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Martinez JA, Yu VY, Tringale KR, Otazo R, Cohen O. Phase-sensitive deep reconstruction method for rapid multiparametric MR fingerprinting and quantitative susceptibility mapping in the brain. Magn Reson Imaging 2024; 109:147-157. [PMID: 38513790 PMCID: PMC11042874 DOI: 10.1016/j.mri.2024.03.023] [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: 12/01/2023] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 03/23/2024]
Abstract
INTRODUCTION This study explores the potential of Magnetic Resonance Fingerprinting (MRF) with a novel Phase-Sensitivity Deep Reconstruction Network (PS-DRONE) for simultaneous quantification of T1, T2, Proton Density, B1+, phase and quantitative susceptibility mapping (QSM). METHODS Data were acquired at 3 T in vitro and in vivo using an optimized EPI-based MRF sequence. Phantom experiments were conducted using a standardized phantom for T1 and T2 maps and a custom-made agar-based gadolinium phantom for B1 and QSM maps. In vivo experiments included five healthy volunteers and one patient diagnosed with brain metastasis. PSDRONE maps were compared to reference maps obtained through standard imaging sequences. RESULTS Total scan time was 2 min for 32 slices and a resolution of [1 mm, 1 mm, 4.5 mm]. The reconstruction of T1, T2, Proton Density, B1+ and phase maps were reconstructed within 1 s. In the phantoms, PS-DRONE analysis presented accurate and strongly correlated T1 and T2 maps (r = 0.99) compared to the reference maps. B1 maps from PS-DRONE showed slightly higher values, though still correlated (r = 0.6) with the reference. QSM values showed a small bias but were strongly correlated (r = 0.99) with reference data. In the in vivo analysis, PS-DRONE-derived T1 and T2 values for gray and white matter matched reference values in healthy volunteers. PS-DRONE B1 and QSM maps showed strong correlations with reference values. CONCLUSION The PS-DRONE network enables concurrent acquisition of T1, T2, PD, B1+, phase and QSM maps, within 2 min of acquisition time and 1 s of reconstruction time.
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Affiliation(s)
- Jessica A Martinez
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA.
| | - Victoria Y Yu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Kathryn R Tringale
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
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Hu S, Qiu Z, Adams RJ, Zhao W, Boyacioglu R, Calvetti D, McGivney D, Ma D. Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index. Magn Reson Med 2024. [PMID: 38725131 DOI: 10.1002/mrm.30155] [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: 02/14/2024] [Revised: 03/31/2024] [Accepted: 04/24/2024] [Indexed: 07/07/2024]
Abstract
PURPOSE For effective optimization of MR fingerprinting (MRF) pulse sequences, estimating and minimizing errors from actual scan conditions are crucial. Although virtual-scan simulations offer an approximation to these errors, their computational demands become expensive for high-dimensional MRF frameworks, where interactions between more than two tissue properties are considered. This complexity makes sequence optimization impractical. We introduce a new mathematical model, the systematic error index (SEI), to address the scalability challenges for high-dimensional MRF sequence design. METHODS By eliminating the need to perform dictionary matching, the SEI model approximates quantification errors with low computational costs. The SEI model was validated in comparison with virtual-scan simulations. The SEI model was further applied to optimize three high-dimensional MRF sequences that quantify two to four tissue properties. The optimized scans were examined in simulations and healthy subjects. RESULTS The proposed SEI model closely approximated the virtual-scan simulation outcomes while achieving hundred- to thousand-times acceleration in the computational speed. In both simulation and in vivo experiments, the optimized MRF sequences yield higher measurement accuracy with fewer undersampling artifacts at shorter scan times than the heuristically designed sequences. CONCLUSION We developed an efficient method for estimating real-world errors in MRF scans with high computational efficiency. Our results illustrate that the SEI model could approximate errors both qualitatively and quantitatively. We also proved the practicality of the SEI model of optimizing sequences for high-dimensional MRF frameworks with manageable computational power. The optimized high-dimensional MRF scans exhibited enhanced robustness against undersampling and system imperfections with faster scan times.
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Affiliation(s)
- Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Zhilang Qiu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Richard James Adams
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Rasim Boyacioglu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Daniela Calvetti
- Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, Ohio, USA
| | - Debra McGivney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
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Zhang Y, Ye Z, Xia C, Tan Y, Zhang M, Lv X, Tang J, Li Z. Clinical Applications and Recent Updates of Simultaneous Multi-slice Technique in Accelerated MRI. Acad Radiol 2024; 31:1976-1988. [PMID: 38220568 DOI: 10.1016/j.acra.2023.12.032] [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: 10/29/2023] [Revised: 12/15/2023] [Accepted: 12/19/2023] [Indexed: 01/16/2024]
Abstract
Simultaneous multi-slice (SMS) is a magnetic resonance imaging (MRI) acceleration technique that utilizes multi-band radio-frequency pulses to simultaneously excite and encode multiple slices. Currently, SMS has been widely studied and applied in the MRI examination to reduce acquisition time, which can significantly improve the examination efficiency and patient throughput. Moreover, SMS technique can improve spatial resolution, which is of great value in disease diagnosis, treatment response monitoring, and prognosis prediction. This review will briefly introduce the technical principles of SMS, and summarize its current clinical applications. More importantly, we will discuss the recent technical progress and future research direction of SMS, hoping to highlight the clinical value and scientific potential of this technique.
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Affiliation(s)
- Yiteng Zhang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Zheng Ye
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Yuqi Tan
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Meng Zhang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Xinyang Lv
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Jing Tang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Zhenlin Li
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China.
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4
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Cao X, Liao C, Zhou Z, Zhong Z, Li Z, Dai E, Iyer SS, Hannum AJ, Yurt M, Schauman S, Chen Q, Wang N, Wei J, Yan Y, He H, Skare S, Zhong J, Kerr A, Setsompop K. DTI-MR fingerprinting for rapid high-resolution whole-brain T 1 , T 2 , proton density, ADC, and fractional anisotropy mapping. Magn Reson Med 2024; 91:987-1001. [PMID: 37936313 PMCID: PMC11068310 DOI: 10.1002/mrm.29916] [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: 07/14/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 11/09/2023]
Abstract
PURPOSE This study aims to develop a high-efficiency and high-resolution 3D imaging approach for simultaneous mapping of multiple key tissue parameters for routine brain imaging, including T1 , T2 , proton density (PD), ADC, and fractional anisotropy (FA). The proposed method is intended for pushing routine clinical brain imaging from weighted imaging to quantitative imaging and can also be particularly useful for diffusion-relaxometry studies, which typically suffer from lengthy acquisition time. METHODS To address challenges associated with diffusion weighting, such as shot-to-shot phase variation and low SNR, we integrated several innovative data acquisition and reconstruction techniques. Specifically, we used M1-compensated diffusion gradients, cardiac gating, and navigators to mitigate phase variations caused by cardiac motion. We also introduced a data-driven pre-pulse gradient to cancel out eddy currents induced by diffusion gradients. Additionally, to enhance image quality within a limited acquisition time, we proposed a data-sharing joint reconstruction approach coupled with a corresponding sequence design. RESULTS The phantom and in vivo studies indicated that the T1 and T2 values measured by the proposed method are consistent with a conventional MR fingerprinting sequence and the diffusion results (including diffusivity, ADC, and FA) are consistent with the spin-echo EPI DWI sequence. CONCLUSION The proposed method can achieve whole-brain T1 , T2 , diffusivity, ADC, and FA maps at 1-mm isotropic resolution within 10 min, providing a powerful tool for investigating the microstructural properties of brain tissue, with potential applications in clinical and research settings.
