center, University of Wisconsin, Madison, Wisconsin
| | - Kevin M Johnson
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin
| | - Leah C Henze Bancroft
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Scott B Reeder
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin.,Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin.,Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Walter F Block
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin.,Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin
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9
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Nakarmi U, Wang Y, Lyu J, Liang D, Ying L. A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:2297-2307. [PMID: 28692970 PMCID: PMC6422674 DOI: 10.1109/tmi.2017.2723871] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
While many low rank and sparsity-based approaches have been developed for accelerated dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input space, overlooking the intrinsic nonlinear correlation in most dMRI data. In this paper, we propose a kernel-based framework to allow nonlinear manifold models in reconstruction from sub-Nyquist data. Within this framework, many existing algorithms can be extended to kernel framework with nonlinear models. In particular, we have developed a novel algorithm with a kernel-based low-rank model generalizing the conventional low rank formulation. The algorithm consists of manifold learning using kernel, low rank enforcement in feature space, and preimaging with data consistency. Extensive simulation and experiment results show that the proposed method surpasses the conventional low-rank-modeled approaches for dMRI.
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Affiliation(s)
- Ukash Nakarmi
- Department of Electrical Engineering, University at Buffalo, NY, 14260, USA
| | - Yanhua Wang
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China
| | - Jingyuan Lyu
- Department of Electrical Engineering, University at Buffalo, NY, 14260, USA
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong 518055, China
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Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast. Int J Biomed Imaging 2017; 2017:7835749. [PMID: 28932236 PMCID: PMC5592397 DOI: 10.1155/2017/7835749] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 07/20/2017] [Indexed: 11/17/2022] Open
Abstract
PURPOSE Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled k-space data using nonlinear recovery schemes. The purpose of this paper is to quantitatively evaluate common temporal sparsity-promoting regularizers for CS DCE-MRI of the breast. METHODS We considered five ubiquitous temporal regularizers on 4.5x retrospectively undersampled Cartesian in vivo breast DCE-MRI data: Fourier transform (FT), Haar wavelet transform (WT), total variation (TV), second-order total generalized variation (TGV α2), and nuclear norm (NN). We measured the signal-to-error ratio (SER) of the reconstructed images, the error in tumor mean, and concordance correlation coefficients (CCCs) of the derived pharmacokinetic parameters Ktrans (volume transfer constant) and ve (extravascular-extracellular volume fraction) across a population of random sampling schemes. RESULTS NN produced the lowest image error (SER: 29.1), while TV/TGV α2 produced the most accurate Ktrans (CCC: 0.974/0.974) and ve (CCC: 0.916/0.917). WT produced the highest image error (SER: 21.8), while FT produced the least accurate Ktrans (CCC: 0.842) and ve (CCC: 0.799). CONCLUSION TV/TGV α2 should be used as temporal constraints for CS DCE-MRI of the breast.
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11
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Majumdar A. Causal MRI reconstruction via Kalman prediction and compressed sensing correction. Magn Reson Imaging 2017; 39:64-70. [PMID: 28167143 DOI: 10.1016/j.mri.2017.02.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2015] [Revised: 02/01/2017] [Accepted: 02/02/2017] [Indexed: 11/28/2022]
Abstract
This technical note addresses the problem of causal online reconstruction of dynamic MRI, i.e. given the reconstructed frames till the previous time instant, we reconstruct the frame at the current instant. Our work follows a prediction-correction framework. Given the previous frames, the current frame is predicted based on a Kalman estimate. The difference between the estimate and the current frame is then corrected based on the k-space samples of the current frame; this reconstruction assumes that the difference is sparse. The method is compared against prior Kalman filtering based techniques and Compressed Sensing based techniques. Experimental results show that the proposed method is more accurate than these and considerably faster.
