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Murali S, Ding H, Adedeji F, Qin C, Obungoloch J, Asllani I, Anazodo U, Ntusi NAB, Mammen R, Niendorf T, Adeleke S. Bringing MRI to low- and middle-income countries: Directions, challenges and potential solutions. NMR IN BIOMEDICINE 2024; 37:e4992. [PMID: 37401341 DOI: 10.1002/nbm.4992] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 05/27/2023] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
The global disparity of magnetic resonance imaging (MRI) is a major challenge, with many low- and middle-income countries (LMICs) experiencing limited access to MRI. The reasons for limited access are technological, economic and social. With the advancement of MRI technology, we explore why these challenges still prevail, highlighting the importance of MRI as the epidemiology of disease changes in LMICs. In this paper, we establish a framework to develop MRI with these challenges in mind and discuss the different aspects of MRI development, including maximising image quality using cost-effective components, integrating local technology and infrastructure and implementing sustainable practices. We also highlight the current solutions-including teleradiology, artificial intelligence and doctor and patient education strategies-and how these might be further improved to achieve greater access to MRI.
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Affiliation(s)
- Sanjana Murali
- School of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | - Hao Ding
- School of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | - Fope Adedeji
- School of Medicine, Faculty of Medicine, University College London, London, UK
| | - Cathy Qin
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Johnes Obungoloch
- Department of Biomedical Engineering, Mbarara University of Science and Technology, Mbarara, Uganda
| | - Iris Asllani
- Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, New York, USA
| | - Udunna Anazodo
- Department of Medical Biophysics, Western University, London, Ontario, Canada
- The Research Institute of London Health Sciences Centre and St. Joseph's Health Care, London, Ontario, Canada
| | - Ntobeko A B Ntusi
- Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- South African Medical Research Council Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, Cape Town, South Africa
| | - Regina Mammen
- Department of Cardiology, The Essex Cardiothoracic Centre, Basildon, UK
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (BUFF), Max-Delbrück Centre for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Sola Adeleke
- School of Cancer & Pharmaceutical Sciences, King's College London, London, UK
- High Dimensional Neuro-oncology, University College London Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
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Chekhonin IV, Cohen O, Otazo R, Young RJ, Holodny AI, Pronin IN. Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review. Neuroradiol J 2024; 37:267-275. [PMID: 37133228 PMCID: PMC11138331 DOI: 10.1177/19714009231173100] [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: 05/04/2023] Open
Abstract
Magnetic resonance (MR) relaxometry is a quantitative imaging method that measures tissue relaxation properties. This review discusses the state of the art of clinical proton MR relaxometry for glial brain tumors. Current MR relaxometry technology also includes MR fingerprinting and synthetic MRI, which solve the inefficiencies and challenges of earlier techniques. Despite mixed results regarding its capability for brain tumor differential diagnosis, there is growing evidence that MR relaxometry can differentiate between gliomas and metastases and between glioma grades. Studies of the peritumoral zones have demonstrated their heterogeneity and possible directions of tumor infiltration. In addition, relaxometry offers T2* mapping that can define areas of tissue hypoxia not discriminated by perfusion assessment. Studies of tumor therapy response have demonstrated an association between survival and progression terms and dynamics of native and contrast-enhanced tumor relaxometric profiles. In conclusion, MR relaxometry is a promising technique for glial tumor diagnosis, particularly in association with neuropathological studies and other imaging techniques.
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Affiliation(s)
- Ivan V Chekhonin
- Federal State Autonomous Institution N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
- Federal State Budgetary Institution V.P. Serbsky National Medical Research Centre for Psychiatry and Narcology of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
- Department of Neuroscience, Weill Cornell Graduate School of the Medical Sciences, New York, NY, USA
| | - Igor N Pronin
- Federal State Autonomous Institution N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, Moscow, Russian Federation
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Monga A, Singh D, de Moura HL, Zhang X, Zibetti MVW, Regatte RR. Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review. Bioengineering (Basel) 2024; 11:236. [PMID: 38534511 DOI: 10.3390/bioengineering11030236] [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: 01/19/2024] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/28/2024] Open
Abstract
Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.