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Affiliation(s)
- Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Zihan Zhou
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Zheng Zhong
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Zhitao Li
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Erpeng Dai
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Siddharth Srinivasan Iyer
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA
| | - Ariel J Hannum
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Mahmut Yurt
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Sophie Schauman
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Quan Chen
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Jintao Wei
- Center for Brain Imaging Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yifan Yan
- School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
- School of Physics, Zhejiang University, Hangzhou, Zhejiang, China
| | - Stefan Skare
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Jianhui Zhong
- Department of Imaging Sciences, University of Rochester, NY, USA
| | - Adam Kerr
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
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Sachdeva R, Armstrong AK, Arnaout R, Grosse-Wortmann L, Han BK, Mertens L, Moore RA, Olivieri LJ, Parthiban A, Powell AJ. Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement. J Am Coll Cardiol 2024; 83:63-81. [PMID: 38171712 PMCID: PMC10947556 DOI: 10.1016/j.jacc.2023.10.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/05/2023] [Accepted: 10/13/2023] [Indexed: 01/05/2024]
Abstract
Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow at well-resourced centers. Studies to evaluate the clinical value and cost-effectiveness of these techniques are needed. For techniques that enhance the value of care for congenital heart disease patients, resources will need to be allocated for education and training to promote widespread implementation.
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Affiliation(s)
- Ritu Sachdeva
- Department of Pediatrics, Division of Pediatric Cardiology, Emory University School of Medicine and Children's Healthcare of Atlanta, Atlanta, Georgia, USA.
| | - Aimee K Armstrong
- The Heart Center, Nationwide Children's Hospital, Department of Pediatrics, Division of Cardiology, Ohio State University, Columbus, Ohio, USA
| | - Rima Arnaout
- Division of Cardiology, Department of Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Lars Grosse-Wortmann
- Division of Cardiology, Department of Pediatrics, Oregon Health and Science University, Portland, Oregon, USA
| | - B Kelly Han
- Division of Cardiology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Luc Mertens
- Division of Cardiology, Department of Pediatrics, University of Toronto and The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Ryan A Moore
- The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Laura J Olivieri
- Division of Cardiology, Department of Pediatrics, Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Anitha Parthiban
- Department of Cardiology, Texas Children's Hospital, Baylor College of Medicine, Houston, Texas, USA
| | - Andrew J Powell
- Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
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Gaur S, Panda A, Fajardo JE, Hamilton J, Jiang Y, Gulani V. Magnetic Resonance Fingerprinting: A Review of Clinical Applications. Invest Radiol 2023; 58:561-577. [PMID: 37026802 PMCID: PMC10330487 DOI: 10.1097/rli.0000000000000975] [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] [Indexed: 04/08/2023]
Abstract
ABSTRACT Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance imaging that allows for efficient simultaneous measurements of multiple tissue properties, which are then used to create accurate and reproducible quantitative maps of these properties. As the technique has gained popularity, the extent of preclinical and clinical applications has vastly increased. The goal of this review is to provide an overview of currently investigated preclinical and clinical applications of MRF, as well as future directions. Topics covered include MRF in neuroimaging, neurovascular, prostate, liver, kidney, breast, abdominal quantitative imaging, cardiac, and musculoskeletal applications.
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Affiliation(s)
- Sonia Gaur
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Ananya Panda
- All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | | | - Jesse Hamilton
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Yun Jiang
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Vikas Gulani
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
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DiLorenzo MP, Grosse-Wortmann L. Myocardial Fibrosis in Congenital Heart Disease and the Role of MRI. Radiol Cardiothorac Imaging 2023; 5:e220255. [PMID: 37404787 PMCID: PMC10316299 DOI: 10.1148/ryct.220255] [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: 10/27/2022] [Revised: 04/10/2023] [Accepted: 04/17/2023] [Indexed: 07/06/2023]
Abstract
Progress in the field of congenital heart surgery over the last century can only be described as revolutionary. Recent improvements in patient outcomes have been achieved through refinements in perioperative care. In the current and future eras, the preservation and restoration of myocardial health, beginning with the monitoring of tissue remodeling, will be central to improving cardiac outcomes. Visualization and quantification of fibrotic myocardial remodeling is one of the greatest assets that cardiac MRI brings to the field of cardiology, and its clinical use within the field of congenital heart disease (CHD) has been an area of particular interest in the last few decades. This review summarizes the physical underpinnings of myocardial tissue characterization in CHD, with an emphasis on T1 parametric mapping and late gadolinium enhancement. It describes methods and suggestions for obtaining images, extracting quantitative and qualitative data, and interpreting the results for children and adults with CHD. The tissue characterization observed in different lesions is used to examine the causes and pathomechanisms of fibrotic remodeling in this population. Similarly, the clinical consequences of elevated imaging biomarkers of fibrosis on patient health and outcomes are explored. Keywords: Pediatrics, MR Imaging, Cardiac, Heart, Congenital, Tissue Characterization, Congenital Heart Disease, Cardiac MRI, Parametric Mapping, Fibrosis, Late Gadolinium Enhancement © RSNA, 2023.
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Tippareddy C, Onyewadume L, Sloan AE, Wang GM, Patil NT, Hu S, Barnholtz-Sloan JS, Boyacıoğlu R, Gulani V, Sunshine J, Griswold M, Ma D, Badve C. Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study. Eur Radiol 2023; 33:836-844. [PMID: 35999374 DOI: 10.1007/s00330-022-09067-w] [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: 03/11/2022] [Revised: 06/16/2022] [Accepted: 07/27/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms. METHODS 3D MRF acquisition was performed on 78 patients with newly diagnosed brain tumors including 33 glioblastomas (grade IV), 6 grade III gliomas, 12 grade II gliomas, and 27 patients with brain metastases. Regions of enhancing tumor, non-enhancing tumor, and peritumoral edema were segmented and radiomics analysis with gray-level co-occurrence matrices and gray-level run-length matrices was performed. Statistical analysis was performed to identify features capable of differentiating tumors based on type, grade, and isocitrate dehydrogenase (IDH1) status. Receiver operating curve analysis was performed and the area under the curve (AUC) was calculated for tumor classification and grading. For gliomas, Kaplan-Meier analysis for overall survival was performed using MRF T1 features from enhancing tumor region. RESULTS Multiple MRF T1 and T2 features from enhancing tumor region were capable of differentiating glioblastomas from brain metastases. Although no differences were identified between grade 2 and grade 3 gliomas, differentiation between grade 2 and grade 4 gliomas as well as between grade 3 and grade 4 gliomas was achieved. MRF radiomics features were also able to differentiate IDH1 mutant from the wild-type gliomas. Radiomics T1 features for enhancing tumor region in gliomas correlated to overall survival (p < 0.05). CONCLUSION Radiomics analysis of 3D MRF maps allows differentiating glioblastomas from metastases and is capable of differentiating glioblastomas from metastases and characterizing gliomas based on grade, IDH1 status, and survival. KEY POINTS • 3D MRF data analysis using radiomics offers novel tissue characterization of brain tumors. • 3D MRF with radiomics offers glioma characterization based on grade, IDH1 status, and overall patient survival.