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12
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Pineda FD, Medved M, Wang S, Fan X, Schacht DV, Sennett C, Oto A, Newstead GM, Abe H, Karczmar GS. Ultrafast Bilateral DCE-MRI of the Breast with Conventional Fourier Sampling: Preliminary Evaluation of Semi-quantitative Analysis. Acad Radiol 2016; 23:1137-44. [PMID: 27283068 PMCID: PMC4987200 DOI: 10.1016/j.acra.2016.04.008] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Revised: 01/27/2016] [Accepted: 04/12/2016] [Indexed: 12/25/2022]
Abstract
RATIONALE AND OBJECTIVES The study aimed to evaluate the feasibility and advantages of a combined high temporal and high spatial resolution protocol for dynamic contrast-enhanced magnetic resonance imaging of the breast. MATERIALS AND METHODS Twenty-three patients with enhancing lesions were imaged at 3T. The acquisition protocol consisted of a series of bilateral, fat-suppressed "ultrafast" acquisitions, with 6.9- to 9.9-second temporal resolution for the first minute following contrast injection, followed by four high spatial resolution acquisitions with 60- to 79.5-second temporal resolution. All images were acquired with standard uniform Fourier sampling. A filtering method was developed to reduce noise and detect significant enhancement in the high temporal resolution images. Time of arrival (TOA) was defined as the time at which each voxel first satisfied all the filter conditions, relative to the time of initial arterial enhancement. RESULTS Ultrafast images improved visualization of the vasculature feeding and draining lesions. A small percentage of the entire field of view (<6%) enhanced significantly in the 30 seconds following contrast injection. Lesion conspicuity was highest in early ultrafast images, especially in cases with marked parenchymal enhancement. Although the sample size was relatively small, the average TOA for malignant lesions was significantly shorter than the TOA for benign lesions. Significant differences were also measured in other parameters descriptive of early contrast media uptake kinetics (P < 0.05). CONCLUSIONS Ultrafast imaging in the first minute of dynamic contrast-enhanced magnetic resonance imaging of the breast has the potential to add valuable information on early contrast dynamics. Ultrafast imaging could allow radiologists to confidently identify lesions in the presence of marked background parenchymal enhancement.
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Affiliation(s)
- Federico D Pineda
- Department of Radiology, University of Chicago, 5841 S. Maryland Ave. MC 2026, Chicago, IL, 60637
| | - Milica Medved
- Department of Radiology, University of Chicago, 5841 S. Maryland Ave. MC 2026, Chicago, IL, 60637
| | - Shiyang Wang
- Department of Radiology, University of Chicago, 5841 S. Maryland Ave. MC 2026, Chicago, IL, 60637
| | - Xiaobing Fan
- Department of Radiology, University of Chicago, 5841 S. Maryland Ave. MC 2026, Chicago, IL, 60637
| | - David V Schacht
- Department of Radiology, University of Chicago, 5841 S. Maryland Ave. MC 2026, Chicago, IL, 60637
| | - Charlene Sennett
- Department of Radiology, University of Chicago, 5841 S. Maryland Ave. MC 2026, Chicago, IL, 60637
| | - Aytekin Oto
- Department of Radiology, University of Chicago, 5841 S. Maryland Ave. MC 2026, Chicago, IL, 60637
| | - Gillian M Newstead
- Department of Radiology, University of Chicago, 5841 S. Maryland Ave. MC 2026, Chicago, IL, 60637
| | - Hiroyuki Abe
- Department of Radiology, University of Chicago, 5841 S. Maryland Ave. MC 2026, Chicago, IL, 60637
| | - Gregory S Karczmar
- Department of Radiology, University of Chicago, 5841 S. Maryland Ave. MC 2026, Chicago, IL, 60637.
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Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study. Technol Cancer Res Treat 2016; 16:446-460. [PMID: 27215931 DOI: 10.1177/1533034616649294] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging. METHODS Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio-optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant Ktrans in the extended Tofts model and blood flow FB and blood volume VB from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter's accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated. RESULTS The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average cross-correlation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data. CONCLUSION With sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.
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Chen C, Li Y, Axel L, Huang J. Real time dynamic MRI by exploiting spatial and temporal sparsity. Magn Reson Imaging 2016; 34:473-82. [DOI: 10.1016/j.mri.2015.10.033] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Accepted: 10/26/2015] [Indexed: 11/30/2022]
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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] [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.
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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
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Yin XX, Hadjiloucas S, Zhang Y, Su MY, Miao Y, Abbott D. Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs. Artif Intell Med 2016; 67:1-23. [PMID: 26951630 DOI: 10.1016/j.artmed.2016.01.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 12/28/2015] [Accepted: 01/16/2016] [Indexed: 12/25/2022]
Abstract
OBJECTIVE We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. METHODS Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. VALIDATION Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. RESULTS Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. CONCLUSION The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community.