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Affiliation(s)
- Anmol Monga
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Dilbag Singh
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Hector L de Moura
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Xiaoxia Zhang
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Marcelo V W Zibetti
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Ravinder R Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
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Wong YL, Li T, Liu C, Lee HFV, Cheung LYA, Hui ESK, Cao P, Cai J. Reconstruction of multi-phase parametric maps in 4D-magnetic resonance fingerprinting (4D-MRF) by optimization of local T1 and T2 sensitivities. Med Phys 2024. [PMID: 38386904 DOI: 10.1002/mp.17001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND Time-resolved magnetic resonance fingerprinting (MRF), or 4D-MRF, has been demonstrated its feasibility in motion management in radiotherapy (RT). However, the prohibitive long acquisition time is one of challenges of the clinical implementation of 4D-MRF. The shortening of acquisition time causes data insufficiency in each respiratory phase, leading to poor accuracies and consistencies of the predicted tissues' properties of each phase. PURPOSE To develop a technique for the reconstruction of multi-phase parametric maps in four-dimensional magnetic resonance fingerprinting (4D-MRF) through the optimization of local T1 and T2 sensitivities. METHODS The proposed technique employed an iterative optimization to tailor the data arrangement of each phase by manipulation of inter-phase frames, such that the T1 and T2 sensitivities, which were quantified by the modified Minkowski distance, of the truncated signal evolution curve was maximized. The multi-phase signal evolution curves were modified by sliding window reconstruction and inter-phase frame sharing (SWIFS). Motion correction (MC) and dot product matching were sequentially performed on the modified signal evolution and dictionary to reconstruct the multi-parametric maps. The proposed technique was evaluated by numerical simulations using the extended cardiac-torso (XCAT) phantom with regular and irregular breathing patterns, and by in vivo MRF data of three health volunteers and six liver cancer patients acquired at a 3.0 T scanner. RESULTS In simulation study, the proposed SWIFS approach achieved the overall mean absolute percentage error (MAPE) of 8.62% ± 1.59% and 16.2% ± 3.88% for the eight-phases T1 and T2 maps, respectively, in the sagittal view with irregular breathing patterns. In contrast, the overall MAPE of T1 and T2 maps generated by the conventional approach with multiple MRF repetitions were 22.1% ± 11.0% and 30.8% ± 14.9%, respectively. For in-vivo study, the predicted mean T1 and T2 of liver by the proposed SWIFS approach were 795 ms ± 38.9 ms and 58.3 ms ± 11.7 ms, respectively. CONCLUSIONS Both simulation and in vivo results showed that the approach empowered by T1 and T2 sensitivities optimization and sliding window under the shortened acquisition of MRF had superior performance in the estimation of multi-phase T1 and T2 maps as compared to the conventional approach with oversampling of MRF data.
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Affiliation(s)
- Yat Lam Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China
| | - Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Chenyang Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ho-Fun Victor Lee
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China
| | - Lai-Yin Andy Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Clinical Oncology, Oncology Center, St. Paul's Hospital, Hong Kong, China
| | - Edward Sai Kam Hui
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Cabini RF, Barzaghi L, Cicolari D, Arosio P, Carrazza S, Figini S, Filibian M, Gazzano A, Krause R, Mariani M, Peviani M, Pichiecchio A, Pizzagalli DU, Lascialfari A. Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting. NMR IN BIOMEDICINE 2024; 37:e5028. [PMID: 37669779 DOI: 10.1002/nbm.5028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/05/2023] [Accepted: 07/27/2023] [Indexed: 09/07/2023]
Abstract
We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T1 and T2 maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex vivo rat brain phantoms using a 7-T preclinical scanner. Subsequently, the DL model was trained using experimental data, completely excluding the use of any theoretical MRI signal simulator. The best combination of the DL parameters was implemented by an automatic hyperparameter optimization strategy, whose key aspect is to include all the parameters to the fit, allowing the simultaneous optimization of the neural network architecture, the structure of the DL model, and the supervised learning algorithm. By comparing the reconstruction performances of the DL technique with those achieved from the traditional dictionary-based method on an independent dataset, the DL approach was shown to reduce the mean percentage relative error by a factor of 3 for T1 and by a factor of 2 for T2 , and to improve the computational time by at least a factor of 37. Furthermore, the proposed DL method enables maintaining comparable reconstruction performance, even with a lower number of MRF images and a reduced k-space sampling percentage, with respect to the dictionary-based method. Our results suggest that the proposed DL methodology may offer an improvement in reconstruction accuracy, as well as speeding up MRF for preclinical, and in prospective clinical, investigations.