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Affiliation(s)
- Charit Tippareddy
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Louisa Onyewadume
- Department of Neurosurgery, West Virginia University Health Sciences Center, Morgantown, WV, USA
| | - Andrew E Sloan
- Departments of Neurosurgery and Pathology, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Gi-Ming Wang
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Research and Education Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Nirav T Patil
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Jill S Barnholtz-Sloan
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
- Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Rasim Boyacıoğlu
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Vikas Gulani
- Department of Radiology, Michigan Institute of Imaging Technology and Translation, Michigan Medicine, Ann Arbor, MI, USA
| | - Jeffrey Sunshine
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Mark Griswold
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Dan Ma
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA.
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Yang Q, Ma L, Zhou Z, Bao J, Yang Q, Huang H, Cai S, He H, Chen Z, Zhong J, Cai C. Rapid high-fidelity T 2 * mapping using single-shot overlapping-echo acquisition and deep learning reconstruction. Magn Reson Med 2023; 89:2157-2170. [PMID: 36656132 DOI: 10.1002/mrm.29585] [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: 09/15/2022] [Revised: 12/07/2022] [Accepted: 12/29/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE To develop and evaluate a single-shot quantitative MRI technique called GRE-MOLED (gradient-echo multiple overlapping-echo detachment) for rapid T 2 * $$ {T}_2^{\ast } $$ mapping. METHODS In GRE-MOLED, multiple echoes with different TEs are generated and captured in a single shot of the k-space through MOLED encoding and EPI readout. A deep neural network, trained by synthetic data, was employed for end-to-end parametric mapping from overlapping-echo signals. GRE-MOLED uses pure GRE acquisition with a single echo train to deliver T 2 * $$ {T}_2^{\ast } $$ maps less than 90 ms per slice. The self-registered B0 information modulated in image phase was utilized for distortion-corrected parametric mapping. The proposed method was evaluated in phantoms, healthy volunteers, and task-based FMRI experiments. RESULTS The quantitative results of GRE-MOLED T 2 * $$ {T}_2^{\ast } $$ mapping demonstrated good agreement with those obtained from the multi-echo GRE method (Pearson's correlation coefficient = 0.991 and 0.973 for phantom and in vivo brains, respectively). High intrasubject repeatability (coefficient of variation <1.0%) were also achieved in scan-rescan test. Enabled by deep learning reconstruction, GRE-MOLED showed excellent robustness to geometric distortion, noise, and random subject motion. Compared to the conventional FMRI approach, GRE-MOLED also achieved a higher temporal SNR and BOLD sensitivity in task-based FMRI. CONCLUSION GRE-MOLED is a new real-time technique for T 2 * $$ {T}_2^{\ast } $$ quantification with high efficiency and quality, and it has the potential to be a better quantitative BOLD detection method.
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Affiliation(s)
- Qinqin Yang
- Department of Electronic Science, Xiamen University, Xiamen, Fujian, China
| | - Lingceng Ma
- Department of Electronic Science, Xiamen University, Xiamen, Fujian, China
| | - Zihan Zhou
- The Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianfeng Bao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Qizhi Yang
- Department of Electronic Science, Xiamen University, Xiamen, Fujian, China
| | - Haitao Huang
- Department of Electronic Science, Xiamen University, Xiamen, Fujian, China
| | - Shuhui Cai
- Department of Electronic Science, Xiamen University, Xiamen, Fujian, China
| | - Hongjian He
- The Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhong Chen
- Department of Electronic Science, Xiamen University, Xiamen, Fujian, China
| | - Jianhui Zhong
- The Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
| | - Congbo Cai
- Department of Electronic Science, Xiamen University, Xiamen, Fujian, China
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10
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Liu X, Tang S, Mu C, Qin H, Cu D, Lai YC, Riselli AM, Delos Santos R, Carvajal L, Gebrezgiabhier D, Bok RA, Chen HY, Flavell RR, Gordon JW, Vigneron DB, Kurhanewicz J, Larson PE. Development of specialized magnetic resonance acquisition techniques for human hyperpolarized [ 13 C, 15 N 2 ]urea + [1- 13 C]pyruvate simultaneous perfusion and metabolic imaging. Magn Reson Med 2022; 88:1039-1054. [PMID: 35526263 PMCID: PMC9810116 DOI: 10.1002/mrm.29266] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 01/05/2023]
Abstract
PURPOSE This study aimed to develop and demonstrate the in vivo feasibility of a 3D stack-of-spiral balanced steady-state free precession(3D-bSSFP) urea sequence, interleaved with a metabolite-specific gradient echo (GRE) sequence for pyruvate and metabolic products, for improving the SNR and spatial resolution of the first hyperpolarized 13 C-MRI human study with injection of co-hyperpolarized [1-13 C]pyruvate and [13 C,15 N2 ]urea. METHODS A metabolite-specific bSSFP urea imaging sequence was designed using a urea-specific excitation pulse, optimized TR, and 3D stack-of-spiral readouts. Simulations and phantom studies were performed to validate the spectral response of the sequence. The image quality of urea data acquired by the 3D-bSSFP sequence and the 2D-GRE sequence was evaluated with 2 identical injections of co-hyperpolarized [1-13 C]pyruvate and [13 C,15 N2 ]urea formula in a rat. Subsequently, the feasibility of the acquisition strategy was validated in a prostate cancer patient. RESULTS Simulations and phantom studies demonstrated that 3D-bSSFP sequence achieved urea-only excitation, while minimally perturbing other metabolites (<1%). An animal study demonstrated that compared to GRE, bSSFP sequence provided an ∼2.5-fold improvement in SNR without perturbing urea or pyruvate kinetics, and bSSFP approach with a shorter spiral readout reduced blurring artifacts caused by J-coupling of [13 C,15 N2 ]urea. The human study demonstrated the in vivo feasibility and data quality of the acquisition strategy. CONCLUSION The 3D-bSSFP urea sequence with a stack-of-spiral acquisition demonstrated significantly increased SNR and image quality for [13 C,15 N2 ]urea in co-hyperpolarized [1-13 C]pyruvate and [13 C,15 N2 ]urea imaging studies. This work lays the foundation for future human studies to achieve high-quality and high-SNR metabolism and perfusion images.
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Affiliation(s)
- Xiaoxi Liu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Shuyu Tang
- HeartVista Inc., Los Altos, California, USA
| | - Changhua Mu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Hecong Qin
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Di Cu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Ying-Chieh Lai
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 333, Taiwan
| | - Andrew M. Riselli
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Romelyn Delos Santos
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Lucas Carvajal
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Daniel Gebrezgiabhier
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Robert A. Bok
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Hsin-Yu Chen
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Robert R. Flavell
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Jeremy W. Gordon
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Daniel B. Vigneron
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
- Graduate Program in Bioengineering, University of California, Berkeley and San Francisco, San Francisco, California, USA
| | - John Kurhanewicz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
- Graduate Program in Bioengineering, University of California, Berkeley and San Francisco, San Francisco, California, USA
| | - Peder E.Z. Larson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
- Graduate Program in Bioengineering, University of California, Berkeley and San Francisco, San Francisco, California, USA
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11
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Lu H, Ye H, Zhao B. Improved Balanced Steady-State Free Precession Based MR Fingerprinting with Deep Autoencoders. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3029-3034. [PMID: 36086452 PMCID: PMC9472809 DOI: 10.1109/embc48229.2022.9871003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Magnetic Resonance (MR) Fingerprinting is an emerging transient-state imaging paradigm, which enables the quantization of multiple MR tissue parameters in a single experiment. Balanced steady-state free precession (bSSFP)-based MR Fingerprinting has excellent signal-to-noise characteristics and also allows for acquiring both tissue parameter maps and field inhomogeneity maps. However, field inhomogeneity often results in complex magnetization evolutions in bSSFP-based MR Fingerprinting, which creates significant challenges in image reconstruction. In this paper, we introduce a new method to address the image reconstruction problem. The proposed method incorporates a low-dimensional nonlinear manifold learned from an ensemble of magnetization evolutions using a deep autoencoder. It provides much better representation power than a low-dimensional linear subspace in capturing complex magnetization evolutions. We formulate the image reconstruction problem with this nonlinear model and solve the resulting optimization problem using an algorithm based on variable splitting and the alternating direction method of multipliers. We evaluate the performance of the proposed method using numerical experiments and demonstrate that it significantly outperforms the state-of-art method using a linear subspace model.