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Affiliation(s)
- Xiao-Xia Yin
- Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia.
| | - Sillas Hadjiloucas
- School of Systems Engineering and Department of Bioengineering, University of Reading, Reading RG6 6AY, UK
| | - Yanchun Zhang
- Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia
| | - Min-Ying Su
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, USA
| | - Yuan Miao
- College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia
| | - Derek Abbott
- Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, South Australia, SA 5000, Australia
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Lebel RM, Jones J, Ferre JC, Law M, Nayak KS. Highly accelerated dynamic contrast enhanced imaging. Magn Reson Med 2016; 71:635-44. [PMID: 23504992 DOI: 10.1002/mrm.24710] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
PURPOSE Dynamic contrast-enhanced imaging provides unique physiological information, notably the endothelial permeability (K(trans)), and may improve the diagnosis and management of multiple pathologies. Current acquisition methods provide limited spatial-temporal resolution and field-of-view, often preventing characterization of the entire pathology and precluding measurement of the arterial input function. We present a method for highly accelerated dynamic imaging and demonstrate its utility for dynamic contrast-enhanced modeling. METHODS We propose a novel Poisson ellipsoid sampling scheme and enforce multiple spatial and temporal l1-norm constraints during image reconstruction. Retrospective and prospective analyses were performed to validate the approach. RESULTS Retrospectively, no mean bias or diverging trend was observed as the acceleration rate was increased from 3× to 18×; less than 10% error was measured in K(trans) at any individual rates in this range. Prospectively accelerated images at a rate of 36× enabled full brain coverage with 0.94 × 0.94 × 1.9 mm(3) spatial and 4.1 s temporal resolutions. Images showed no visible degradation and provided accurate K(trans) values when compared to a clinical population. CONCLUSION Highly accelerated dynamic MRI using compressed sensing and parallel imaging provides accurate permeability modeling and enables full brain, high resolution acquisitions.
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Affiliation(s)
- Robert Marc Lebel
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
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18
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Han S, Cho H. Temporal resolution improvement of calibration-free dynamic contrast-enhanced MRI with compressed sensing optimized turbo spin echo: The effects of replacing turbo factor with compressed sensing accelerations. J Magn Reson Imaging 2015; 44:138-47. [DOI: 10.1002/jmri.25136] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Accepted: 12/03/2015] [Indexed: 11/09/2022] Open
Affiliation(s)
- SoHyun Han
- Department of Biomedical Engineering; Ulsan National Institute of Science and Technology; Ulsan South Korea
| | - HyungJoon Cho
- Department of Biomedical Engineering; Ulsan National Institute of Science and Technology; Ulsan South Korea
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Chen B, Zhao K, Li B, Cai W, Wang X, Zhang J, Fang J. High temporal resolution dynamic contrast-enhanced MRI using compressed sensing-combined sequence in quantitative renal perfusion measurement. Magn Reson Imaging 2015; 33:962-9. [PMID: 25967586 DOI: 10.1016/j.mri.2015.05.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Accepted: 05/06/2015] [Indexed: 10/23/2022]
Abstract
PURPOSE To demonstrate the feasibility of the improved temporal resolution by using compressed sensing (CS) combined imaging sequence in dynamic contrast-enhanced MRI (DCE-MRI) of kidney, and investigate its quantitative effects on renal perfusion measurements. MATERIALS AND METHODS Ten rabbits were included in the accelerated scans with a CS-combined 3D pulse sequence. To evaluate the image quality, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between the proposed CS strategy and the conventional full sampling method. Moreover, renal perfusion was estimated by using the separable compartmental model in both CS simulation and realistic CS acquisitions. RESULTS The CS method showed DCE-MRI images with improved temporal resolution and acceptable image contrast, while presenting significantly higher SNR than the fully sampled images (p<.01) at 2-, 3- and 4-X acceleration. In quantitative measurements, renal perfusion results were in good agreement with the fully sampled one (concordance correlation coefficient=0.95, 0.91, 0.88) at 2-, 3- and 4-X acceleration in CS simulation. Moreover, in realistic acquisitions, the estimated perfusion by the separable compartmental model exhibited no significant differences (p>.05) between each CS-accelerated acquisition and the full sampling method. CONCLUSION The CS-combined 3D sequence could improve the temporal resolution for DCE-MRI in kidney while yielding diagnostically acceptable image quality, and it could provide effective measurements of renal perfusion.
