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Pei H, Shepherd TM, Wang Y, Liu F, Sodickson DK, Ben-Eliezer N, Feng L. DeepEMC-T 2 mapping: Deep learning-enabled T 2 mapping based on echo modulation curve modeling. Magn Reson Med 2024; 92:2707-2722. [PMID: 39129209 PMCID: PMC11436299 DOI: 10.1002/mrm.30239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 07/14/2024] [Accepted: 07/15/2024] [Indexed: 08/13/2024]
Abstract
PURPOSE Echo modulation curve (EMC) modeling enables accurate quantification of T2 relaxation times in multi-echo spin-echo (MESE) imaging. The standard EMC-T2 mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T2 mapping, called DeepEMC-T2 mapping, to efficiently estimate accurate T2 maps from fewer echoes. METHODS DeepEMC-T2 mapping was developed using a modified U-Net to estimate both T2 and proton density (PD) maps directly from MESE images. The network implements several new features to improve the accuracy of T2/PD estimation. A total of 67 MESE datasets acquired in axial orientation were used for network training and evaluation. An additional 57 datasets acquired in coronal orientation with different scan parameters were used to evaluate the generalizability of the framework. The performance of DeepEMC-T2 mapping was evaluated in seven experiments. RESULTS Compared to the reference, DeepEMC-T2 mapping achieved T2 estimation errors from 1% to 11% and PD estimation errors from 0.4% to 1.5% with ten/seven/five/three echoes, which are more accurate than standard EMC-T2 mapping. By incorporating datasets acquired with different scan parameters and orientations for joint training, DeepEMC-T2 exhibits robust generalizability across varying imaging protocols. Increasing the echo spacing and including longer echoes improve the accuracy of parameter estimation. The new features proposed in DeepEMC-T2 mapping all enabled more accurate T2 estimation. CONCLUSIONS DeepEMC-T2 mapping enables simplified, efficient, and accurate T2 quantification directly from MESE images without dictionary matching. Accurate T2 estimation from fewer echoes allows for increased volumetric coverage and/or higher slice resolution without prolonging total scan times.
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Affiliation(s)
- Haoyang Pei
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, USA
- Department of Electrical and Computer Engineering and Department of Biomedical Engineering, NYU Tandon School of Engineering, New York, NY, USA
| | - Timothy M. Shepherd
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, USA
| | - Yao Wang
- Department of Electrical and Computer Engineering and Department of Biomedical Engineering, NYU Tandon School of Engineering, New York, NY, USA
| | - Fang Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Daniel K Sodickson
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, USA
| | - Noam Ben-Eliezer
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, USA
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel-Aviv, Israel
| | - Li Feng
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, USA
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Zhao T, Tang J, Krumpelman C, Moum SJ, Russin JJ, Ansari SA, Chen Z, Feng L, Yan L. Highly accelerated non-contrast-enhanced time-resolved 4D MRA using stack-of-stars golden-angle radial acquisition with a self-calibrated low-rank subspace reconstruction. Magn Reson Med 2024. [PMID: 39344291 DOI: 10.1002/mrm.30304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 08/08/2024] [Accepted: 08/29/2024] [Indexed: 10/01/2024]
Abstract
PURPOSE To develop a highly accelerated non-contrast-enhanced 4D-MRA technique by combining stack-of-stars golden-angle radial acquisition with a modified self-calibrated low-rank subspace reconstruction. METHODS A low-rank subspace reconstruction framework was introduced in radial 4D MRA (SUPER 4D MRA) by combining stack-of-stars golden-angle radial acquisition with control-label k-space subtraction-based low-rank subspace modeling. Radial 4D MRA data were acquired and reconstructed using the proposed technique on 12 healthy volunteers and 1 patient with steno-occlusive disease. The performance of SUPER 4D MRA was compared with two temporally constrained reconstruction methods (golden-angle radial sparse parallel [GRASP] and GRASP-Pro) at different acceleration rates in terms of image quality and delineation of blood dynamics. RESULTS SUPER 4D MRA outperformed the other two reconstruction methods, offering superior image quality with a clear background and detailed delineation of cerebrovascular structures as well as great temporal fidelity in blood flow dynamics. SUPER 4D MRA maintained excellent performance even at higher acceleration rates. CONCLUSIONS SUPER 4D MRA is a promising technique for highly accelerating 4D MRA acquisition without comprising both temporal fidelity and image quality.
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Affiliation(s)
- Tianrui Zhao
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois, USA
| | - Jianing Tang
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois, USA
| | - Chase Krumpelman
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Sarah J Moum
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA
| | - Jonathan J Russin
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Sameer A Ansari
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Zhifeng Chen
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Lirong Yan
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois, USA
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Chen J, Huang C, Shanbhogue K, Xia D, Bruno M, Huang Y, Block KT, Chandarana H, Feng L. DCE-MRI of the liver with sub-second temporal resolution using GRASP-Pro with navi-stack-of-stars sampling. NMR IN BIOMEDICINE 2024:e5262. [PMID: 39323100 DOI: 10.1002/nbm.5262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 09/09/2024] [Accepted: 09/10/2024] [Indexed: 09/27/2024]
Abstract
Respiratory motion-induced image blurring and artifacts can compromise image quality in dynamic contrast-enhanced MRI (DCE-MRI) of the liver. Despite remarkable advances in respiratory motion detection and compensation in past years, these techniques have not yet seen widespread clinical adoption. The accuracy of image-based motion detection can be especially compromised in the presence of contrast enhancement and/or in situations involving deep and/or irregular breathing patterns. This work proposes a framework that combines GRASP-Pro (Golden-angle RAdial Sparse Parallel MRI with imProved performance) MRI with a new radial sampling scheme called navi-stack-of-stars for free-breathing DCE-MRI of the liver without the need for explicit respiratory motion compensation. A prototype 3D golden-angle radial sequence with a navi-stack-of-stars sampling scheme that intermittently acquires a 2D navigator was implemented. Free-breathing DCE-MRI of the liver was conducted in 24 subjects at 3T including 17 volunteers and 7 patients. GRASP-Pro reconstruction was performed with a temporal resolution of 0.34-0.45 s per volume, whereas standard GRASP reconstruction was performed with a temporal resolution of 15 s per volume. Motion compensation was not performed in all image reconstruction tasks. Liver images in different contrast phases from both GRASP and GRASP-Pro reconstructions were visually scored by two experienced abdominal radiologists for comparison. The nonparametric paired two-tailed Wilcoxon signed-rank test was used to compare image quality scores, and the Cohen's kappa coefficient was calculated to evaluate the inter-reader agreement. GRASP-Pro MRI with sub-second temporal resolution consistently received significantly higher image quality scores (P < 0.05) than standard GRASP MRI throughout all contrast enhancement phases and across all assessment categories. There was a substantial inter-reader agreement for all assessment categories (ranging from 0.67 to 0.89). The proposed technique using GRASP-Pro reconstruction with navi-stack-of-stars sampling holds great promise for free-breathing DCE-MRI of the liver without respiratory motion compensation.
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Affiliation(s)
- Jingjia Chen
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Chenchan Huang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Krishna Shanbhogue
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Ding Xia
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mary Bruno
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Yuhui Huang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Kai Tobias Block
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Li Feng
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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Chen J, Xia D, Huang C, Shanbhogue K, Chandarana H, Feng L. Free-breathing time-resolved 4D MRI with improved T1-weighting contrast. NMR IN BIOMEDICINE 2024:e5247. [PMID: 39183645 DOI: 10.1002/nbm.5247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 08/06/2024] [Accepted: 08/08/2024] [Indexed: 08/27/2024]
Abstract
This work proposes MP-Grasp4D (magnetization-prepared golden-angle radial sparse parallel 4D) MRI, a free-breathing, inversion recovery (IR)-prepared, time-resolved 4D MRI technique with improved T1-weighted contrast. MP-Grasp4D MRI acquisition incorporates IR preparation into a radial gradient echo sequence. MP-Grasp4D employs a golden-angle navi-stack-of-stars sampling scheme, where imaging data of rotating radial stacks and navigator stacks (acquired at a consistent rotation angle) are alternately acquired. The navigator stacks are used to estimate a temporal basis for low-rank subspace-constrained reconstruction. This allows for the simultaneous capture of both IR-induced contrast changes and respiratory motion. One temporal frame of the imaging volume in MP-Grasp4D MRI is reconstructed from a single stack and an adjacent navigator stack on average, resulting in a nominal temporal resolution of 0.16 seconds per volume. Images corresponding to the optimal inversion time (TI) can be retrospectively selected for providing the best image contrast. Reader studies were conducted to assess the performance of MP-Grasp4D MRI in liver imaging across 30 subjects in comparison with standard Grasp4D MRI without IR preparation. MP-Grasp4D MRI received significantly higher scores (P < 0.05) than Grasp4D in all assessment categories. There was a moderate to almost perfect agreement (kappa coefficient from 0.42 to 0.9) between the two readers for image quality assessment. When the scan time is reduced, MP-Grasp4D MRI preserves image contrast and quality, demonstrating additional acceleration capability. MP-Grasp4D MRI improves T1-weighted contrast for free-breathing time-resolved 4D MRI and eliminates the need for explicit motion compensation. This method is expected to be valuable in different MRI applications such as MR-guided radiotherapy.