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Affiliation(s)
- Hengfa Lu
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX 78712, USA
| | - Huihui Ye
- State of Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Bo Zhao
- Department of Biomedical Engineering and the Oden Institute for Computational Engineering & Sciences, University of Texas at Austin, Austin, TX 78712, USA
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12
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Chen JJ, Uthayakumar B, Hyder F. Mapping oxidative metabolism in the human brain with calibrated fMRI in health and disease. J Cereb Blood Flow Metab 2022; 42:1139-1162. [PMID: 35296177 PMCID: PMC9207484 DOI: 10.1177/0271678x221077338] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Conventional functional MRI (fMRI) with blood-oxygenation level dependent (BOLD) contrast is an important tool for mapping human brain activity non-invasively. Recent interest in quantitative fMRI has renewed the importance of oxidative neuroenergetics as reflected by cerebral metabolic rate of oxygen consumption (CMRO2) to support brain function. Dynamic CMRO2 mapping by calibrated fMRI require multi-modal measurements of BOLD signal along with cerebral blood flow (CBF) and/or volume (CBV). In human subjects this "calibration" is typically performed using a gas mixture containing small amounts of carbon dioxide and/or oxygen-enriched medical air, which are thought to produce changes in CBF (and CBV) and BOLD signal with minimal or no CMRO2 changes. However non-human studies have demonstrated that the "calibration" can also be achieved without gases, revealing good agreement between CMRO2 changes and underlying neuronal activity (e.g., multi-unit activity and local field potential). Given the simpler set-up of gas-free calibrated fMRI, there is evidence of recent clinical applications for this less intrusive direction. This up-to-date review emphasizes technological advances for such translational gas-free calibrated fMRI experiments, also covering historical progression of the calibrated fMRI field that is impacting neurological and neurodegenerative investigations of the human brain.
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Affiliation(s)
- J Jean Chen
- Medical Biophysics, University of Toronto, Toronto, Canada.,Rotman Research Institute, Baycrest, Toronto, Canada
| | - Biranavan Uthayakumar
- Medical Biophysics, University of Toronto, Toronto, Canada.,Sunnybrook Research Institute, Toronto, Canada
| | - Fahmeed Hyder
- Magnetic Resonance Research Center (MRRC), Yale University, New Haven, Connecticut, USA.,Department of Radiology, Yale University, New Haven, Connecticut, USA.,Quantitative Neuroscience with Magnetic Resonance (QNMR) Research Program, Yale University, New Haven, Connecticut, USA.,Department of Biomedical Engineering, Yale University, New Haven, Connecticut, USA
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13
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Yamashiro A, Saito T, Miyati T. Development of a novel task-based functional magnetic resonance imaging phantom based on a bubble-compression approach. Med Phys 2022; 49:3717-3728. [PMID: 35287246 DOI: 10.1002/mp.15599] [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: 07/19/2021] [Revised: 01/25/2022] [Accepted: 03/02/2022] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Phantoms used in previous functional magnetic resonance imaging (fMRI) studies have drawbacks, such as a complicated circuit and equipment use, a single signal-change rate, and T2 * values that do not correspond to those of living human brains. We aimed to develop a phantom for use in task-based fMRI studies (gradient-echo echo-planar imaging; GRE-EPI) with bioequivalent T1 and T2 * values, using an innovative method to control the rate of signal change. METHODS A gel phantom with T1 and T2 * values equivalent to that of the living brain gray matter was fixed in a 150 mm diameter container, with five holes, each of which could hold a 30-mL syringe. The gel phantom contained microscopic air bubbles; this made it possible to control the percent signal change by injector-induced water pressure changes. Using this phantom, we investigated the percent signal change, derived an equation that can approximately reproduce an arbitrary percent signal change, compared different gel phantom samples, investigated the change in relaxation time and bubble size during signal change, and assessed the change in values in each sample over time. RESULTS The relaxation time of the gel phantom was similar to the literature values for gray matter. The percent signal change achieved was approximately 0-13.51% and was dependent on the water pressure change. The derived equation was y = 0.000008x3 - 0.000771x2 + 0.034222x - 0.026054, with y being the percent signal change and x being the pressure in kPa; the reproducibility was high. No significant difference was detected among samples of gray matter gel phantoms (P > 0.05). The change in the rate of signal change with the change in water pressure was due to the change in T2 * value with the change in bubble size. With pressure increasing from 0 to 151.7 kPa, the T2 * value increased from 52 ms to 85 ms. The newly developed gel phantom was stable for 60 days, but its bubble size changed after 21 days. CONCLUSION We developed a novel phantom for use in fMRI, which could reproduce minute signal changes similar to the blood-oxygen-level-dependent effect and with bioequivalent T1 and T2 * values, and used an innovative method to control the percent signal change by compressing the air contained in the phantom for validation of fMRI using GRE-EPI. This phantom reproduced the percent signal change due to changes in T2 * values, which is very similar to scanning a human body. This phantom is expected to be a powerful tool for advancing the study of task-based fMRI. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Akihiro Yamashiro
- Department of Radiology, Nagano Red Cross Hospital, 5-22-1 Wakasato, Nagano-City, Nagano, 380-8582, Japan.,Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatuno, Kanazawa-City, Ishikawa, 920-0942, Japan
| | - Takaaki Saito
- Department of Radiology, Iiyama Red Cross Hospital, 226-1 Iiyama, Iiyama-City, Nagano, 389-2295, Japan
| | - Tosiaki Miyati
- Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatuno, Kanazawa-City, Ishikawa, 920-0942, Japan
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14
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Cao T, Ma S, Wang N, Gharabaghi S, Xie Y, Fan Z, Hogg E, Wu C, Han F, Tagliati M, Haacke EM, Christodoulou AG, Li D. Three-dimensional simultaneous brain mapping of T1, T2, T2∗ and magnetic susceptibility with MR Multitasking. Magn Reson Med 2022; 87:1375-1389. [PMID: 34708438 PMCID: PMC8776611 DOI: 10.1002/mrm.29059] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 09/08/2021] [Accepted: 10/07/2021] [Indexed: 01/24/2023]
Abstract
PURPOSE To develop a new technique that enables simultaneous quantification of whole-brain T1 , T2 , T 2 ∗ , as well as susceptibility and synthesis of six contrast-weighted images in a single 9.1-minute scan. METHODS The technique uses hybrid T2 -prepared inversion-recovery pulse modules and multi-echo gradient-echo readouts to collect k-space data with various T1, T2, and T 2 ∗ weightings. The underlying image is represented as a six-dimensional low-rank tensor consisting of three spatial dimensions and three temporal dimensions corresponding to T1 recovery, T2 decay, and multi-echo behaviors, respectively. Multiparametric maps were fitted from reconstructed image series. The proposed method was validated on phantoms and healthy volunteers, by comparing quantitative measurements against corresponding reference methods. The feasibility of generating six contrast-weighted images was also examined. RESULTS High quality, co-registered T1 , T2 , and T 2 ∗ susceptibility maps were generated that closely resembled the reference maps. Phantom measurements showed substantial consistency (R2 > 0.98) with the reference measurements. Despite the significant differences of T1 (p < .001), T2 (p = .002), and T 2 ∗ (p = 0.008) between our method and the references for in vivo studies, excellent agreement was achieved with all intraclass correlation coefficients greater than 0.75. No significant difference was found for susceptibility (p = .900). The framework is also capable of synthesizing six contrast-weighted images. CONCLUSION The MR Multitasking-based 3D brain mapping of T1 , T2 , T 2 ∗ , and susceptibility agrees well with the reference and is a promising technique for multicontrast and quantitative imaging.