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Affiliation(s)
- Bin Chen
- Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China
| | - Kai Zhao
- Dept. of Radiology, Peking University First Hospital, 100034, Beijing, China
| | - Bo Li
- College of Engineering, Peking University, 100871, Beijing, China
| | - Wenchao Cai
- Dept. of Radiology, Peking University First Hospital, 100034, Beijing, China
| | - Xiaoying Wang
- Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China; Dept. of Radiology, Peking University First Hospital, 100034, Beijing, China
| | - Jue Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China; College of Engineering, Peking University, 100871, Beijing, China.
| | - Jing Fang
- Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China; College of Engineering, Peking University, 100871, Beijing, China
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Li Q, Qu X, Liu Y, Guo D, Lai Z, Ye J, Chen Z. Accelerating patch-based directional wavelets with multicore parallel computing in compressed sensing MRI. Magn Reson Imaging 2015; 33:649-58. [PMID: 25620521 DOI: 10.1016/j.mri.2015.01.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2014] [Revised: 08/23/2014] [Accepted: 01/18/2015] [Indexed: 10/24/2022]
Abstract
Compressed sensing MRI (CS-MRI) is a promising technology to accelerate magnetic resonance imaging. Both improving the image quality and reducing the computation time are important for this technology. Recently, a patch-based directional wavelet (PBDW) has been applied in CS-MRI to improve edge reconstruction. However, this method is time consuming since it involves extensive computations, including geometric direction estimation and numerous iterations of wavelet transform. To accelerate computations of PBDW, we propose a general parallelization of patch-based processing by taking the advantage of multicore processors. Additionally, two pertinent optimizations, excluding smooth patches and pre-arranged insertion sort, that make use of sparsity in MR images are also proposed. Simulation results demonstrate that the acceleration factor with the parallel architecture of PBDW approaches the number of central processing unit cores, and that pertinent optimizations are also effective to make further accelerations. The proposed approaches allow compressed sensing MRI reconstruction to be accomplished within several seconds.
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Affiliation(s)
- Qiyue Li
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen 361005, China; Department of Communication Engineering, Xiamen University, Xiamen 361005, China
| | - Xiaobo Qu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen 361005, China.
| | - Yunsong Liu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen 361005, China
| | - Di Guo
- School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
| | - Zongying Lai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen 361005, China; Department of Communication Engineering, Xiamen University, Xiamen 361005, China
| | - Jing Ye
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen 361005, China
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Research, Xiamen University, Xiamen 361005, China; Department of Communication Engineering, Xiamen University, Xiamen 361005, China
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Wang Y, Ying L. Compressed Sensing Dynamic Cardiac Cine MRI Using Learned Spatiotemporal Dictionary. IEEE Trans Biomed Eng 2014; 61:1109-20. [DOI: 10.1109/tbme.2013.2294939] [Citation(s) in RCA: 85] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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22
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Smith DS, Li X, Abramson RG, Chad Quarles C, Yankeelov TE, Brian Welch E. Potential of compressed sensing in quantitative MR imaging of cancer. Cancer Imaging 2013; 13:633-44. [PMID: 24434808 PMCID: PMC3893904 DOI: 10.1102/1470-7330.2013.0041] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/29/2013] [Indexed: 12/22/2022] Open
Abstract
Classic signal processing theory dictates that, in order to faithfully reconstruct a band-limited signal (e.g., an image), the sampling rate must be at least twice the maximum frequency contained within the signal, i.e., the Nyquist frequency. Recent developments in applied mathematics, however, have shown that it is often possible to reconstruct signals sampled below the Nyquist rate. This new method of compressed sensing (CS) requires that the signal have a concise and extremely dense representation in some mathematical basis. Magnetic resonance imaging (MRI) is particularly well suited for CS approaches, owing to the flexibility of data collection in the spatial frequency (Fourier) domain available in most MRI protocols. With custom CS acquisition and reconstruction strategies, one can quickly obtain a small subset of the full data and then iteratively reconstruct images that are consistent with the acquired data and sparse by some measure. Successful use of CS results in a substantial decrease in the time required to collect an individual image. This extra time can then be harnessed to increase spatial resolution, temporal resolution, signal-to-noise, or any combination of the three. In this article, we first review the salient features of CS theory and then discuss the specific barriers confronting CS before it can be readily incorporated into clinical quantitative MRI studies of cancer. We finally illustrate applications of the technique by describing examples of CS in dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI.