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Affiliation(s)
- Jingjia Chen
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Ding Xia
- Icahn School of Medicine at Mount Sinai, Biomedical Engineering and Imaging Institute, New York, New York, USA
| | - Chenchan Huang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Krishna Shanbhogue
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Li Feng
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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Li Z, Sun A, Liu C, Sun H, Wei H, Wang S, Li R. Technical Note: Swing golden angle - A navigator-interleaved golden angle trajectory with eddy current suppression - Application in free-running cardiac MRI. Med Phys 2024; 51:5283-5294. [PMID: 38837254 DOI: 10.1002/mp.17188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 02/20/2024] [Accepted: 03/23/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Golden angle (GA) radial trajectory is advantageous for dynamic magnetic resonance imaging (MRI). Recently, several advanced algorithms have been developed based on navigator-interleaved GA trajectory to realize free-running cardiac MRI. However, navigator-interleaved GA trajectory suffers from the eddy-current effect, which reduces the image quality. PURPOSE This work aims to integrate the navigator-interleaved GA trajectory with clinical cardiac MRI acquisition, with the minimum eddy-current artifacts. The ultimate goal is to realize a high-quality free-running cardiac imaging technique. METHODS In this paper, we propose a new "swing golden angle" (swingGA) radial profile order. SwingGA samples the k-space by rotating back and forth at the generalized golden ratio interval, with smoothly interleaved navigator readouts. The sampling efficiency and angle increment distributions were investigated by numerical simulations. Static phantom imaging experiments were conducted to evaluate the eddy current effect, compared with cartesian, golden angle radial (GA), and tiny golden angle (tGA) trajectories. Furthermore, 12 heart-healthy subjects (aged 21-25 years) were recruited for free-running cardiac imaging with different sampling trajectories. Dynamic images were reconstructed by a low-rank subspace-constrained algorithm. The image quality was evaluated by signal-to-noise-ratio and spectrum analysis in the heart region, and compared with traditional clinical cardiac MRI images. RESULTS SwingGA pattern achieves the highest sampling efficiency (mSE > 0.925) and the minimum azimuthal angle increment (mAD < 1.05). SwingGA can effectively suppress eddy currents in static phantom images, with the lowest normalized root mean square error (nRMSE) values among radial trajectories. For the in-vivo cardiac images, swingGA enjoys the highest SNR both in the blood pool and myocardium, and contains the minimum level of high-frequency artifacts. The free-running cardiac images have good consistency with traditional clinical cardiac MRI, and the swingGA sampling pattern achieves the best image quality among all sampling patterns. CONCLUSIONS The proposed swingGA sampling pattern can effectively improve the sampling efficiency and reduce the eddy currents for the navigator-interleaved GA sequence. SwingGA is a promising sampling pattern for free-running cardiac MRI.
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Affiliation(s)
- Zhongsen Li
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Aiqi Sun
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, USA
| | - Chuyu Liu
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Haozhong Sun
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Haining Wei
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Shuai Wang
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Rui Li
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
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Kratochvíla J, Jiřík R, Bartoš M, Standara M, Starčuk Z, Taxt T. Blind deconvolution decreases requirements on temporal resolution of DCE-MRI: Application to 2nd generation pharmacokinetic modeling. Magn Reson Imaging 2024; 109:238-248. [PMID: 38508292 DOI: 10.1016/j.mri.2024.03.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 03/08/2024] [Accepted: 03/16/2024] [Indexed: 03/22/2024]
Abstract
PURPOSE Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied. METHODS The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution. RESULTS The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s. CONCLUSION We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.
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Affiliation(s)
- Jiří Kratochvíla
- Czech Academy of Sciences, Institute of Scientific Instruments, Královopolská 147, 612 64 Brno, Czech Republic.
| | - Radovan Jiřík
- Czech Academy of Sciences, Institute of Scientific Instruments, Královopolská 147, 612 64 Brno, Czech Republic
| | - Michal Bartoš
- Czech Academy of Sciences, Institute of Information Technology and Automation, Pod Vodárenskou věží 4, 182 08 Praha 8, Czech Republic
| | - Michal Standara
- Department of Radiology, Masaryk Memorial Cancer Institute, Žlutý kopec 7, 656 53 Brno, Czech Republic
| | - Zenon Starčuk
- Czech Academy of Sciences, Institute of Scientific Instruments, Královopolská 147, 612 64 Brno, Czech Republic
| | - Torfinn Taxt
- Department of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen, Norway
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Chen L, Xu J, Liu D, Ji B, Wang J, Zeng X, Zhang J, Feng L. High-resolution free-breathing hepatobiliary phase MRI of the liver using XD-GRASP. Magn Reson Imaging 2024; 109:42-48. [PMID: 38447629 DOI: 10.1016/j.mri.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/25/2024] [Accepted: 03/01/2024] [Indexed: 03/08/2024]
Abstract
PURPOSE To evaluate the performance of high-resolution free-breathing (FB) hepatobiliary phase imaging of the liver using the eXtra-Dimension Golden-angle RAdial Sparse Parallel (XD-GRASP) MRI technique. METHODS Fifty-eight clinical patients (41 males, mean age = 52.9 ± 12.9) with liver lesions who underwent dynamic contrast-enhanced MRI with a liver-specific contrast agent were prospectively recruited for this study. Both breath-hold volumetric interpolated examination (BH-VIBE) imaging and FB imaging were performed during the hepatobiliary phase. FB images were acquired using a stack-of-stars golden-angle radial sequence and were reconstructed using the XD-GRASP method. Two experienced radiologists blinded to acquisition schemes independently scored the overall image quality, liver edge sharpness, hepatic vessel clarity, conspicuity of lesion, and overall artifact level of each image. The non-parametric paired two-tailed Wilcoxon signed-rank test was used for statistical analysis. RESULTS Compared to BH-VIBE images, XD-GRASP images received significantly higher scores (P < 0.05) for the liver edge sharpness (4.83 ± 0.45 vs 4.29 ± 0.46), the hepatic vessel clarity (4.64 ± 0.67 vs 4.15 ± 0.56) and the conspicuity of lesion (4.75 ± 0.53 vs 4.31 ± 0.50). There were no significant differences (P > 0.05) between BH-VIBE and XD-GRASP images for the overall image quality (4.61 ± 0.50 vs 4.74 ± 0.47) and the overall artifact level (4.13 ± 0.44 vs 4.05 ± 0.61). CONCLUSION Compared to conventional BH-VIBE MRI, FB radial acquisition combined with XD-GRASP reconstruction facilitates higher spatial resolution imaging of the liver during the hepatobiliary phase. This enhancement can significantly improve the visualization and evaluation of the liver.
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Affiliation(s)
- Lihua Chen
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Jian Xu
- Department of General Surgery, 904th Hospital, Wuxi, Jiangsu, China
| | - Daihong Liu
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Bing Ji
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Xianchun Zeng
- Department of Radiology, Guizhou Provincial People's Hospital, Guizhou, China.
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China.
| | - 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, NY 10016, USA
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Almansour H, Mustafi M, Lescan M, Grosse U, Andic M, Schmehl J, Artzner C, Grözinger G, Walter SS. Dynamic Radial MR Imaging for Endoleak Surveillance after Endovascular Repair of Abdominal Aortic Aneurysms with Inconclusive CT Angiography: A Prospective Study. J Clin Med 2024; 13:2913. [PMID: 38792455 PMCID: PMC11122363 DOI: 10.3390/jcm13102913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/10/2024] [Accepted: 05/11/2024] [Indexed: 05/26/2024] Open
Abstract
Background/Objectives: To assess free-breathing, dynamic radial magnetic resonance angiography (MRA) for detecting endoleaks post-endovascular aortic repair (EVAR) in cases with inconclusive computed tomography angiography (CTA). Methods: This prospective single-center study included 17 participants (mean age, 70 ± 9 years; 13 males) who underwent dynamic radial MRI (Golden-angle RAdial Sparse Parallel-Volumetric Interpolated BrEath-hold, GRASP-VIBE) after inconclusive multiphasic CT for the presence of endoleaks during the follow-up of EVAR-treated abdominal aortic aneurysms. CT and MRI datasets were independently assessed by two radiologists for image quality, diagnostic confidence, and the presence/type of endoleak. Statistical analyses included interrater and intermethod agreement, and diagnostic performance (sensitivity, specificity, area under the curve (AUC)). Results: Subjective image analysis demonstrated good image quality and interrater agreement (k ≥ 0.6) for both modalities, while diagnostic confidence was significantly higher in MRA (p = 0.03). There was significantly improved accuracy for detecting type II endoleaks on MRA (AUC 0.97 [95% CI: 0.87, 1.0]) compared to CTA (AUC 0.66 [95% CI: 0.41, 0.91]; p = 0.03). Although MRA demonstrated higher values for sensitivity, specificity, AUC, and interrater agreement, none of the other types nor the overall detection rate for endoleaks showed differences in the diagnostic performance over CT (p ≥ 0.12). CTA and MRA revealed slight to moderate intermethod concordance in endoleak detection (k = 0.3-0.64). Conclusions: The GRASP-VIBE MRA characterized by high spatial and temporal resolution demonstrates clinical feasibility with good image quality and superior diagnostic confidence. It notably enhances diagnostic performance in detecting and classifying endoleaks, particularly type II, compared to traditional multiphase CTA with inconclusive findings.
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Affiliation(s)
- Haidara Almansour
- Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, 72076 Tuebingen, Germany; (H.A.); (J.S.); (C.A.); (G.G.); (S.S.W.)
| | - Migdat Mustafi
- Klinik für Thoraxchirurgie-Lungentransplantation und Klinik für Kinderherzchirurgie, Universitätsklinikum des Saarlandes, 66421 Homburg, Germany;
| | - Mario Lescan
- Department of Cardiovascular Surgery, University Hospital Freiburg, 79106 Freiburg, Germany;
| | - Ulrich Grosse
- Department of Radiology, Cantonal Hospital Frauenfeld, Switzerland Pfaffenholzstrasse 4, 8500 Frauenfeld, Switzerland
| | - Mateja Andic
- Department of Thoracic and Cardiovascular Surgery, University Hospital Tübingen, 72076 Tübingen, Germany;
| | - Jörg Schmehl
- Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, 72076 Tuebingen, Germany; (H.A.); (J.S.); (C.A.); (G.G.); (S.S.W.)
| | - Christoph Artzner
- Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, 72076 Tuebingen, Germany; (H.A.); (J.S.); (C.A.); (G.G.); (S.S.W.)
- Diakonie Klinikum Stuttgart, Department for Radiology, 70176 Stuttgart, Germany
| | - Gerd Grözinger
- Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, 72076 Tuebingen, Germany; (H.A.); (J.S.); (C.A.); (G.G.); (S.S.W.)
| | - Sven S. Walter
- Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, 72076 Tuebingen, Germany; (H.A.); (J.S.); (C.A.); (G.G.); (S.S.W.)