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Affiliation(s)
- Tianle Cao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Sen Ma
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Sara Gharabaghi
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Elliot Hogg
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Chaowei Wu
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Fei Han
- Siemens Medical Solutions USA, Inc., Los Angeles, California, USA
| | - Michele Tagliati
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - E. Mark Haacke
- Magnetic Resonance Innovations, Inc., Bingham Farms, MI, USA
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA
- The MRI Institute for Biomedical Research, Bingham Farms, MI, USA
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Bioengineering, University of California, Los Angeles, California, USA
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15
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Abstract
Magnetic resonance fingerprinting (MRF) is increasingly being used to evaluate brain development and differentiate normal and pathologic tissues in children. MRF can provide reliable and accurate intrinsic tissue properties, such as T1 and T2 relaxation times. MRF is a powerful tool in evaluating brain disease in pediatric population. MRF is a new quantitative MR imaging technique for rapid and simultaneous quantification of multiple tissue properties.
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Affiliation(s)
- Sheng-Che Hung
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 2006 Old Clinic, CB#7510, 101 Manning Dr, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Marsico Hall, suite 1200, Chapel Hill, NC 27599, USA
| | - Yong Chen
- Department of Radiology, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA
| | - Pew-Thian Yap
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 2006 Old Clinic, CB#7510, 101 Manning Dr, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Marsico Hall, suite 1200, Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 2006 Old Clinic, CB#7510, 101 Manning Dr, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Marsico Hall, suite 1200, Chapel Hill, NC 27599, USA.
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16
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Zou L, Liang D, Ye H, Su S, Zhu Y, Liu X, Zheng H, Wang H. Quantitative MR relaxation using MR fingerprinting with fractional-order signal evolution. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2021; 330:107042. [PMID: 34333244 DOI: 10.1016/j.jmr.2021.107042] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 06/19/2021] [Accepted: 07/19/2021] [Indexed: 06/13/2023]
Abstract
The fractional-order Bloch equations have been shown to describe a wider range of experimental situations involving heterogeneous, porous, or composite materials. This paper introduces a novel dictionary of quantitative MR fingerprinting generated by signal evolution model with fractional-order Bloch equations to describe magnetic resonance (MR) relaxation. Here, the fractional-order relaxation models are implemented into Bloch equations through phase transitions using EPG simulation. In the phantom experiments, the fractional-order analysis showed smaller root mean squared error (T1: RMSE = 5.21%, T2: RMSE=3.75%) using the proposed method compared to using conventional method. Among the in vivo experiments of human brains, the estimated T1 and T2 values (mean ± SD) were 843 ± 46.3 ms and 70 ± 4.7 ms in white matter, 1323 ± 28.5 ms and 95 ± 3.8 ms in gray matter. So the proposed method can provide well extensions of current MR fingerprinting and has shown potential to apply into the phantom experiments and the in vivo applications to approach the standard methods for quantitative imaging.
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Affiliation(s)
- Lixian Zou
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Dong Liang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China; Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Huihui Ye
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shi Su
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Xin Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China.
| | - Haifeng Wang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China.
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17
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Khajehim M, Christen T, Tam F, Graham SJ. Streamlined magnetic resonance fingerprinting: Fast whole-brain coverage with deep-learning based parameter estimation. Neuroimage 2021; 238:118237. [PMID: 34091035 DOI: 10.1016/j.neuroimage.2021.118237] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/26/2021] [Accepted: 06/02/2021] [Indexed: 01/02/2023] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a quantitative MRI (qMRI) framework that provides simultaneous estimates of multiple relaxation parameters as well as metrics of field inhomogeneity in a single acquisition. However, current challenges exist in the forms of (1) scan time; (2) need for custom image reconstruction; (3) large dictionary sizes; (4) long dictionary-matching time. This study aims to introduce a novel streamlined magnetic-resonance fingerprinting (sMRF) framework based on a single-shot echo-planar imaging (EPI) sequence to simultaneously estimate tissue T1, T2, and T2* with integrated B1+ correction. Encouraged by recent work on EPI-based MRF, we developed a method that combines spin-echo EPI with gradient-echo EPI to achieve T2 in addition to T1 and T2* quantification. To this design, we add simultaneous multi-slice (SMS) acceleration to enable full-brain coverage in a few minutes. Moreover, in the parameter-estimation step, we use deep learning to train a deep neural network (DNN) to accelerate the estimation process by orders of magnitude. Notably, due to the high image quality of the EPI scans, the training process can rely simply on Bloch-simulated data. The DNN also removes the need for storing large dictionaries. Phantom scans along with in-vivo multi-slice scans from seven healthy volunteers were acquired with resolutions of 1.1×1.1×3 mm3 and 1.7×1.7×3 mm3, and the results were validated against ground truth measurements. Excellent correspondence was found between our T1, T2, and T2* estimates and results obtained from standard approaches. In the phantom scan, a strong linear relationship (R = 1-1.04, R2>0.96) was found for all parameter estimates, with a particularly high agreement for T2 estimation (R2>0.99). Similar findings are reported for the in-vivo human data for all of our parameter estimates. Incorporation of DNN results in a reduction of parameter estimation time on the order of 1000 x and a reduction in storage requirements on the order of 2500 x while achieving highly similar results as conventional dictionary matching (%differences of 7.4 ± 0.4%, 3.6 ± 0.3% and 6.0 ± 0.4% error in T1, T2, and T2* estimation). Thus, sMRF has the potential to be the method of choice for future MRF studies by providing ease of implementation, fast whole-brain coverage, and ultra-fast T1/T2/T2* estimation.