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Affiliation(s)
- Raffaella Fiamma Cabini
- Department of Mathematics, University of Pavia, Pavia, Italy
- INFN, Istituto Nazionale di Fisica Nucleare, Pavia, Italy
| | - Leonardo Barzaghi
- Department of Mathematics, University of Pavia, Pavia, Italy
- INFN, Istituto Nazionale di Fisica Nucleare, Pavia, Italy
- Advanced Imaging and Artificial Intelligence, Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy
| | - Davide Cicolari
- Department of Physics, University of Pavia, Pavia, Italy
- Department of Physics, University of Milan, Milan, Italy
- INFN, Istituto Nazionale di Fisica Nucleare, Milan, Italy
- Department of Medical Physics, ASST GOM Niguarda, Milan, Italy
| | - Paolo Arosio
- Department of Physics, University of Milan, Milan, Italy
- INFN, Istituto Nazionale di Fisica Nucleare, Milan, Italy
| | - Stefano Carrazza
- Department of Physics, University of Milan, Milan, Italy
- INFN, Istituto Nazionale di Fisica Nucleare, Milan, Italy
| | - Silvia Figini
- INFN, Istituto Nazionale di Fisica Nucleare, Pavia, Italy
- Department of Social and Political Science, University of Pavia, Pavia, Italy
| | - Marta Filibian
- INFN, Istituto Nazionale di Fisica Nucleare, Pavia, Italy
- Centro Grandi Strumenti, University of Pavia, Pavia, Italy
| | - Andrea Gazzano
- Laboratory of Cellular and Molecular Neuropharmacology, Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia, Italy
| | | | - Manuel Mariani
- Department of Physics, University of Pavia, Pavia, Italy
| | - Marco Peviani
- Laboratory of Cellular and Molecular Neuropharmacology, Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia, Italy
| | - Anna Pichiecchio
- Advanced Imaging and Artificial Intelligence, Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | | | - Alessandro Lascialfari
- INFN, Istituto Nazionale di Fisica Nucleare, Pavia, Italy
- Department of Physics, University of Pavia, Pavia, Italy
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Lyu Z, Hua S, Xu J, Shen Y, Guo R, Hu P, Qi H. Free-breathing simultaneous native myocardial T1, T2 and T1ρ mapping with Cartesian acquisition and dictionary matching. J Cardiovasc Magn Reson 2023; 25:63. [PMID: 37946191 PMCID: PMC10636995 DOI: 10.1186/s12968-023-00973-6] [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: 05/29/2023] [Accepted: 10/20/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND T1, T2 and T1ρ are well-recognized parameters for quantitative cardiac MRI. Simultaneous estimation of these parameters allows for comprehensive myocardial tissue characterization, such as myocardial fibrosis and edema. However, conventional techniques either quantify the parameters individually with separate breath-hold acquisitions, which may result in unregistered parameter maps, or estimate multiple parameters in a prolonged breath-hold acquisition, which may be intolerable to patients. We propose a free-breathing multi-parametric mapping (FB-MultiMap) technique that provides co-registered myocardial T1, T2 and T1ρ maps in a single efficient acquisition. METHODS The proposed FB-MultiMap performs electrocardiogram-triggered single-shot Cartesian acquisition over 16 consecutive cardiac cycles, where inversion, T2 and T1ρ preparations are introduced for varying contrasts. A diaphragmatic navigator was used for prospective through-plane motion correction and the in-plane motion was corrected retrospectively with a group-wise image registration method. Quantitative mapping was conducted through dictionary matching of the motion corrected images, where the subject-specific dictionary was created using Bloch simulations for a range of T1, T2 and T1ρ values, as well as B1 factors to account for B1 inhomogeneities. The FB-MultiMap was optimized and validated in numerical simulations, phantom experiments, and in vivo imaging of 15 healthy subjects and six patients with suspected cardiac diseases. RESULTS The phantom