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Bae J, Qayyum S, Zhang J, Das A, Reyes I, Aronowitz E, Stavarache MA, Kaplitt MG, Masurkar A, Kim SG. Feasibility of measuring blood-brain barrier permeability using ultra-short echo time radial magnetic resonance imaging. J Neuroimaging 2024; 34:320-328. [PMID: 38616297 PMCID: PMC11090723 DOI: 10.1111/jon.13199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/14/2024] [Accepted: 03/14/2024] [Indexed: 04/16/2024] Open
Abstract
BACKGROUND AND PURPOSE The purpose of this study is to evaluate the feasibility of using 3-dimensional (3D) ultra-short echo time (UTE) radial imaging method for measurement of the permeability of the blood-brain barrier (BBB) to gadolinium-based contrast agent. In this study, we propose to use the golden-angle radial sparse parallel (GRASP) method with 3D center-out trajectories for UTE, hence named as 3D UTE-GRASP. We first examined the feasibility of using 3D UTE-GRASP dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) for differentiating subtle BBB disruptions induced by focused ultrasound (FUS). Then, we examined the BBB permeability changes in Alzheimer's disease (AD) pathology using Alzheimer's disease transgenic mice (5xFAD) at different ages. METHODS For FUS experiments, we used four Sprague Dawley rats at similar ages where we compared BBB permeability of each rat receiving the FUS sonication with different acoustic power (0.4-1.0 MPa). For AD transgenic mice experiments, we included three 5xFAD mice (6, 12, and 16 months old) and three wild-type mice (4, 8, and 12 months old). RESULTS The result from FUS experiments showed a progressive increase in BBB permeability with increase of acoustic power (p < .05), demonstrating the sensitivity of DCE-MRI method for detecting subtle changes in BBB disruption. Our AD transgenic mice experiments suggest an early BBB disruption in 5xFAD mice, which is further impaired with aging. CONCLUSION The results in this study substantiate the feasibility of using the proposed 3D UTE-GRASP method for detecting subtle BBB permeability changes expected in neurodegenerative diseases, such as AD.
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Affiliation(s)
- Jonghyun Bae
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
- Department of Radiology, Weill Cornell Medical College
| | - Sawwal Qayyum
- Department of Radiology, Weill Cornell Medical College
| | - Jin Zhang
- Department of Radiology, Weill Cornell Medical College
| | - Ayesha Das
- Department of Radiology, Weill Cornell Medical College
| | - Isabel Reyes
- Center for Cognitive Neurology, Department of Neurology, New York University School of Medicine
- Department of Neuroscience & Physiology, New York University School of Medicine
- Neuroscience Institute, New York University School of Medicine
| | | | | | | | - Arjun Masurkar
- Center for Cognitive Neurology, Department of Neurology, New York University School of Medicine
- Department of Neuroscience & Physiology, New York University School of Medicine
- Neuroscience Institute, New York University School of Medicine
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Zhao M, Shen D, Fan L, Hong K, Feng L, Benefield BC, Allen BD, Lee DC, Kim D. Incorporation of view sharing and KWIC filtering into GRASP-Pro improves spatial resolution of single-shot, multi-TI, late gadolinium enhancement MRI. NMR IN BIOMEDICINE 2024; 37:e5059. [PMID: 37872862 PMCID: PMC10922561 DOI: 10.1002/nbm.5059] [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: 05/08/2023] [Revised: 08/14/2023] [Accepted: 09/19/2023] [Indexed: 10/25/2023]
Abstract
While single-shot late gadolinium enhancement (LGE) is useful for imaging patients with arrhythmia and/or dyspnea, it produces low spatial resolution. One approach to improve spatial resolution is to accelerate data acquisition using compressed sensing (CS). Our previous work described a single-shot, multi-inversion time (TI) LGE pulse sequence using radial k-space sampling and CS, but over-regularization resulted in significant image blurring that muted the benefits of data acceleration. The purpose of the present study was to improve the spatial resolution of the single-shot, multi-TI LGE pulse sequence by incorporating view sharing (VS) and k-space weighted contrast (KWIC) filtering into a GRASP-Pro reconstruction. In 24 patients (mean age = 61 ± 16 years; 9/15 females/males), we compared the performance of our improved multi-TI LGE and standard multi-TI LGE, where clinical standard LGE was used as a reference. Two clinical raters independently graded multi-TI images and clinical LGE images visually on a five-point Likert scale (1, nondiagnostic; 3, clinically acceptable; 5, best) for three categories: the conspicuity of myocardium or scar, artifact, and noise. The summed visual score (SVS) was defined as the sum of the three scores. Myocardial scar volume was quantified using the full-width at half-maximum method. The SVS was not significantly different between clinical breath-holding LGE (median 13.5, IQR 1.3) and multi-TI LGE (median 12.5, IQR 1.6) (P = 0.068). The myocardial scar volumes measured from clinical standard LGE and multi-TI LGE were strongly correlated (coefficient of determination, R2 = 0.99) and in good agreement (mean difference = 0.11%, lower limit of the agreement = -2.13%, upper limit of the agreement = 2.34%). The inter-rater agreement in myocardial scar volume quantification was strong (intraclass correlation coefficient = 0.79). The incorporation of VS and KWIC into GRASP-Pro improved spatial resolution. Our improved 25-fold accelerated, single-shot LGE sequence produces clinically acceptable image quality, multi-TI reconstruction, and accurate myocardial scar volume quantification.
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Affiliation(s)
- Mingyue Zhao
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
- Department of Biomedical Engineering, Northwestern University, Evanston, IL
| | | | - Lexiaozi Fan
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Kyungpyo Hong
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, New York, NY
| | - Brandon C. Benefield
- Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Bradley D. Allen
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Daniel C. Lee
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
- Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Daniel Kim
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
- Department of Biomedical Engineering, Northwestern University, Evanston, IL
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11
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Feng L. Live-view 4D GRASP MRI: A framework for robust real-time respiratory motion tracking with a sub-second imaging latency. Magn Reson Med 2023; 90:1053-1068. [PMID: 37203314 PMCID: PMC10330383 DOI: 10.1002/mrm.29700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 04/21/2023] [Accepted: 04/21/2023] [Indexed: 05/20/2023]
Abstract
PURPOSE To propose a framework called live-view golden-angle radial sparse parallel (GRASP) MRI for low-latency and high-fidelity real-time volumetric MRI. METHODS Live-view GRASP MRI has two stages. The first one is called an off-view stage and the second one is called a live-view stage. In the off-view stage, 3D k-space data and 2D navigators are acquired alternatively using a new navi-stack-of-stars sampling scheme. A 4D motion database is then generated that contains time-resolved MR images at a sub-second temporal resolution, and each image is linked to a 2D navigator. In the live-view stage, only 2D navigators are acquired. At each time point, a live-view 2D navigator is matched to all the off-view 2D navigators. A 3D image that is linked to the best-matched off-view 2D navigator is then selected for this time point. This framework places the typical acquisition and reconstruction burden of MRI in the off-view stage, enabling low-latency real-time 3D imaging in the live-view stage. The accuracy of live-view GRASP MRI and the robustness of 2D navigators for characterizing respiratory variations and/or body movements were assessed. RESULTS Live-view GRASP MRI can efficiently generate real-time volumetric images that match well with the ground-truth references, with an imaging latency below 500 ms. Compared to 1D navigators, 2D navigators enable more reliable characterization of respiratory variations and/or body movements that may occur throughout the two imaging stages. CONCLUSION Live-view GRASP MRI represents a novel, accurate, and robust framework for real-time volumetric imaging, which can potentially be applied for motion adaptive radiotherapy on MRI-Linac.
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Affiliation(s)
- Li Feng
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, New York, New York, USA
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, New York, USA
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12
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Wang J, Geng W, Wu J, Kang T, Wu Z, Lin J, Yang Y, Cai C, Cai S. Intravoxel incoherent motion magnetic resonance imaging reconstruction from highly under-sampled diffusion-weighted PROPELLER acquisition data via physics-informed residual feedback unrolled network. Phys Med Biol 2023; 68:175022. [PMID: 37541226 DOI: 10.1088/1361-6560/aced77] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 08/04/2023] [Indexed: 08/06/2023]
Abstract
Objective. The acquisition of diffusion-weighted images for intravoxel incoherent motion (IVIM) imaging is time consuming. This work aims to accelerate the scan through a highly under-sampling diffusion-weighted turbo spin echo PROPELLER (DW-TSE-PROPELLER) scheme and to develop a reconstruction method for accurate IVIM parameter mapping from the under-sampled data.Approach.The proposed under-sampling DW-TSE-PROPELLER scheme for IVIM imaging is that a few blades perb-value are acquired and rotated along theb-value dimension to cover high-frequency information. A physics-informed residual feedback unrolled network (PIRFU-Net) is proposed to directly estimate distortion-free and artifact-free IVIM parametric maps (i.e., the perfusion-free diffusion coefficientDand the perfusion fractionf) from highly under-sampled DW-TSE-PROPELLER data. PIRFU-Net used an unrolled convolution network to explore data redundancy in the k-q space to remove under-sampling artifacts. An empirical IVIM physical constraint was incorporated into the network to ensure that the signal evolution curves along theb-value follow a bi-exponential decay. The residual between the realistic and estimated measurements was fed into the network to refine the parametric maps. Meanwhile, the use of synthetic training data eliminated the need for genuine DW-TSE-PROPELLER data.Main results.The experimental results show that the DW-TSE-PROPELLER acquisition was six times faster than full k-space coverage PROPELLER acquisition and within a clinically acceptable time. Compared with the state-of-the-art methods, the distortion-freeDandfmaps estimated by PIRFU-Net were more accurate and had better-preserved tissue boundaries on a simulated human brain and realistic phantom/rat brain/human brain data.Significance.Our proposed method greatly accelerates IVIM imaging. It is capable of directly and simultaneously reconstructing distortion-free, artifact-free, and accurateDandfmaps from six-fold under-sampled DW-TSE-PROPELLER data.