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Affiliation(s)
- David S. Smith
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - Xia Li
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - Richard G. Abramson
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - C. Chad Quarles
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - Thomas E. Yankeelov
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
| | - E. Brian Welch
- Institute of Imaging Science, Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, TN, USA
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Majumdar A. Motion predicted online dynamic MRI reconstruction from partially sampled k-space data. Magn Reson Imaging 2013; 31:1578-86. [DOI: 10.1016/j.mri.2013.06.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Revised: 05/31/2013] [Accepted: 06/03/2013] [Indexed: 11/29/2022]
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Ning B, Qu X, Guo D, Hu C, Chen Z. Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization. Magn Reson Imaging 2013; 31:1611-22. [DOI: 10.1016/j.mri.2013.07.010] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Revised: 07/03/2013] [Accepted: 07/21/2013] [Indexed: 11/24/2022]
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25
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Majumdar A. Improved dynamic MRI reconstruction by exploiting sparsity and rank-deficiency. Magn Reson Imaging 2013; 31:789-95. [DOI: 10.1016/j.mri.2012.10.026] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2012] [Revised: 10/17/2012] [Accepted: 10/30/2012] [Indexed: 11/29/2022]
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26
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Non-convex algorithm for sparse and low-rank recovery: Application to dynamic MRI reconstruction. Magn Reson Imaging 2013; 31:448-55. [DOI: 10.1016/j.mri.2012.08.011] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2012] [Revised: 07/19/2012] [Accepted: 08/30/2012] [Indexed: 11/24/2022]
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Majumdar A, Ward RK, Aboulnasr T. Compressed sensing based real-time dynamic MRI reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:2253-66. [PMID: 22949054 DOI: 10.1109/tmi.2012.2215921] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
This work addresses the problem of real-time online reconstruction of dynamic magnetic resonance imaging sequences. The proposed method reconstructs the difference between the previous and the current image frames. This difference image is sparse. We recover the sparse difference image from its partial k-space scans by using a nonconvex compressed sensing algorithm. As there was no previous fast enough algorithm for real-time reconstruction, we derive a novel algorithm for this purpose. Our proposed method has been compared against state-of-the-art offline and online reconstruction methods. The accuracy of the proposed method is less than offline methods but noticeably higher than the online techniques. For real-time reconstruction we are also concerned about the reconstruction speed. Our method is capable of reconstructing 128 × 128 images at the rate of 6 frames/s, 180 × 180 images at the rate of 5 frames/s and 256 × 256 images at the rate of 2.5 frames/s.
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Affiliation(s)
- Angshul Majumdar
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T1Z4, Canada.
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28
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Majumdar A, Ward RK. Causal dynamic MRI reconstruction via nuclear norm minimization. Magn Reson Imaging 2012; 30:1483-94. [DOI: 10.1016/j.mri.2012.04.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2011] [Revised: 04/02/2012] [Accepted: 04/18/2012] [Indexed: 11/27/2022]
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29
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Sung K, Daniel BL, Hargreaves BA. Location constrained approximate message passing for compressed sensing MRI. Magn Reson Med 2012; 70:370-81. [PMID: 23042658 DOI: 10.1002/mrm.24468] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2011] [Revised: 07/16/2012] [Accepted: 08/01/2012] [Indexed: 11/07/2022]
Abstract
Iterative thresholding methods have been extensively studied as faster alternatives to convex optimization methods for solving large-sized problems in compressed sensing. A novel iterative thresholding method called LCAMP (Location Constrained Approximate Message Passing) is presented for reducing computational complexity and improving reconstruction accuracy when a nonzero location (or sparse support) constraint can be obtained from view shared images. LCAMP modifies the existing approximate message passing algorithm by replacing the thresholding stage with a location constraint, which avoids adjusting regularization parameters or thresholding levels. This work is first compared with other conventional reconstruction methods using random one-dimention signals and then applied to dynamic contrast-enhanced breast magnetic resonance imaging to demonstrate the excellent reconstruction accuracy (less than 2% absolute difference) and low computation time (5-10 s using Matlab) with highly undersampled three-dimentional data (244 × 128 × 48; overall reduction factor = 10).
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Affiliation(s)
- Kyunghyun Sung
- Department of Radiology, Stanford University, Stanford, California, USA.