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Affiliation(s)
- Mahdi Khajehim
- Department of Medical Biophysics, University of Toronto, 101 College St Suite 15-701, Toronto, ON M5G 1L7, Canada.
| | - Thomas Christen
- Grenoble Institute of Neuroscience, Inserm, Grenoble, France
| | - Fred Tam
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Simon J Graham
- Department of Medical Biophysics, University of Toronto, 101 College St Suite 15-701, Toronto, ON M5G 1L7, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
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18
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Tippareddy C, Zhao W, Sunshine JL, Griswold M, Ma D, Badve C. Magnetic resonance fingerprinting: an overview. Eur J Nucl Med Mol Imaging 2021; 48:4189-4200. [PMID: 34037831 DOI: 10.1007/s00259-021-05384-2] [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: 12/21/2020] [Accepted: 04/25/2021] [Indexed: 12/17/2022]
Abstract
Magnetic resonance fingerprinting (MRF) is an evolving quantitative MRI framework consisting of unique data acquisition, processing, visualization, and interpretation steps. MRF is capable of simultaneously producing multiple high-resolution property maps including T1, T2, M0, ADC, and T2* measurements. While a relatively new technology, MRF has undergone rapid development for a variety of clinical applications from brain tumor characterization and epilepsy imaging to characterization of prostate cancer, cardiac imaging, among others. This paper will provide a brief overview of current state of MRF technology including highlights of technical and clinical advances. We will conclude with a brief discussion of the challenges that need to be overcome to establish MRF as a quantitative imaging biomarker.
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Affiliation(s)
- Charit Tippareddy
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Walter Zhao
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Jeffrey L Sunshine
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Mark Griswold
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA.,Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA.
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19
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Marriott A, Bowen C, Rioux J, Brewer K. Simultaneous quantification of SPIO and gadolinium contrast agents using MR fingerprinting. Magn Reson Imaging 2021; 79:121-129. [PMID: 33774098 DOI: 10.1016/j.mri.2021.03.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 03/22/2021] [Accepted: 03/22/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE Develop a magnetic resonance fingerprinting (MRF) methodology with R2∗ quantification, intended for use with simultaneous contrast agent concentration mapping, particularly gadolinium (Gd) and iron labelled CD8+ T cells. METHODS Variable-density spiral SSFP MRF was used, modified to allow variable TE, and with an exp.(-TE·R2∗) dictionary modulation. In vitro phantoms containing SPIO labelled cells and/or gadolinium were used to validate parameter maps, probe undersampling capacity, and verify dual quantification capabilities. A C57BL/6 mouse was imaged using MRF to demonstrate acceptable in vivo resolution and signal at 8× undersampling necessary for a 25-min scan. RESULTS Strong agreement was found between conventional and MRF-derived values for R1, R2, and R2∗. Expanded MRF allowed quantification of iron-loaded CD8+ T cells. Results were robust to 8× undersampling and enabled recreation of relaxation profiles for both a Gd agent and iron labelled cells simultaneously. In vivo data demonstrated sufficient SNR in undersampled data for parameter mapping to visualise key features. CONCLUSION MRF can be expanded to include R1, R2, and R2∗ mapping required for simultaneous quantification of gadolinium and SPIO in vitro, allowing for potential implementation of a variety of future in vivo studies using dual MR contrast agents, including molecular imaging of labelled cells.
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Affiliation(s)
- Anna Marriott
- Biomedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada; Dalhousie University, Halifax, NS, Canada
| | - Chris Bowen
- Biomedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada; Dalhousie University, Halifax, NS, Canada
| | - James Rioux
- Biomedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada; Dalhousie University, Halifax, NS, Canada
| | - Kimberly Brewer
- Biomedical Translational Imaging Centre (BIOTIC), Halifax, NS, Canada; Dalhousie University, Halifax, NS, Canada.
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20
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Kiselev VG, Körzdörfer G, Gall P. Toward Quantification: Microstructure and Magnetic Resonance Fingerprinting. Invest Radiol 2021; 56:1-9. [PMID: 33186141 DOI: 10.1097/rli.0000000000000738] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Quantitative magnetic resonance imaging (MRI) is a long-standing challenge. We advocate that the origin of the problem is the simplification applied in commonly used models of the MRI signal relation to the target parameters of biological tissues. Two research fields are briefly reviewed as ways to respond to the challenge of quantitative MRI, both experiencing an exponential growth right now. Microstructure MRI strives to build physiology-based models from cells to signal and, given the signal, back to the cells again. Magnetic resonance fingerprinting aims at efficient simultaneous determination of multiple signal parameters. The synergy of these yet disjoined approaches promises truly quantitative MRI with specific target-oriented diagnostic tools rather than universal imaging methods.
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Affiliation(s)
- Valerij G Kiselev
- From the Medical Physics, Department of Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg
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21
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Boyacioglu R, Wang C, Ma D, McGivney DF, Yu X, Griswold MA. 3D magnetic resonance fingerprinting with quadratic RF phase. Magn Reson Med 2020; 85:2084-2094. [PMID: 33179822 DOI: 10.1002/mrm.28581] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 09/25/2020] [Accepted: 10/12/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE To implement 3D magnetic resonance fingerprinting (MRF) with quadratic RF phase (qRF-MRF) for simultaneous quantification of T1 , T2 , ΔB0 , and T 2 ∗ . METHODS 3D MRF data with effective undersampling factor of 3 in the slice direction were acquired with quadratic RF phase patterns for T1 , T2 , and T 2 ∗ sensitivity. Quadratic RF phase encodes the off-resonance by modulating the on-resonance frequency linearly in time. Transition to 3D brings practical limitations for reconstruction and dictionary matching because of increased data and dictionary sizes. Randomized singular value decomposition (rSVD)-based compression in time and reduction in dictionary size with a quadratic interpolation method are combined to be able to process prohibitively large data sets in feasible reconstruction and matching times. RESULTS Accuracy of 3D qRF-MRF maps in various resolutions and orientations are compared to 3D fast imaging with steady-state precession (FISP) for T1 and T2 contrast and to 2D qRF-MRF for T 2 ∗ contrast and ΔB0 . The precision of 3D qRF-MRF was 1.5-2 times higher than routine clinical scans. 3D qRF-MRF ΔB0 maps were further processed to highlight the susceptibility contrast. CONCLUSION Natively co-registered 3D whole brain T1 , T2 , T 2 ∗ , ΔB0 , and QSM maps can be acquired in as short as 5 min with 3D qRF-MRF.
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Affiliation(s)
- Rasim Boyacioglu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Charlie Wang
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Debra F McGivney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Mark A Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
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22
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van der Heide O, Sbrizzi A, Luijten PR, van den Berg CA. High-resolution in vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm. NMR IN BIOMEDICINE 2020; 33:e4251. [PMID: 31985134 PMCID: PMC7079175 DOI: 10.1002/nbm.4251] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 11/12/2019] [Accepted: 12/05/2019] [Indexed: 05/25/2023]
Abstract
MR-STAT is a recently proposed framework that allows the reconstruction of multiple quantitative parameter maps from a single short scan by performing spatial localisation and parameter estimation on the time-domain data simultaneously, without relying on the fast Fourier transform (FFT). To do this at high resolution, specialized algorithms are required to solve the underlying large-scale nonlinear optimisation problem. We propose a matrix-free and parallelized inexact Gauss-Newton based reconstruction algorithm for this purpose. The proposed algorithm is implemented on a high-performance computing cluster and is demonstrated to be able to generate high-resolution (1 mm × 1 mm in-plane resolution) quantitative parameter maps in simulation, phantom, and in vivo brain experiments. Reconstructed T1 and T2 values for the gel phantoms are in agreement with results from gold standard measurements and, for the in vivo experiments, the quantitative values show good agreement with literature values. In all experiments, short pulse sequences with robust Cartesian sampling are used, for which MR fingerprinting reconstructions are shown to fail.