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Affiliation(s)
- Jiechao Wang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, People's Republic of China
| | - Wenhua Geng
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, People's Republic of China
| | - Jian Wu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, People's Republic of China
| | - Taishan Kang
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361004, People's Republic of China
| | - Zhigang Wu
- Clinical & Technical Solutions, Philips Healthcare, Shenzhen, 518000, People's Republic of China
| | - Jianzhong Lin
- Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361004, People's Republic of China
| | - Yu Yang
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, People's Republic of China
| | - Congbo Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, People's Republic of China
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, 361005, People's Republic of China
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13
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Pei H, Xia D, Xu X, Yang Y, Wang Y, Liu F, Feng L. Rapid 3D T 1 mapping using deep learning-assisted Look-Locker inversion recovery MRI. Magn Reson Med 2023; 90:569-582. [PMID: 37125662 PMCID: PMC10225330 DOI: 10.1002/mrm.29672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/01/2023] [Accepted: 03/28/2023] [Indexed: 05/02/2023]
Abstract
PURPOSE Conventional 3D Look-Locker inversion recovery (LLIR) T1 mapping requires multi-repetition data acquisition to reconstruct images at different inversion times for T1 fitting. To ensure B1 robustness, sufficient time of delay (TD) is needed between repetitions, which prolongs scan time. This work proposes a novel deep learning-assisted LLIR MRI approach for rapid 3D T1 mapping without TD. THEORY AND METHODS The proposed approach is based on the fact thatT 1 * $$ {\mathrm{T}}_1^{\ast } $$ , the effective T1 in LLIR imaging, is independent of TD and can be estimated from both LLIR imaging with and without TD, while accurate conversion ofT 1 * $$ {\mathrm{T}}_1^{\ast } $$ to T1 requires TD. Therefore, deep learning can be used to learn the conversion ofT 1 * $$ {\mathrm{T}}_1^{\ast } $$ to T1 , which eliminates the need for TD. This idea was implemented for inversion-recovery-prepared Golden-angel RAdial Sparse Parallel T1 mapping (GraspT1 ). 39 GraspT1 datasets with a TD of 6 s (GraspT1 -TD6) were used for training, which also incorporates additional anatomical images. The trained network was applied for T1 estimation in 14 GraspT1 datasets without TD (GraspT1 -TD0). The robustness of the trained network was also tested. RESULTS Deep learning-based T1 estimation from GraspT1 -TD0 is accurate compared to the reference. Incorporation of additional anatomical images improves the accuracy of T1 estimation. The technique is also robust against slight variation in spatial resolution, imaging orientation and scanner platform. CONCLUSION Our approach eliminates the need for TD in 3D LLIR imaging without affecting the T1 estimation accuracy. It represents a novel use of deep learning towards more efficient and robust 3D LLIR T1 mapping.
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Affiliation(s)
- Haoyang Pei
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, New York, NY, USA
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York, NY, USA
| | - Ding Xia
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xiang Xu
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Yao Wang
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York, NY, USA
| | - Fang Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, MA, USA
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, New York, NY, USA
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Arefeen Y, Xu J, Zhang M, Dong Z, Wang F, White J, Bilgic B, Adalsteinsson E. Latent signal models: Learning compact representations of signal evolution for improved time-resolved, multi-contrast MRI. Magn Reson Med 2023; 90:483-501. [PMID: 37093775 DOI: 10.1002/mrm.29657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/09/2023] [Accepted: 03/16/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE To improve time-resolved reconstructions by training auto-encoders to learn compact representations of Bloch-simulated signal evolution and inserting the decoder into the forward model. METHODS Building on model-based nonlinear and linear subspace techniques, we train auto-encoders on dictionaries of simulated signal evolution to learn compact, nonlinear, latent representations. The proposed latent signal model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation. Latent signal models essentially serve as a proxy for fast and feasible differentiation through the Bloch equations used to simulate signal. This work performs experiments in the context of T2 -shuffling, gradient echo EPTI, and MPRAGE-shuffling. We compare how efficiently auto-encoders represent signal evolution in comparison to linear subspaces. Simulation and in vivo experiments then evaluate if reducing degrees of freedom by incorporating our proxy for the Bloch equations, the decoder portion of the auto-encoder, into the forward model improves reconstructions in comparison to subspace constraints. RESULTS An auto-encoder with 1 real latent variable represents single-tissue fast spin echo, EPTI, and MPRAGE signal evolution to within 0.15% normalized RMS error, enabling reconstruction problems with 3 degrees of freedom per voxel (real latent variable + complex scaling) in comparison to linear models with 4-8 degrees of freedom per voxel. In simulated/in vivo T2 -shuffling and in vivo EPTI experiments, the proposed framework achieves consistent quantitative normalized RMS error improvement over linear approaches. From qualitative evaluation, the proposed approach yields images with reduced blurring and noise amplification in MPRAGE-shuffling experiments. CONCLUSION Directly solving for nonlinear latent representations of signal evolution improves time-resolved MRI reconstructions.
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Affiliation(s)
- Yamin Arefeen
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Junshen Xu
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Molin Zhang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | - Jacob White
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Elfar Adalsteinsson
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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15
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Li Z, Huang C, Tong A, Chandarana H, Feng L. Kz-accelerated variable-density stack-of-stars MRI. Magn Reson Imaging 2023; 97:56-67. [PMID: 36577458 PMCID: PMC10072203 DOI: 10.1016/j.mri.2022.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/17/2022] [Accepted: 12/23/2022] [Indexed: 12/26/2022]
Abstract
This work aimed to develop a modified stack-of-stars golden-angle radial sampling scheme with variable-density acceleration along the slice (kz) dimension (referred to as VD-stack-of-stars) and to test this new sampling trajectory with multi-coil compressed sensing reconstruction for rapid motion-robust 3D liver MRI. VD-stack-of-stars sampling implements additional variable-density undersampling along the kz dimension, so that slice resolution (or volumetric coverage) can be increased without prolonging scan time. The new sampling trajectory (with increased slice resolution) was compared with standard stack-of-stars sampling with fully sampled kz (with standard slice resolution) in both non-contrast-enhanced free-breathing liver MRI and dynamic contrast-enhanced MRI (DCE-MRI) of the liver in volunteers. For both sampling trajectories, respiratory motion was extracted from the acquired radial data, and images were reconstructed using motion-compensated (respiratory-resolved or respiratory-weighted) dynamic radial compressed sensing reconstruction techniques. Qualitative image quality assessment (visual assessment by experienced radiologists) and quantitative analysis (as a metric of image sharpness) were performed to compare images acquired using the new and standard stack-of-stars sampling trajectories. Compared to standard stack-of-stars sampling, both non-contrast-enhanced and DCE liver MR images acquired with VD-stack-of-stars sampling presented improved overall image quality, sharper liver edges and increased hepatic vessel clarity in all image planes. The results have suggested that the proposed VD-stack-of-stars sampling scheme can achieve improved performance (increased slice resolution or volumetric coverage with better image quality) over standard stack-of-stars sampling in free-breathing DCE-MRI without increasing scan time. The reformatted coronal and sagittal images with better slice resolution may provide added clinical value.
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Affiliation(s)
- Zhitao Li
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Chenchan Huang
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Angela Tong
- Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Hersh Chandarana
- Department of Radiology, New York University School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI(2)R), and Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA.
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16
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Jafari R, Do RKG, LaGratta MD, Fung M, Bayram E, Cashen T, Otazo R. GRASPNET: Fast spatiotemporal deep learning reconstruction of golden-angle radial data for free-breathing dynamic contrast-enhanced magnetic resonance imaging. NMR IN BIOMEDICINE 2023; 36:e4861. [PMID: 36305619 PMCID: PMC9898111 DOI: 10.1002/nbm.4861] [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: 03/16/2022] [Revised: 10/23/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
The purpose of the current study was to develop a deep learning technique called Golden-angle RAdial Sparse Parallel Network (GRASPnet) for fast reconstruction of dynamic contrast-enhanced 4D MRI acquired with golden-angle radial k-space trajectories. GRASPnet operates in the image-time space and does not use explicit data consistency to minimize the reconstruction time. Three different network architectures were developed: (1) GRASPnet-2D: 2D convolutional kernels (x,y) and coil and contrast dimensions collapsed into a single combined dimension; (2) GRASPnet-3D: 3D kernels (x,y,t); and (3) GRASPnet-2D + time: two 3D kernels to first exploit spatial correlations (x,y,1) followed by temporal correlations (1,1,t). The networks were trained using iterative GRASP reconstruction as the reference. Free-breathing 3D abdominal imaging with contrast injection was performed on 33 patients with liver lesions using a T1-weighted golden-angle stack-of-stars pulse sequence. Ten datasets were used for testing. The three GRASPnet architectures were compared with iterative GRASP results using quantitative and qualitative analysis, including impressions from two body radiologists. The three GRASPnet techniques reduced the reconstruction time to about 13 s with similar results with respect to iterative GRASP. Among the GRASPnet techniques, GRASPnet-2D + time compared favorably in the quantitative analysis. Spatiotemporal deep learning enables reconstruction of dynamic 4D contrast-enhanced images in a few seconds, which would facilitate translation to clinical practice of compressed sensing methods that are currently limited by long reconstruction times.