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Chen L, Adluru G, Schabel MC, McGann CJ, Dibella EVR. Myocardial perfusion MRI with an undersampled 3D stack-of-stars sequence. Med Phys 2012; 39:5204-11. [PMID: 22894445 DOI: 10.1118/1.4738965] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To determine the feasibility of three-dimensional (3D) hybrid radial (stack-of-stars) MRI with spatiotemporal total variation (TV) constrained reconstruction for dynamic contrast enhanced myocardial perfusion imaging. METHODS An ECG-triggered saturation recovery turboFLASH sequence with undersampled stack-of-stars sampling with spatiotemporal TV constrained reconstruction was developed for dynamic contrast enhanced myocardial perfusion imaging. Simulations were performed to study the dependence of the approach to steady state on flip angle and saturation recovery time for this stack-of-stars acquisition. Phantom studies were used to show the effect of the flip angle selection and imperfect spoiling on image qualities. Studies were done in three humans to test the feasibility of the approach for myocardial perfusion imaging. RESULTS The simulation and phantom studies showed that imperfect spoiling and magnetization changes during the readout were a function of flip angle and nonoptimized selection of flip angle could degrade the images. Low flip angle acquisitions in the human subjects result in images with good quality similar to multislice radial 2D images. CONCLUSIONS 3D stack-of-stars sampling with spatiotemporal TV constrained reconstruction provides a promising alternative for myocardial perfusion imaging.
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Affiliation(s)
- Liyong Chen
- Department of Bioengineering, University of Utah, Salt Lake City, Utah 84108, USA
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A feasible high spatiotemporal resolution breast DCE-MRI protocol for clinical settings. Magn Reson Imaging 2012; 30:1257-67. [PMID: 22770687 DOI: 10.1016/j.mri.2012.04.009] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2012] [Revised: 04/07/2012] [Accepted: 04/18/2012] [Indexed: 11/23/2022]
Abstract
Three dimensional bilateral imaging is the standard for most clinical breast dynamic contrast-enhanced (DCE) MRI protocols. Because of high spatial resolution (sRes) requirement, the typical 1-2 min temporal resolution (tRes) afforded by a conventional full-k-space-sampling gradient echo (GRE) sequence precludes meaningful and accurate pharmacokinetic analysis of DCE time-course data. The commercially available, GRE-based, k-space undersampling and data sharing TWIST (time-resolved angiography with stochastic trajectories) sequence was used in this study to perform DCE-MRI exams on thirty one patients (with 36 suspicious breast lesions) before their biopsies. The TWIST DCE-MRI was immediately followed by a single-frame conventional GRE acquisition. Blinded from each other, three radiologist readers assessed agreements in multiple lesion morphology categories between the last set of TWIST DCE images and the conventional GRE images. Fleiss' κ test was used to evaluate inter-reader agreement. The TWIST DCE time-course data were subjected to quantitative pharmacokinetic analyses. With a four-channel phased-array breast coil, the TWIST sequence produced DCE images with 20 s or less tRes and ~ 1.0×1.0×1.4 mm(3) sRes. There were no significant differences in signal-to-noise (P=.45) and contrast-to-noise (P=.51) ratios between the TWIST and conventional GRE images. The agreements in morphology evaluations between the two image sets were excellent with the intra-reader agreement ranging from 79% for mass margin to 100% for mammographic density and the inter-reader κ value ranging from 0.54 (P<.0001) for lesion size to 1.00 (P<.0001) for background parenchymal enhancement. Quantitative analyses of the DCE time-course data provided higher breast cancer diagnostic accuracy (91% specificity at 100% sensitivity) than the current clinical practice of morphology and qualitative kinetics assessments. The TWIST sequence may be used in clinical settings to acquire high spatiotemporal resolution breast DCE-MRI images for both precise lesion morphology characterization and accurate pharmacokinetic analysis.