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Affiliation(s)
- Oscar van der Heide
- Center for Image SciencesUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Alessandro Sbrizzi
- Center for Image SciencesUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Peter R. Luijten
- Center for Image SciencesUniversity Medical Center UtrechtUtrechtthe Netherlands
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23
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McGivney DF, Boyacioğlu R, Jiang Y, Poorman ME, Seiberlich N, Gulani V, Keenan KE, Griswold MA, Ma D. Magnetic resonance fingerprinting review part 2: Technique and directions. J Magn Reson Imaging 2020; 51:993-1007. [PMID: 31347226 PMCID: PMC6980890 DOI: 10.1002/jmri.26877] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/05/2019] [Accepted: 07/05/2019] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR-sensitive tissue properties with a single acquisition. There have been numerous advances in MRF in the years since its inception. In this work we highlight some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:993-1007.
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Affiliation(s)
- Debra F. McGivney
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Rasim Boyacioğlu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yun Jiang
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Megan E. Poorman
- Department of Physics, University of Colorado Boulder, Boulder, Colorado, USA
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
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24
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Guo S, Noll DC. Oscillating steady-state imaging (OSSI): A novel method for functional MRI. Magn Reson Med 2020; 84:698-712. [PMID: 31912574 DOI: 10.1002/mrm.28156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 11/12/2019] [Accepted: 12/10/2019] [Indexed: 11/08/2022]
Abstract
PURPOSE Signal-to-noise ratio (SNR) is crucial for high-resolution fMRI; however, current methods for SNR improvement are limited. A new approach, called oscillating steady-state imaging (OSSI), produces a signal that is large and T 2 ∗ -weighted, and is demonstrated to produce improved SNR compared to gradient echo (GRE) imaging with matched effective TE and spatial-temporal acquisition characteristics for high-resolution fMRI. METHODS Quadratic phase sequences were combined with balanced gradients to produce a large, oscillating steady-state signal. The quadratic phase progression was periodic over short intervals such as 10 TRs, inducing a frequency-dependent phase dispersal. Images over one period were combined to produce a single image with effectively T 2 ∗ -weighting. The OSSI parameters were explored through simulation and phantom data, and 2D and 3D human fMRI data were collected using OSSI and GRE imaging. RESULTS Phantom and human OSSI data showed highly reproducible signal oscillations with greater signal strength than GRE. Compared to single slice GRE with matched effective TE and spatial-temporal resolution, OSSI yielded more activation in the visual cortex by a factor of 1.84 and an improvement in temporal SNR by a factor of 1.83. Voxelwise percentage change comparisons between OSSI and GRE demonstrate a similar T 2 ∗ -weighted contrast mechanism with additional T 2 ' -weighting of about 15 ms immediately after the RF pulse. CONCLUSIONS OSSI is a new acquisition method that exploits a large, oscillating signal that is T 2 ∗ -weighted and suitable for fMRI. The steady-state signal from balanced gradients creates higher signal strength than single slice GRE at varying TEs, enabling greater volumes of functional activity and higher SNR for high-resolution fMRI.
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Affiliation(s)
- Shouchang Guo
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Douglas C Noll
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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25
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Hermann I, Chacon-Caldera J, Brumer I, Rieger B, Weingärtner S, Schad LR, Zöllner FG. Magnetic resonance fingerprinting for simultaneous renal T 1 and T 2 * mapping in a single breath-hold. Magn Reson Med 2020; 83:1940-1948. [PMID: 31900983 DOI: 10.1002/mrm.28160] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 12/11/2019] [Accepted: 12/11/2019] [Indexed: 12/22/2022]
Abstract
PURPOSE To evaluate the use of magnetic resonance fingerprinting (MRF) for simultaneous quantification of T 1 and T 2 ∗ in a single breath-hold in the kidneys. METHODS The proposed kidney MRF sequence was based on MRF echo-planar imaging. Thirty-five measurements per slice and overall 4 slices were measured in 15.4 seconds. Group matching was performed for in-line quantification of T 1 and T 2 ∗ . Images were acquired in a phantom and 8 healthy volunteers in coronal orientation. To evaluate our approach, region of interests were drawn in the kidneys to calculate mean values and standard deviations of the T 1 and T 2 ∗ times. Precision was calculated across multiple repeated MRF scans. Gaussian filtering is applied on baseline images to improve SNR and match stability. RESULTS T 1 and T 2 ∗ times acquired with MRF in the phantom showed good agreement with reference measurements and conventional mapping methods with deviations of less than 5% for T 1 and less than 10% for T 2 ∗ . Baseline images in vivo were free of artifacts and relaxation times yielded good agreement with conventional methods and literature (deviation T 1 : 7 ± 4 % , T 2 ∗ : 6 ± 3 % ). CONCLUSIONS In this feasibility study, the proposed renal MRF sequence resulted in accurate T 1 and T 2 ∗ quantification in a single breath-hold.
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Affiliation(s)
- Ingo Hermann
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Magnetic Resonance Systems Lab, Department of Imaging Physics, Delft University of Technology, Delft, Netherlands
| | - Jorge Chacon-Caldera
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Irène Brumer
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Benedikt Rieger
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sebastian Weingärtner
- Magnetic Resonance Systems Lab, Department of Imaging Physics, Delft University of Technology, Delft, Netherlands
| | - Lothar R Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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26
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Hamilton JI, Seiberlich N. Machine Learning for Rapid Magnetic Resonance Fingerprinting Tissue Property Quantification. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:69-85. [PMID: 33132408 PMCID: PMC7595247 DOI: 10.1109/jproc.2019.2936998] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Magnetic Resonance Fingerprinting (MRF) is an MRI-based method that can provide quantitative maps of multiple tissue properties simultaneously from a single rapid acquisition. Tissue property maps are generated by matching the complex signal evolutions collected at the scanner to a dictionary of signals derived using Bloch equation simulations. However, in some circumstances, the process of dictionary generation and signal matching can be time-consuming, reducing the utility of this technique. Recently, several groups have proposed using machine learning to accelerate the extraction of quantitative maps from MRF data. This article will provide an overview of current research that combines MRF and machine learning, as well as present original research demonstrating how machine learning can speed up dictionary generation for cardiac MRF.