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Affiliation(s)
- Ramin Jafari
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | | | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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Chaika M, Afat S, Wessling D, Afat C, Nickel D, Kannengiesser S, Herrmann J, Almansour H, Männlin S, Othman AE, Gassenmaier S. Deep learning-based super-resolution gradient echo imaging of the pancreas: Improvement of image quality and reduction of acquisition time. Diagn Interv Imaging 2023; 104:53-59. [PMID: 35843839 DOI: 10.1016/j.diii.2022.06.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/16/2022] [Accepted: 06/17/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the impact of a deep learning-based super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric interpolated breath-hold examination; VIBE) on the assessment of pancreatic MRI at 1.5 T compared to standard VIBE imaging (VIBESTD). MATERIALS AND METHODS This retrospective single-center study was conducted between April 2021 and October 2021. Fifty patients with a total of 50 detectable pancreatic lesion entities were included in this study. There were 27 men and 23 women, with a mean age of 69 ± 13 (standard deviation [SD]) years (age range: 33-89 years). VIBESTD (precontrast, dynamic, postcontrast) was retrospectively processed with a deep learning-based super-resolution algorithm including a more aggressive partial Fourier setting leading to a simulated acquisition time reduction (VIBESR). Image analysis was performed by two radiologists regarding lesion detectability, noise levels, sharpness and contrast of pancreatic edges, as well as regarding diagnostic confidence using a 5-point Likert-scale with 5 being the best. RESULTS VIBESR was rated better than VIBESTD by both readers regarding lesion detectability (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5], for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5]) for reader 2; both P <0.001), noise levels (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001), sharpness and contrast of pancreatic edges (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001), as well as regarding diagnostic confidence (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001). There were no significant differences between lesion sizes as measured by the two readers on VIBESR and VIBESTD images (P > 0.05). The mean acquisition time for VIBESTD (15 ± 1 [SD] s; range: 11-16 s) was longer than that for VIBESR (13 ± 1 [SD] s; range: 11-14 s) (P < 0.001). CONCLUSION Our results indicate that the newly developed deep learning-based super-resolution algorithm adapted to partial Fourier acquisitions has a positive influence not only on shortening the examination time but also on improvement of image quality in pancreatic MRI.
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Affiliation(s)
- Maryanna Chaika
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Daniel Wessling
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Carmen Afat
- Department of Internal Medicine I, Otfried-Müller-Straße 10, Eberhard Karls University Tuebingen, 72076, Tuebingen, Germany
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052, Erlangen, Germany
| | - Stephan Kannengiesser
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052, Erlangen, Germany
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Simon Männlin
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Ahmed E Othman
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany; Department of Neuroradiology, University Medical Center, 55131, Mainz, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany.
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18
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Feng L. 4D Golden-Angle Radial MRI at Subsecond Temporal Resolution. NMR IN BIOMEDICINE 2023; 36:e4844. [PMID: 36259951 PMCID: PMC9845193 DOI: 10.1002/nbm.4844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/29/2022] [Accepted: 10/13/2022] [Indexed: 05/14/2023]
Abstract
Intraframe motion blurring, as a major challenge in free-breathing dynamic MRI, can be reduced if high temporal resolution can be achieved. To address this challenge, this work proposes a highly accelerated 4D (3D + time) dynamic MRI framework with subsecond temporal resolution that does not require explicit motion compensation. The method combines standard stack-of-stars golden-angle radial sampling and tailored GRASP-Pro (Golden-angle RAdial Sparse Parallel imaging with imProved performance) reconstruction. Specifically, 4D dynamic MRI acquisition is performed continuously without motion gating or sorting. The k-space centers in stack-of-stars radial data are organized to guide estimation of a temporal basis, with which GRASP-Pro reconstruction is employed to enforce joint low-rank subspace and sparsity constraints. This new basis estimation strategy is the new feature proposed for subspace-based reconstruction in this work to achieve high temporal resolution (e.g., subsecond/3D volume). It does not require sequence modification to acquire additional navigation data, it is compatible with commercially available stack-of-stars sequences, and it does not need an intermediate reconstruction step. The proposed 4D dynamic MRI approach was tested in abdominal motion phantom, free-breathing abdominal MRI, and dynamic contrast-enhanced MRI (DCE-MRI). Our results have shown that GRASP-Pro reconstruction with the new basis estimation strategy enables highly-accelerated 4D dynamic imaging at subsecond temporal resolution (with five spokes or less for each dynamic frame per image slice) for both free-breathing non-DCE-MRI and DCE-MRI. In the abdominal phantom, better image quality with lower root mean square error and higher structural similarity index was achieved using GRASP-Pro compared with standard GRASP. With the ability to acquire each 3D image in less than 1 s, intraframe respiratory blurring can be intrinsically reduced for body applications with our approach, which eliminates the need for explicit motion detection and motion compensation.
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Affiliation(s)
- Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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19
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Sun C, Robinson A, Wang Y, Bilchick KC, Kramer CM, Weller D, Salerno M, Epstein FH. A Slice-Low-Rank Plus Sparse (slice-L + S) Reconstruction Method for k-t Undersampled Multiband First-Pass Myocardial Perfusion MRI. Magn Reson Med 2022; 88:1140-1155. [PMID: 35608225 PMCID: PMC9325064 DOI: 10.1002/mrm.29281] [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: 07/27/2021] [Revised: 03/14/2022] [Accepted: 04/11/2022] [Indexed: 11/19/2022]
Abstract
PURPOSE The synergistic use of k-t undersampling and multiband (MB) imaging has the potential to provide extended slice coverage and high spatial resolution for first-pass perfusion MRI. The low-rank plus sparse (L + S) model has shown excellent performance for accelerating single-band (SB) perfusion MRI. METHODS A MB data consistency method employing ESPIRiT maps and through-plane coil information was developed. This data consistency method was combined with the temporal L + S constraint to form the slice-L + S method. Slice-L + S was compared to SB L + S and the sequential operations of split slice-GRAPPA and SB L + S (seq-SG-L + S) using synthetic data formed from multislice SB images. Prospectively k-t undersampled MB data were also acquired and reconstructed using seq-SG-L + S and slice-L + S. RESULTS Using synthetic data with total acceleration rates of 6-12, slice-L + S outperformed SB L + S and seq-SG-L + S (N = 7 subjects) with respect to normalized RMSE and the structural similarity index (P < 0.05 for both). For the specific case with MB factor = 3 and rate 3 undersampling, or for SB imaging with rate 9 undersampling (N = 7 subjects), the normalized RMSE values were 0.037 ± 0.007, 0.042 ± 0.005, and 0.031 ± 0.004; and the structural similarity index values were 0.88 ± 0.03, 0.85 ± 0.03, and 0.89 ± 0.02 for SB L + S, seq-SG-L + S, and slice-L + S, respectively (P < 0.05 for both). For prospectively undersampled MB data, slice-L + S provided better image quality than seq-SG-L + S for rate 6 (N = 7) and rate 9 acceleration (N = 7) as scored by blinded experts. CONCLUSION Slice-L + S outperformed SB-L + S and seq-SG-L + S and provides 9 slice coverage of the left ventricle with a spatial resolution of 1.5 mm × 1.5 mm with good image quality.
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Affiliation(s)
- Changyu Sun
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginia
- Department of Biomedical, Biological and Chemical EngineeringUniversity of MissouriColumbiaMissouri
- Department of RadiologyUniversity of MissouriColumbiaMissouri
| | - Austin Robinson
- Department of MedicineUniversity of Virginia Health SystemCharlottesvilleVirginia
| | - Yu Wang
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginia
| | - Kenneth C. Bilchick
- Department of MedicineUniversity of Virginia Health SystemCharlottesvilleVirginia
| | - Christopher M. Kramer
- Department of MedicineUniversity of Virginia Health SystemCharlottesvilleVirginia
- Department of RadiologyUniversity of Virginia Health SystemCharlottesvilleVirginia
| | - Daniel Weller
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginia
- Department of RadiologyUniversity of Virginia Health SystemCharlottesvilleVirginia
- Department of Electrical and Computer EngineeringUniversity of VirginiaCharlottesvilleVirginia
| | - Michael Salerno
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginia
- Department of MedicineUniversity of Virginia Health SystemCharlottesvilleVirginia
- Department of RadiologyUniversity of Virginia Health SystemCharlottesvilleVirginia
| | - Frederick H. Epstein
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVirginia
- Department of RadiologyUniversity of Virginia Health SystemCharlottesvilleVirginia
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20
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Li Z, Xu X, Yang Y, Feng L. Repeatability and robustness of MP-GRASP T 1 mapping. Magn Reson Med 2022; 87:2271-2286. [PMID: 34971467 PMCID: PMC10061203 DOI: 10.1002/mrm.29131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 12/03/2021] [Accepted: 12/06/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To demonstrate the repeatability of fast 3D T1 mapping using Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI and its robustness to variation of imaging parameters including flip angle and spatial resolution in phantoms and the brain. THEORY AND METHODS Multiple imaging experiments were performed to (1) assess the robustness of MP-GRASP T1 mapping to B1 inhomogeneity using a single tube phantom filled with uniform MnCl2 liquid; (2) compare the repeatability of T1 mapping between MP-GRASP and inversion recovery-based spin-echo (IR-SE; over 12 scans), using a commercial T1MES phantom; (3) evaluate the longitudinal variation of T1 estimation using MP-GRASP with varying imaging parameters, including spatial resolution, flip angle, TR/TE, and acceleration rate, using the T1MES phantom (106 scans performed over a period of 12 months); and (4) evaluate the variation of T1 estimation using MP-GRASP with varying imaging parameters in the brain (24 scans in a single visit). In addition, the accuracy of MP-GRASP T1 mapping was also validated against IR-SE by performing linear correlation and calculating the Lin's concordance correlation coefficient (CCC). RESULTS MP-GRASP demonstrates good robustness to B1 inhomogeneity, with intra-slice variability below 1% in the single tube phantom experiment. The longitudinal variability is good both in the phantom (below 2.5%) and in the brain (below 2%) with varying imaging parameters. The T1 values estimated from MP-GRASP are accurate compared to that from the IR-SE imaging (R2 = 0.997, Lin's CCC = 0.996). CONCLUSION MP-GRASP shows excellent repeatability of T1 estimation over time, and it is also robust to variation of different imaging parameters evaluated in this study.
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Affiliation(s)
- Zhitao Li
- Department of Radiology, Stanford University, Palo Alto, California, United States
| | - Xiang Xu
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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21
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Bae J, Huang Z, Knoll F, Geras K, Sood TP, Feng L, Heacock L, Moy L, Kim SG. Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach. Magn Reson Med 2022; 87:2536-2550. [PMID: 35001423 PMCID: PMC8852816 DOI: 10.1002/mrm.29148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 12/09/2021] [Accepted: 12/16/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI. METHODS A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy. RESULT The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81. CONCLUSION This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.