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Han S, Paulsen JL, Zhu G, Song Y, Chun S, Cho G, Ackerstaff E, Koutcher JA, Cho H. Temporal/spatial resolution improvement of in vivo DCE-MRI with compressed sensing-optimized FLASH. Magn Reson Imaging 2012; 30:741-52. [PMID: 22465192 PMCID: PMC3792168 DOI: 10.1016/j.mri.2012.02.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2011] [Revised: 11/14/2011] [Accepted: 02/14/2012] [Indexed: 11/26/2022]
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides critical information regarding tumor perfusion and permeability by injecting a T(1) contrast agent, such as Gd-DTPA, and making a time-resolved measurement of signal increase. Both temporal and spatial resolutions are required to be high to achieve an accurate and reproducible estimation of tumor perfusion. However, the dynamic nature of the DCE experiment limits simultaneous improvement of temporal and spatial resolution by conventional methods. Compressed sensing (CS) has become an important tool for the acceleration of imaging times in MRI, which is achieved by enabling the reconstruction of subsampled data. Similarly, CS algorithms can be utilized to improve the temporal/spatial resolution of DCE-MRI, and several works describing retrospective simulations have demonstrated the feasibility of such improvements. In this study, the fast low angle shot sequence was modified to implement a Cartesian, CS-optimized, sub-Nyquist phase encoding acquisition/reconstruction with multiple two-dimensional slice selections and was tested on water phantoms and animal tumor models. The mean voxel-level concordance correlation coefficient for Ak(ep) values obtained from ×4 and ×8 accelerated and the fully sampled data was 0.87±0.11 and 0.83±0.11, respectively (n=6), with optimized CS parameters. In this case, the reduction of phase encoding steps made possible by CS reconstruction improved effectively the temporal/spatial resolution of DCE-MRI data using an in vivo animal tumor model (n=6) and may be useful for the investigation of accelerated acquisitions in preclinical and clinical DCE-MRI trials.
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Affiliation(s)
- SoHyun Han
- School of Nano-Bioscience and Chemical Engineering, UNIST, Ulsan, Republic of Korea
| | | | - Gang Zhu
- Bruker BioSpin, Billerica, MA, USA
| | - Youngkyu Song
- Korea Basic Science Institute, Ochang, Republic of Korea
| | - SongI Chun
- Korea Basic Science Institute, Ochang, Republic of Korea
| | - Gyunggoo Cho
- Korea Basic Science Institute, Ochang, Republic of Korea
| | | | | | - HyungJoon Cho
- School of Nano-Bioscience and Chemical Engineering, UNIST, Ulsan, Republic of Korea
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Smith DS, Li X, Gambrell JV, Arlinghaus LR, Quarles CC, Yankeelov TE, Welch EB. Robustness of quantitative compressive sensing MRI: the effect of random undersampling patterns on derived parameters for DCE- and DSC-MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:504-511. [PMID: 22010146 PMCID: PMC3289060 DOI: 10.1109/tmi.2011.2172216] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Compressive sensing (CS) in Cartesian magnetic resonance imaging (MRI) involves random partial Fourier acquisitions. The random nature of these acquisitions can lead to variance in reconstruction errors. In quantitative MRI, variance in the reconstructed images translates to an uncertainty in the derived quantitative maps. We show that for a spatially regularized 2 ×-accelerated human breast CS DCE-MRI acquisition with a 192 (2) matrix size, the coefficients of variation (CoVs) in voxel-level parameters due to the random acquisition are 1.1%, 0.96%, and 1.5% for the tissue parameters K(trans), v(e), and v(p), with an average error in the mean of -2.5%, -2.0%, and -3.7%, respectively. Only 5% of the acquisition schemes had a systematic underestimation larger than than 4.2%, 3.7%, and 6.1%, respectively. For a 2 × -accelerated rat brain CS DSC-MRI study with a 64(2) matrix size, the CoVs due to the random acquisition were 19%, 9.5%, and 15% for the cerebral blood flow and blood volume and mean transit time, respectively, and the average errors in the tumor mean were 9.2%, 0.49%, and -7.0%, respectively. Across 11 000 different CS reconstructions, we saw no outliers in the distribution of parameters, suggesting that, despite the random undersampling schemes, CS accelerated quantitative MRI may have a predictable level of performance.
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Affiliation(s)
- David S Smith
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37240 USA.
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Liang D, DiBella EVR, Chen RR, Ying L. k-t ISD: dynamic cardiac MR imaging using compressed sensing with iterative support detection. Magn Reson Med 2011; 68:41-53. [PMID: 22113706 DOI: 10.1002/mrm.23197] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2010] [Revised: 07/14/2011] [Accepted: 08/02/2011] [Indexed: 11/07/2022]
Abstract
Compressed sensing (CS) has been used in dynamic cardiac MRI to reduce the data acquisition time. The sparseness of the dynamic image series in the spatial- and temporal-frequency (x-f) domain has been exploited in existing works. In this article, we propose a new k-t iterative support detection (k-t ISD) method to improve the CS reconstruction for dynamic cardiac MRI by incorporating additional information on the support of the dynamic image in x-f space based on the theory of CS with partially known support. The proposed method uses an iterative procedure for alternating between image reconstruction and support detection in x-f space. At each iteration, a truncated ℓ(1) minimization is applied to obtain the reconstructed image in x-f space using the support information from the previous iteration. Subsequently, by thresholding the reconstruction, we update the support information to be used in the next iteration. Experimental results demonstrate that the proposed k-t ISD method improves the reconstruction quality of dynamic cardiac MRI over the basic CS method in which support information is not exploited.