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Affiliation(s)
- Jesse I Hamilton
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106 USA, and the Department of Radiology, University of Michigan, Ann Arbor, MI 48109
| | - Nicole Seiberlich
- Department of Biomedical Engineering and the Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106 USA, the Department of Radiology and Cardiology, University Hospitals, Cleveland, OH 44106 USA, and the Department of Radiology, University of Michigan, Ann Arbor, MI 48109
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27
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Chen Y, Fang Z, Hung SC, Chang WT, Shen D, Lin W. High-resolution 3D MR Fingerprinting using parallel imaging and deep learning. Neuroimage 2019; 206:116329. [PMID: 31689536 DOI: 10.1016/j.neuroimage.2019.116329] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/10/2019] [Accepted: 10/30/2019] [Indexed: 12/16/2022] Open
Abstract
MR Fingerprinting (MRF) is a relatively new imaging framework capable of providing accurate and simultaneous quantification of multiple tissue properties for improved tissue characterization and disease diagnosis. While 2D MRF has been widely available, extending the method to 3D MRF has been an actively pursued area of research as a 3D approach can provide a higher spatial resolution and better tissue characterization with an inherently higher signal-to-noise ratio. However, 3D MRF with a high spatial resolution requires lengthy acquisition times, especially for a large volume, making it impractical for most clinical applications. In this study, a high-resolution 3D MR Fingerprinting technique, combining parallel imaging and deep learning, was developed for rapid and simultaneous quantification of T1 and T2 relaxation times. Parallel imaging was first applied along the partition-encoding direction to reduce the amount of acquired data. An advanced convolutional neural network was then integrated with the MRF framework to extract features from the MRF signal evolution for improved tissue characterization and accelerated mapping. A modified 3D-MRF sequence was also developed in the study to acquire data to train the deep learning model that can be directly applied to prospectively accelerate 3D MRF scans. Our results of quantitative T1 and T2 maps demonstrate that improved tissue characterization can be achieved using the proposed method as compared to prior methods. With the integration of parallel imaging and deep learning techniques, whole-brain (26 × 26 × 18 cm3) quantitative T1 and T2 mapping with 1-mm isotropic resolution were achieved in ~7 min. In addition, a ~7-fold improvement in processing time to extract tissue properties was also accomplished with the deep learning approach as compared to the standard template matching method. All of these improvements make high-resolution whole-brain quantitative MR imaging feasible for clinical applications.
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Affiliation(s)
- Yong Chen
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA
| | - Zhenghan Fang
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA
| | - Sheng-Che Hung
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA
| | - Wei-Tang Chang
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA
| | - Dinggang Shen
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
| | - Weili Lin
- Departments of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, North Carolina, USA.
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28
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Fang Z, Chen Y, Liu M, Xiang L, Zhang Q, Wang Q, Lin W, Shen D. Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2364-2374. [PMID: 30762540 PMCID: PMC6692257 DOI: 10.1109/tmi.2019.2899328] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of human body. Although MRF has demonstrated improved scan efficiency as compared to conventional techniques, further acceleration is still desired for translation into routine clinical practice. The purpose of this paper is to accelerate MRF acquisition by developing a new tissue quantification method for MRF that allows accurate quantification with fewer sampling data. Most of the existing approaches use the MRF signal evolution at each individual pixel to estimate tissue properties, without considering the spatial association among neighboring pixels. In this paper, we propose a spatially constrained quantification method that uses the signals at multiple neighboring pixels to better estimate tissue properties at the central pixel. Specifically, we design a unique two-step deep learning model that learns the mapping from the observed signals to the desired properties for tissue quantification, i.e.: 1) with a feature extraction module for reducing the dimension of signals by extracting a low-dimensional feature vector from the high-dimensional signal evolution and 2) a spatially constrained quantification module for exploiting the spatial information from the extracted feature maps to generate the final tissue property map. A corresponding two-step training strategy is developed for network training. The proposed method is tested on highly undersampled MRF data acquired from human brains. Experimental results demonstrate that our method can achieve accurate quantification for T1 and T2 relaxation times by using only 1/4 time points of the original sequence (i.e., four times of acceleration for MRF acquisition).
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29
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Lee PK, Watkins LE, Anderson TI, Buonincontri G, Hargreaves BA. Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations. Magn Reson Med 2019; 82:1438-1451. [PMID: 31131500 PMCID: PMC8057531 DOI: 10.1002/mrm.27832] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 05/06/2019] [Accepted: 05/08/2019] [Indexed: 12/21/2022]
Abstract
PURPOSE To investigate a computationally efficient method for optimizing the Cramér-Rao Lower Bound (CRLB) of quantitative sequences without using approximations or an analytical expression of the signal. METHODS Automatic differentiation was applied to Bloch simulations and used to optimize several quantitative sequences without the need for approximations or an analytical expression. The results were validated with in vivo measurements and comparisons to prior art. Multi-echo spin echo and DESPO T 1 were used as benchmarks to verify the CRLB implementation. The CRLB of the Magnetic Resonance Fingerprinting (MRF) sequence, which has a complicated analytical formulation, was also optimized using automatic differentiation. RESULTS The sequence parameters obtained for multi-echo spin echo and DESPO T 1 matched results obtained using conventional methods. In vivo, MRF scans demonstrate that the CRLB optimization obtained with automatic differentiation can improve performance in presence of white noise. For MRF, the CRLB optimization converges in 1.1 CPU hours for N TR = 400 and has O ( N TR ) asymptotic runtime scaling for the calculation of the CRLB objective and gradient. CONCLUSIONS Automatic differentiation can be used to optimize the CRLB of quantitative sequences without using approximations or analytical expressions. For MRF, the runtime is computationally efficient and can be used to investigate confounding factors as well as MRF sequences with a greater number of repetitions.
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Affiliation(s)
- Philip K. Lee
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Lauren E. Watkins
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | | | - Guido Buonincontri
- IRCCS Fondazione Stella Maris, Pisa, PI, 56128, Italy
- Fondazione Imago7, Pisa, PI, 56128, Italy
| | - Brian A. Hargreaves
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
- Bioengineering, Stanford University, Stanford, CA, 94305, USA
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30
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Liu Y, Hamilton J, Rajagopalan S, Seiberlich N. Cardiac Magnetic Resonance Fingerprinting: Technical Overview and Initial Results. JACC Cardiovasc Imaging 2018; 11:1837-1853. [PMID: 30522686 PMCID: PMC6394856 DOI: 10.1016/j.jcmg.2018.08.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 08/29/2018] [Accepted: 08/30/2018] [Indexed: 01/03/2023]
Abstract
Cardiovascular magnetic resonance is a versatile tool that enables noninvasive characterization of cardiac tissue structure and function. Parametric mapping techniques have allowed unparalleled differentiation of pathophysiological differences in the myocardium such as the delineation of myocardial fibrosis, hemorrhage, and edema. These methods are increasingly used as part of a tool kit to characterize disease states such as cardiomyopathies and coronary artery disease more accurately. Currently conventional mapping techniques require separate acquisitions for T1 and T2 mapping, the values of which may depend on specifics of the magnetic resonance imaging system hardware, pulse sequence implementation, and physiological variables including blood pressure and heart rate. The cardiac magnetic resonance fingerprinting (cMRF) technique has recently been introduced for simultaneous and reproducible measurement of T1 and T2 maps in a single scan. The potential for this technique to provide consistent tissue property values independent of variables including scanner, pulse sequence, and physiology could allow an unbiased framework for the assessment of intrinsic properties of cardiac tissue including structure, perfusion, and parameters such as extracellular volume without the administration of exogenous contrast agents. This review seeks to introduce the basics of the cMRF technique, including pulse sequence design, dictionary generation, and pattern matching. The potential applications of cMRF in assessing diseases such as nonischemic cardiomyopathy are also briefly discussed, and ongoing areas of research are described.
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Affiliation(s)
- Yuchi Liu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jesse Hamilton
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Sanjay Rajagopalan
- Department of Cardiovascular Medicine, University Hospitals, Harrington Heart and Vascular Institute, Cleveland Medical Center and Case Western Reserve School of Medicine, Cleveland, Ohio
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio; Department of Cardiovascular Medicine, University Hospitals, Harrington Heart and Vascular Institute, Cleveland Medical Center and Case Western Reserve School of Medicine, Cleveland, Ohio; Department of Radiology, Case Western Reserve University, Cleveland, Ohio.
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