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Affiliation(s)
- Jonghyun Bae
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
- Department of Radiology, Weill Cornell Medical College
| | - Zhengnan Huang
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Florian Knoll
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Krzysztof Geras
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
- Center for Data Science, New York University
| | - Terlika Pandit Sood
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai
| | - Laura Heacock
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
| | - Linda Moy
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine
- Center for Biomedical Imaging, Radiology, New York University School of Medicine
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine
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22
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Feng L. Golden-Angle Radial MRI: Basics, Advances, and Applications. J Magn Reson Imaging 2022; 56:45-62. [PMID: 35396897 PMCID: PMC9189059 DOI: 10.1002/jmri.28187] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 12/21/2022] Open
Abstract
In recent years, golden‐angle radial sampling has received substantial attention and interest in the magnetic resonance imaging (MRI) community, and it has become a popular sampling trajectory for both research and clinical use. However, although the number of relevant techniques and publications has grown rapidly, there is still a lack of a review paper that provides a comprehensive overview and summary of the basics of golden‐angle rotation, the advantages and challenges/limitations of golden‐angle radial sampling, and recommendations in using different types of golden‐angle radial trajectories for MRI applications. Such a review paper is expected to be helpful both for clinicians who are interested in learning the potential benefits of golden‐angle radial sampling and for MRI physicists who are interested in exploring this research direction. The main purpose of this review paper is thus to present an overview and summary about golden‐angle radial MRI sampling. The review consists of three sections. The first section aims to answer basic questions such as: what is a golden angle; how is the golden angle calculated; why is golden‐angle radial sampling useful, and what are its limitations. The second section aims to review more advanced trajectories of golden‐angle radial sampling, including tiny golden‐angle rotation, stack‐of‐stars golden‐angle radial sampling, and three‐dimensional (3D) kooshball golden‐angle radial sampling. Their respective advantages and limitations and potential solutions to address these limitations are also discussed. Finally, the third section reviews MRI applications that can benefit from golden‐angle radial sampling and provides recommendations to readers who are interested in implementing golden‐angle radial trajectories in their MRI studies.
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Affiliation(s)
- Li Feng
- BioMedical Engineering and Imaging Institute (BMEII) and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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23
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Ding Z, Cheng Z, She H, Liu B, Yin Y, Du YP. Dynamic pulmonary MRI using motion-state weighted motion-compensation (MostMoCo) reconstruction with ultrashort TE: A structural and functional study. Magn Reson Med 2022; 88:224-238. [PMID: 35388914 DOI: 10.1002/mrm.29204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 12/24/2021] [Accepted: 02/01/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE To improve the quality of structural images and the quantification of ventilation in free-breathing dynamic pulmonary MRI. METHODS A 3D radial ultrashort TE (UTE) sequence with superior-inferior navigators was used to acquire pulmonary data during free breathing. All acquired data were binned into different motion states according to the respiratory signal extracted from superior-inferior navigators. Motion-resolved images were reconstructed using eXtra-Dimensional (XD) UTE reconstruction. The initial motion fields were generated by registering images at each motion state to other motion states in motion-resolved images. A motion-state weighted motion-compensation (MostMoCo) reconstruction algorithm was proposed to reconstruct the dynamic UTE images. This technique, termed as MostMoCo-UTE, was compared with XD-UTE and iterative motion-compensation (iMoCo) on a porcine lung and 10 subjects. RESULTS MostMoCo reconstruction provides higher peak SNR (37.0 vs. 35.4 and 34.2) and structural similarity (0.964 vs. 0.931 and 0.947) compared to XD-UTE and iMoCo in the porcine lung experiment. Higher apparent SNR and contrast-to-noise ratio are achieved using MostMoCo in the human experiment. MostMoCo reconstruction better preserves the temporal variations of signal intensity of parenchyma compared to iMoCo, shows reduced random noise and improved sharpness of anatomical structures compared to XD-UTE. In the porcine lung experiment, the quantification of ventilation using MostMoCo images is more accurate than that using XD-UTE and iMoCo images. CONCLUSION The proposed MostMoCo-UTE provides improved quality of structural images and quantification of ventilation for free-breathing pulmonary MRI. It has the potential for the detection of structural and functional disorders of the lung in clinical settings.
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Affiliation(s)
- Zekang Ding
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Huajun She
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Bei Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yongfang Yin
- Department of Radiology, People's Hospital of Jilin Province, Changchun, China
| | - Yiping P Du
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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24
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Feng L, Ma D, Liu F. Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends. NMR IN BIOMEDICINE 2022; 35:e4416. [PMID: 33063400 PMCID: PMC8046845 DOI: 10.1002/nbm.4416] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/25/2020] [Accepted: 09/09/2020] [Indexed: 05/08/2023]
Abstract
Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T1 ), the spin-spin relaxation time (T2 ), and the spin-lattice relaxation in the rotating frame (T1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T1 -, T2 -, or T1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.
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Affiliation(s)
- Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, Massachusetts
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25
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Chen Z, Chen Y, Xie Y, Li D, Christodoulou AG. Data-Consistent non-Cartesian deep subspace learning for efficient dynamic MR image reconstruction. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2022; 2022:10.1109/isbi52829.2022.9761497. [PMID: 35572068 PMCID: PMC9104888 DOI: 10.1109/isbi52829.2022.9761497] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Non-Cartesian sampling with subspace-constrained image reconstruction is a popular approach to dynamic MRI, but slow iterative reconstruction limits its clinical application. Data-consistent (DC) deep learning can accelerate reconstruction with good image quality, but has not been formulated for non-Cartesian subspace imaging. In this study, we propose a DC non-Cartesian deep subspace learning framework for fast, accurate dynamic MR image reconstruction. Four novel DC formulations are developed and evaluated: two gradient decent approaches, a directly solved approach, and a conjugate gradient approach. We applied a U-Net model with and without DC layers to reconstruct T1-weighted images for cardiac MR Multitasking (an advanced multidimensional imaging method), comparing our results to the iteratively reconstructed reference. Experimental results show that the proposed framework significantly improves reconstruction accuracy over the U-Net model without DC, while significantly accelerating the reconstruction over conventional iterative reconstruction.
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Affiliation(s)
- Zihao Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
- Department of Bioengineering, UCLA, Los Angeles, USA
| | - Yuhua Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
- Department of Bioengineering, UCLA, Los Angeles, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
- Department of Bioengineering, UCLA, Los Angeles, USA
| | - Anthony G Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
- Department of Bioengineering, UCLA, Los Angeles, USA
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26
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Bridging cell-scale simulations and radiologic images to explain short-time intratumoral oxygen fluctuations. PLoS Comput Biol 2021; 17:e1009206. [PMID: 34310608 PMCID: PMC8341701 DOI: 10.1371/journal.pcbi.1009206] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 08/05/2021] [Accepted: 06/22/2021] [Indexed: 11/19/2022] Open
Abstract
Radiologic images provide a way to monitor tumor development and its response to therapies in a longitudinal and minimally invasive fashion. However, they operate on a macroscopic scale (average value per voxel) and are not able to capture microscopic scale (cell-level) phenomena. Nevertheless, to examine the causes of frequent fast fluctuations in tissue oxygenation, models simulating individual cells’ behavior are needed. Here, we provide a link between the average data values recorded for radiologic images and the cellular and vascular architecture of the corresponding tissues. Using hybrid agent-based modeling, we generate a set of tissue morphologies capable of reproducing oxygenation levels observed in radiologic images. We then use these in silico tissues to investigate whether oxygen fluctuations can be explained by changes in vascular oxygen supply or by modulations in cellular oxygen absorption. Our studies show that intravascular changes in oxygen supply reproduce the observed fluctuations in tissue oxygenation in all considered regions of interest. However, larger-magnitude fluctuations cannot be recreated by modifications in cellular absorption of oxygen in a biologically feasible manner. Additionally, we develop a procedure to identify plausible tissue morphologies for a given temporal series of average data from radiology images. In future applications, this approach can be used to generate a set of tissues comparable with radiology images and to simulate tumor responses to various anti-cancer treatments at the tissue-scale level. Low levels of oxygen, called hypoxia, are observable in many solid tumors. They are associated with more aggressive malignant cells that are resistant to chemo-, radio-, and immunotherapies. Recently developed imaging techniques provide a way to measure the magnitude of frequent short-term oxygen fluctuations, but they operate on a macro-scale voxel level. To examine the possible causes of rapid oxygen fluctuations at the cell level, we developed a hybrid agent-based mathematical model. We tested two different mechanisms that may be responsible for these cyclic effects on tissue oxygenation: temporal variations in vascular influx of oxygen and modulations in cellular oxygen absorption. Additionally, we developed a procedure to identify plausible tissue morphologies from data collected from radiological images. This can provide a bridge between the micro-scale simulations with individual cells and the longitudinal medical images containing average values. In future applications, this approach can be used to generate a set of tissues compatible with radiology images and to simulate tumor responses to various anticancer treatments at the cell-scale level.
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27
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Feng L, Liu F, Soultanidis G, Liu C, Benkert T, Block KT, Fayad ZA, Yang Y. Magnetization-prepared GRASP MRI for rapid 3D T1 mapping and fat/water-separated T1 mapping. Magn Reson Med 2021; 86:97-114. [PMID: 33580909 PMCID: PMC8197608 DOI: 10.1002/mrm.28679] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/18/2020] [Accepted: 12/22/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE This study aimed to (i) develop Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI using a stack-of-stars trajectory for rapid free-breathing T1 mapping and (ii) extend MP-GRASP to multi-echo acquisition (MP-Dixon-GRASP) for fat/water-separated (water-specific) T1 mapping. METHODS An adiabatic non-selective 180° inversion-recovery pulse was added to a gradient-echo-based golden-angle stack-of-stars sequence for magnetization-prepared 3D single-echo or 3D multi-echo acquisition. In combination with subspace-based GRASP-Pro reconstruction, the sequence allows for standard T1 mapping (MP-GRASP) or fat/water-separated T1 mapping (MP-Dixon-GRASP), respectively. The accuracy of T1 mapping using MP-GRASP was evaluated in a phantom and volunteers (brain and liver) against clinically accepted reference methods. The repeatability of T1 estimation was also assessed in the phantom and volunteers. The performance of MP-Dixon-GRASP for water-specific T1 mapping was evaluated in a fat/water phantom and volunteers (brain and liver). RESULTS ROI-based mean T1 values are correlated between the references and MP-GRASP in the phantom (R2 = 1.0), brain (R2 = 0.96), and liver (R2 = 0.73). MP-GRASP achieved good repeatability of T1 estimation in the phantom (R2 = 1.0), brain (R2 = 0.99), and liver (R2 = 0.82). Water-specific T1 is different from in-phase and out-of-phase composite T1 (composite T1 when fat and water signal are mixed in phase or out of phase) both in the phantom and volunteers. CONCLUSION This work demonstrated the initial performance of MP-GRASP and MP-Dixon-GRASP MRI for rapid 3D T1 mapping and 3D fat/water-separated T1 mapping in the brain (without motion) and in the liver (during free breathing). With fat/water-separated T1 estimation, MP-Dixon-GRASP could be potentially useful for imaging patients with fatty-liver diseases.