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Affiliation(s)
- Dong Liang
- Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53211, USA
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Exploiting rank deficiency and transform domain sparsity for MR image reconstruction. Magn Reson Imaging 2011; 30:9-18. [PMID: 21937179 DOI: 10.1016/j.mri.2011.07.021] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2011] [Revised: 07/08/2011] [Accepted: 07/27/2011] [Indexed: 11/23/2022]
Abstract
The reconstruction of magnetic resonance (MR) images from the partial samples of their k-space data using compressed sensing (CS)-based methods has generated a lot of interest in recent years. To reconstruct the MR images, these techniques exploit the sparsity of the image in a transform domain (wavelets, total variation, etc.). In a recent work, it has been shown that it is also possible to reconstruct MR images by exploiting their rank deficiency. In this work, it will be shown that, instead of exploiting the sparsity of the image or rank deficiency alone, better reconstruction results can be achieved by combining transform domain sparsity with rank deficiency. To reconstruct an MR image using its transform domain sparsity and its rank deficiency, this work proposes a combined l(1)-norm (of the transform coefficients) and nuclear norm (of the MR image matrix) minimization problem. Since such an optimization problem has not been encountered before, this work proposes and derives a first-order algorithm to solve it. The reconstruction results show that the proposed approach yields significant improvements, in terms of both visual quality as well as the signal to noise ratio, over previous works that reconstruct MR images either by exploiting rank deficiency or by the standard CS-based technique popularly known as the 'Sparse MRI.'
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Hu C, Qu X, Guo D, Bao L, Chen Z. Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI. Magn Reson Imaging 2011; 29:907-15. [DOI: 10.1016/j.mri.2011.04.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2011] [Revised: 04/12/2011] [Accepted: 04/22/2011] [Indexed: 11/29/2022]
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37
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Smith DS, Welch EB, Li X, Arlinghaus LR, Loveless ME, Koyama T, Gore JC, Yankeelov TE. Quantitative effects of using compressed sensing in dynamic contrast enhanced MRI. Phys Med Biol 2011; 56:4933-46. [PMID: 21772079 DOI: 10.1088/0031-9155/56/15/018] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) involves the acquisition of images before, during and after the injection of a contrast agent. In order to perform quantitative modeling on the resulting signal intensity time course, data must be acquired rapidly, which compromises spatial resolution, signal to noise and/or field of view. One approach that may allow for gains in temporal or spatial resolution or signal to noise of an individual image is to use compressed sensing (CS) MRI. In this study, we demonstrate the accuracy of extracted pharmacokinetic parameters from DCE-MRI data obtained as part of pre-clinical and clinical studies in which fully sampled acquisitions have been retrospectively undersampled by factors of 2, 3 and 4 in Fourier space and then reconstructed with CS. The mean voxel-level concordance correlation coefficient for K(trans) (i.e. the volume transfer constant) obtained from the 2× accelerated and the fully sampled data is 0.92 and 0.90 for mouse and human data, respectively; for 3×, the results are 0.79 and 0.79, respectively; for 4×, the results are 0.64 and 0.70, respectively. The mean error in the tumor mean K(trans) for the mouse and human data at 2× acceleration is 1.8% and -4.2%, respectively; at 3×, 3.6% and -10%, respectively; at 4×, 7.8% and -12%, respectively. These results suggest that CS combined with appropriate reduced acquisitions may be an effective approach to improving image quality in DCE-MRI.
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Affiliation(s)
- David S Smith
- Institute of Imaging Science, Vanderbilt University, Nashville, TN 37212, USA.
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Chan RW, Ramsay EA, Cheung EY, Plewes DB. The influence of radial undersampling schemes on compressed sensing reconstruction in breast MRI. Magn Reson Med 2011; 67:363-77. [DOI: 10.1002/mrm.23008] [Citation(s) in RCA: 72] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2011] [Revised: 03/31/2011] [Accepted: 04/28/2011] [Indexed: 12/24/2022]
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