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Affiliation(s)
- Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Georgios Soultanidis
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chenyu Liu
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Benkert
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
| | - Kai Tobias Block
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
- Center for Advanced Imaging Innovation and Research (CAIR), New York University School of Medicine, New York, NY, USA
| | - Zahi A. Fayad
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Yoon JH, Lee JM, Yu MH, Hur BY, Grimm R, Sourbron S, Chandarana H, Son Y, Basak S, Lee KB, Yi NJ, Lee KW, Suh KS. Simultaneous evaluation of perfusion and morphology using GRASP MRI in hepatic fibrosis. Eur Radiol 2021; 32:34-45. [PMID: 34120229 DOI: 10.1007/s00330-021-08087-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/11/2021] [Accepted: 05/20/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To determine if golden-angle radial sparse parallel (GRASP) dynamic contrast-enhanced (DCE)-MRI allows simultaneous evaluation of perfusion and morphology in liver fibrosis. METHODS Participants who were scheduled for liver biopsy or resection were enrolled (NCT02480972). Images were reconstructed at 12-s temporal resolution for morphologic assessment and at 3.3-s temporal resolution for quantitative evaluation. The image quality of the morphologic images was assessed on a four-point scale, and the Liver Imaging Reporting and Data System score was recorded for hepatic observations. Comparisons were made between quantitative parameters of DCE-MRI for the different fibrosis stages, and for hepatocellular carcinoma (HCCs) with different LR features. RESULTS DCE-MRI of 64 participants (male = 48) were analyzed. The overall image quality consistently stood at 3.5 ± 0.4 to 3.7 ± 0.4 throughout the exam. Portal blood flow significantly decreased in participants with F2-F3 (n = 18, 175 ± 110 mL/100 mL/min) and F4 (n = 12, 98 ± 47 mL/100 mL/min) compared with those in participants with F0-F1 (n = 34, 283 ± 178 mL/100 mL/min, p < 0.05 for all). In participants with F4, the arterial fraction and extracellular volume were significantly higher than those in participants with F0-F1 and F2-F3 (p < 0.05). Compared with HCCs showing non-LR-M features (n = 16), HCCs with LR-M (n = 5) had a significantly prolonged mean transit time and lower arterial blood flow (p < 0.05). CONCLUSIONS Liver MRI using GRASP obtains both sufficient spatial resolution for confident diagnosis and high temporal resolution for pharmacokinetic modeling. Significant differences were found between the MRI-derived portal blood flow at different hepatic fibrosis stages. KEY POINTS A single MRI examination is able to provide both images with sufficient spatial resolution for anatomic evaluation and those with high temporal resolution for pharmacokinetic modeling. Portal blood flow was significantly lower in clinically significant hepatic fibrosis and mean transit time and extracellular volume increased in cirrhosis, compared with those in no or mild hepatic fibrosis. HCCs with different LR features showed different quantitative parameters of DCE-MRI: longer mean transit time and lower arterial flow were observed in HCCs with LR-M features.
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Affiliation(s)
- Jeong Hee Yoon
- Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Jeong Min Lee
- Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. .,Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. .,Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 03087, Republic of Korea.
| | - Mi Hye Yu
- Radiology, Konkuk University School of Medicine, Seoul, 05080, Republic of Korea
| | - Bo Yun Hur
- Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, Republic of Korea
| | | | - Steven Sourbron
- Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), New York, NY, USA.,Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Yohan Son
- Siemens Healthcare Korea, Seoul, 03737, Republic of Korea
| | - Susmita Basak
- Biomedical Imaging Sciences Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Kyoung-Bun Lee
- Pathology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03087, Republic of Korea
| | - Nam-Joon Yi
- Surgery, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03087, Republic of Korea
| | - Kwang-Woong Lee
- Surgery, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03087, Republic of Korea
| | - Kyung-Suk Suh
- Surgery, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03087, Republic of Korea
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Liu F, Kijowski R, El Fakhri G, Feng L. Magnetic resonance parameter mapping using model-guided self-supervised deep learning. Magn Reson Med 2021; 85:3211-3226. [PMID: 33464652 PMCID: PMC9185837 DOI: 10.1002/mrm.28659] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/15/2020] [Accepted: 12/07/2020] [Indexed: 12/25/2022]
Abstract
PURPOSE To develop a model-guided self-supervised deep learning MRI reconstruction framework called reference-free latent map extraction (RELAX) for rapid quantitative MR parameter mapping. METHODS Two physical models are incorporated for network training in RELAX, including the inherent MR imaging model and a quantitative model that is used to fit parameters in quantitative MRI. By enforcing these physical model constraints, RELAX eliminates the need for full sampled reference data sets that are required in standard supervised learning. Meanwhile, RELAX also enables direct reconstruction of corresponding MR parameter maps from undersampled k-space. Generic sparsity constraints used in conventional iterative reconstruction, such as the total variation constraint, can be additionally included in the RELAX framework to improve reconstruction quality. The performance of RELAX was tested for accelerated T1 and T2 mapping in both simulated and actually acquired MRI data sets and was compared with supervised learning and conventional constrained reconstruction for suppressing noise and/or undersampling-induced artifacts. RESULTS In the simulated data sets, RELAX generated good T1 /T2 maps in the presence of noise and/or undersampling artifacts, comparable to artifact/noise-free ground truth. The inclusion of a spatial total variation constraint helps improve image quality. For the in vivo T1 /T2 mapping data sets, RELAX achieved superior reconstruction quality compared with conventional iterative reconstruction, and similar reconstruction performance to supervised deep learning reconstruction. CONCLUSION This work has demonstrated the initial feasibility of rapid quantitative MR parameter mapping based on self-supervised deep learning. The RELAX framework may also be further extended to other quantitative MRI applications by incorporating corresponding quantitative imaging models.
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Affiliation(s)
- Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Richard Kijowski
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Georges El Fakhri
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
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Mickevicius NJ, Paulson ES. On the use of low-dimensional temporal subspace constraints to reduce reconstruction time and improve image quality of accelerated 4D-MRI. Radiother Oncol 2021; 158:215-223. [PMID: 33412207 DOI: 10.1016/j.radonc.2020.12.032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 12/18/2020] [Accepted: 12/20/2020] [Indexed: 11/29/2022]
Abstract
BACKGROUND AND PURPOSE The purpose of this work is to investigate the use of low-dimensional temporal subspace constraints for 4D-MRI reconstruction from accelerated data in the context of MR-guided online adaptive radiation therapy (MRgOART). MATERIALS AND METHODS Subspace basis functions are derived directly from the accelerated golden angle radial stack-of-stars 4D-MRI data. The reconstruction times, image quality, and motion estimates are investigated as a function of the number of subspace coefficients and compared with a conventional frame-by-frame reconstruction. These experiments were performed in five patients with four 4D-MRI scans per patient on a 1.5T MR-Linac. RESULTS If two or three subspace coefficients are used, the iterative reconstruction time is reduced by 32% and 18%, respectively, compared to conventional parallel imaging with compressed sensing reconstructions. No significant difference was found between motion estimates made with the subspace-constrained reconstructions (p > 0.08). Qualitative improvements in image quality included reduction in apparent noise and reductions in streaking artifacts from the radial k-space coverage. CONCLUSION Incorporating subspace constraints for accelerated 4D-MRI reconstruction reduces noise and residual undersampling artifacts in the images while reducing computation time, making it a strong candidate for use in clinical MRgOART workflows.
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Affiliation(s)
| | - Eric S Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, United States; Department of Radiology, Medical College of Wisconsin, United States; Department of Biophysics, Medical College of Wisconsin, United States
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31
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Curtis AD, Cheng HM. Primer and Historical Review on Rapid Cardiac
CINE MRI. J Magn Reson Imaging 2020; 55:373-388. [DOI: 10.1002/jmri.27436] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 12/14/2022] Open
Affiliation(s)
- Aaron D. Curtis
- The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada
- Ted Rogers Centre for Heart Research, Translational Biology & Engineering Program Toronto Ontario Canada
| | - Hai‐Ling M. Cheng
- The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada
- Ted Rogers Centre for Heart Research, Translational Biology & Engineering Program Toronto Ontario Canada
- Institute of Biomedical Engineering, University of Toronto Toronto Ontario Canada
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32
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Rapid golden-angle diffusion-weighted propeller MRI for simultaneous assessment of ADC and IVIM. Neuroimage 2020; 223:117327. [PMID: 32882379 PMCID: PMC7792631 DOI: 10.1016/j.neuroimage.2020.117327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/22/2020] [Accepted: 08/24/2020] [Indexed: 11/24/2022] Open
Abstract
Purpose: Golden-angle single-shot PROPLLER (GA-SS-PROP) is proposed to accelerate the PROPELLER acquisition for distortion-free diffusion-weighted (DW) imaging. Acceleration is achieved by acquiring one-shot per b-value and several b-values can be acquired along a diffusion direction, where the DW signal follows a bi-exponential decay (i.e. IVIM). Sparse reconstruction is used to reconstruct full resolution DW images. Consequently, apparent diffusion coefficient (ADC) map and IVIM maps (i.e., perfusion fraction (f) and the perfusion-free diffusion coefficient (D)) are obtained simultaneously. The performance of GA-SS-PROP was demonstrated with simulation and human experiments. Methods: A realistic numerical phantom of high-quality diffusion images of the brain was developed. The error of the reconstructed DW images and quantitative maps were compared to the ground truth. The pulse sequence was developed to acquire human brain data. For comparison, fully sampled PROPELLER and conventional single-shot echo planar imaging (SS-EPI) acquisitions were performed. Results: GA-SS-PROP was 5 times faster than conventional PROPELLER acquisition with comparable image quality. The simulation demonstrated that sparse reconstruction is effective in restoring contrast and resolution. The human experiments demonstrated that GA-SS-PROP achieved superior image fidelity compared to SS-EPI for the same acquisition time and same in-plane resolution (1 × 1 mm2). Conclusion: GA-SS-PROP offers fast, high-resolution and distortion-free DW images. The generated quantitative maps (f, D and ADC) can provide valuable information on tissue perfusion and diffusion properties simultaneously, which are desirable in many applications, especially in oncology. As a turbo spin-echo based technique, it can be applied in most challenging regions where SS-EPI is problematic.
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Ong F, Zhu X, Cheng JY, Johnson KM, Larson PEZ, Vasanawala SS, Lustig M. Extreme MRI: Large-scale volumetric dynamic imaging from continuous non-gated acquisitions. Magn Reson Med 2020; 84:1763-1780. [PMID: 32270547 DOI: 10.1002/mrm.28235] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 02/05/2020] [Accepted: 02/06/2020] [Indexed: 12/30/2022]
Abstract
PURPOSE To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast-enhanced (DCE) imaging. THEORY AND METHODS The problem considered here requires recovering 100 gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage. To overcome these challenges, we leverage intrinsic three-dimensional (3D) trajectories, such as 3D radial and 3D cones, with ordering that incoherently cover time and k-space over the entire acquisition. We then propose two innovations: (a) A compressed representation using multiscale low-rank matrix factorization that constrains the reconstruction problem, and reduces its memory footprint. (b) Stochastic optimization to reduce computation, improve memory locality, and minimize communications between threads and processors. We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory. We compare it with "soft-gated" dynamic reconstruction for DCE and respiratory-resolved reconstruction for pulmonary imaging. RESULTS The proposed technique shows transient dynamics that are not seen in gating-based methods. When applied to datasets with irregular, or non-repetitive motions, the proposed method displays sharper image features. CONCLUSIONS We demonstrated a method that can reconstruct massive 3D dynamic image series in the extreme undersampling and extreme computation setting.
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Affiliation(s)
- Frank Ong
- Electrical Engineering, Stanford University, Stanford, CA, USA.,Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Xucheng Zhu
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, USA.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Joseph Y Cheng
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Kevin M Johnson
- Medical Physics, University of Wisconsin, Madison, WI, USA.,Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - Peder E Z Larson
- Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | | | - Michael Lustig
- Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
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Feng L, Tyagi N, Otazo R. MRSIGMA: Magnetic Resonance SIGnature MAtching for real-time volumetric imaging. Magn Reson Med 2020; 84:1280-1292. [PMID: 32086858 DOI: 10.1002/mrm.28200] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 12/13/2019] [Accepted: 01/16/2020] [Indexed: 12/25/2022]
Abstract
PURPOSE To propose a real-time 3D MRI technique called MR SIGnature MAtching (MRSIGMA) for high-resolution volumetric imaging and motion tracking with very low imaging latency. METHODS MRSIGMA consists of two steps: (1) offline learning of a database of possible 3D motion states and corresponding motion signature ranges and (2) online matching of new motion signatures acquired in real time with prelearned motion states. Specifically, the offline learning step (non-real-time) reconstructs motion-resolved 4D images representing different motion states and assigns a unique motion range to each state. The online matching step (real-time) acquires motion signatures only and selects one of the prelearned 3D motion states for each newly acquired signature, which generates 3D images efficiently in real time. The MRSIGMA technique was evaluated on 15 golden-angle stack-of-stars liver data sets, and the performance of respiratory motion tracking with the online-generated real-time 3D MRI was compared with the corresponding 2D projections acquired in real time. RESULTS The total latency of generating each 3D image during online matching was about 300 ms, including acquisition of the motion signature data (~138 ms) and corresponding matching process (~150 ms). Linear correlation assessment suggested excellent correlation (R2 = 0.948) between motion displacement measured from the online-generated real-time 3D images and the 2D real-time projections. CONCLUSION This proof-of-concept study demonstrates the feasibility of MRSIGMA for high-resolution real-time volumetric imaging, which shifts the acquisition and reconstruction burden to an offline learning step and leaves fast online matching for online imaging with very low imaging latency. The MRSIGMA technique can potentially be used for real-time motion tracking in MRI-guided radiation therapy.
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Affiliation(s)
- Li Feng
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Biomedical Engineering and Imaging Institute, Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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35
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Chen L, Zeng X, Ji B, Liu D, Wang J, Zhang J, Feng L. Improving dynamic contrast-enhanced MRI of the lung using motion-weighted sparse reconstruction: Initial experiences in patients. Magn Reson Imaging 2020; 68:36-44. [PMID: 32001328 DOI: 10.1016/j.mri.2020.01.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 01/17/2020] [Accepted: 01/26/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE The purpose of this study was to evaluate the performance of motion-weighted Golden-angle RAdial Sparse Parallel MRI (motion-weighted GRASP) for free-breathing dynamic contrast-enhanced MRI (DCE-MRI) of the lung. METHODS Motion-weighted GRASP incorporates a soft-gating motion compensation algorithm into standard GRASP reconstruction, so that motion-corrupted motion k-space (e.g., k-space acquired in inspiratory phases) contributes less to the final reconstructed images. Lung MR data from 20 patients (mean age = 57.9 ± 13.5) with known pulmonary lesions were retrospectively collected for this study. Each subject underwent a free-breathing DCE-MR scan using a fat-statured T1-weighted stack-of-stars golden-angle radial sequence and a post-contrast breath-hold MR scan using a Cartesian volumetric-interpolated imaging sequence (BH-VIBE). Each radial dataset was reconstructed using GRASP without motion compensation and motion-weighted GRASP. All MR images were visually evaluated by two experienced radiologists blinded to reconstruction and acquisition schemes independently. In addition, the influence of motion-weighted reconstruction on dynamic contrast-enhancement patterns was also investigated. RESULTS For image quality assessment, motion-weighted GRASP received significantly higher visual scores than GRASP (P < 0.05) for overall image quality (3.68 vs. 3.39), lesion conspicuity (3.54 vs. 3.18) and overall artifact level (3.53 vs. 3.15). There was no significant difference (P > 0.05) between the breath-hold BH-VIBE and motion-weighted GRASP images. For assessment of temporal fidelity, motion-weighted GRASP maintained a good agreement with respect to GRASP. CONCLUSION Motion-weighted GRASP achieved better reconstruction performance in free-breathing DCE-MRI of the lung compared to standard GRASP, and it may enable improved assessment of pulmonary lesions.
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Affiliation(s)
- Lihua Chen
- Department of Radiology, PLA 904 Hospital, Wuxi, Jiangsu, China
| | - Xianchun Zeng
- Department of Radiology, Guizhou Provincial People's Hospital, Guizhou, China
| | - Bing Ji
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Daihong Liu
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, China; Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, China
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, China; Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, China.
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
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36
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Wang N, Gaddam S, Wang L, Xie Y, Fan Z, Yang W, Tuli R, Lo S, Hendifar A, Pandol S, Christodoulou AG, Li D. Six-dimensional quantitative DCE MR Multitasking of the entire abdomen: Method and application to pancreatic ductal adenocarcinoma. Magn Reson Med 2020; 84:928-948. [PMID: 31961967 DOI: 10.1002/mrm.28167] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 11/09/2019] [Accepted: 12/18/2019] [Indexed: 02/06/2023]
Abstract
PURPOSE To develop a quantitative DCE MRI technique enabling entire-abdomen coverage, free-breathing acquisition, 1-second temporal resolution, and T1 -based quantification of contrast agent concentration and kinetic modeling for the characterization of pancreatic ductal adenocarcinoma (PDAC). METHODS Segmented FLASH readouts following saturation-recovery preparation with randomized 3D Cartesian undersampling was used for incoherent data acquisition. MR Multitasking was used to reconstruct 6-dimensional images with 3 spatial dimensions, 1 T1 recovery dimension for dynamic T1 quantification, 1 respiratory dimension to resolve respiratory motion, and 1 DCE time dimension to capture the contrast kinetics. Sixteen healthy subjects and 14 patients with pathologically confirmed PDAC were recruited for the in vivo studies, and kinetic parameters vp , Ktrans , ve , and Kep were evaluated for each subject. Intersession repeatability of Multitasking DCE was assessed in 8 repeat healthy subjects. One-way unbalanced analysis of variance was performed between control and patient groups. RESULTS In vivo studies demonstrated that vp , Ktrans , and Kep of PDAC were significantly lower compared with nontumoral regions in the patient group (P = .002, .003, .004, respectively) and normal pancreas in the control group (P = .011, <.001, <.001, respectively), while ve was significantly higher than nontumoral regions (P < .001) and healthy pancreas (P < .001). The kinetic parameters showed good in vivo repeatability (interclass correlation coefficient: vp , 0.95; Ktrans , 0.98; ve , 0.96; Kep , 0.99). CONCLUSION The proposed Multitasking DCE is promising for the quantification of vascular properties of PDAC. Quantitative DCE parameters were repeatable in vivo and showed significant differences between normal pancreas and both tumor and nontumoral regions in patients with PDAC.
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Affiliation(s)
- Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.,Department of Bioengineering, University of California, Los Angeles, California
| | - Srinivas Gaddam
- Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, California
| | - Lixia Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.,Department of Bioengineering, University of California, Los Angeles, California
| | - Wensha Yang
- Department of Clinical Radiation Oncology, University of Southern California, Los Angeles, California
| | - Richard Tuli
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Simon Lo
- Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, California
| | - Andrew Hendifar
- Department of Gastrointestinal Malignancies, Cedars-Sinai Medical Center, Los Angeles, California
| | - Stephen Pandol
- Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, California
| | | | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.,Department of Bioengineering, University of California, Los Angeles, California
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