T1, T2 and T1ρ values estimated with FB-MultiMap agreed well with reference measurements with no dependency on heart rate. In healthy subjects, FB-MultiMap T1 was higher than MOLLI T1 mapping (1218 ± 50 ms vs. 1166 ± 38 ms, p < 0.001). The myocardial T2 and T1ρ estimated with FB-MultiMap were lower compared to the mapping with T2- or T1ρ-prepared 2D balanced steady-state free precession (T2: 41.2 ± 2.8 ms vs. 42.5 ± 3.1 ms, p = 0.06; T1ρ: 45.3 ± 4.4 ms vs. 50.2 ± 4.0, p < 0.001). The pathological changes in myocardial parameters measured with FB-MultiMap were consistent with conventional techniques in all patients. CONCLUSION The proposed free-breathing multi-parametric mapping technique provides co-registered myocardial T1, T2 and T1ρ maps in 16 heartbeats, achieving similar mapping quality to conventional breath-hold mapping methods.
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Affiliation(s)
- Zhenfeng Lyu
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China
- Shanghai Clinical Research and Trial Center, Shanghai, China
| | - Sha Hua
- Department of Cardiovascular Medicine, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Xu
- UIH America, Inc., Houston, TX, USA
| | - Yiwen Shen
- Department of Cardiovascular Medicine, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rui Guo
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Peng Hu
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China.
- Shanghai Clinical Research and Trial Center, Shanghai, China.
| | - Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China.
- Shanghai Clinical Research and Trial Center, Shanghai, China.
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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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Poojar P, Qian E, Fernandes TT, Nunes RG, Fung M, Quarterman P, Jambawalikar SR, Lignelli A, Geethanath S. Tailored magnetic resonance fingerprinting. Magn Reson Imaging 2023; 99:81-90. [PMID: 36764630 DOI: 10.1016/j.mri.2023.02.002] [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: 10/04/2021] [Revised: 01/27/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023]
Abstract
Neuroimaging of certain pathologies requires both multi-parametric qualitative and quantitative imaging. The role of the quantitative MRI (qMRI) is well accepted but suffers from long acquisition times leading to patient discomfort, especially in geriatric and pediatric patients. Previous studies show that synthetic MRI can be used in order to reduce the scan time and provide qMRI as well as multi-contrast data. However, this approach suffers from artifacts such as partial volume and flow. In order to increase the scan efficiency (the number of contrasts and quantitative maps acquired per unit time), we designed, simulated, and demonstrated rapid, simultaneous, multi-contrast qualitative (T1 weighted, T1 fluid attenuated inversion recovery (FLAIR), T2 weighted, water, and fat), and quantitative imaging (T1 and T2 maps) through the approach of tailored MR fingerprinting (TMRF) to cover whole-brain in approximately four minutes. We performed TMRF on in vivo four healthy human brains and in vitro ISMRM/NIST phantom and compared with vendor supplied gold standard (GS) and MRF sequences. All scans were performed on a 3 T GE Premier system and images were reconstructed offline using MATLAB. The reconstructed qualitative images were then subjected to custom DL denoising and gradient anisotropic diffusion denoising. The quantitative tissue parametric maps were reconstructed using a dense neural network to gain computational speed compared to dictionary matching. The grey matter and white matter tissues in qualitative and quantitative data for the in vivo datasets were segmented semi-automatically. The SNR and mean contrasts were plotted and compared across all three methods. The GS images show better SNR in all four subjects compared to MRF and TMRF (GS > TMRF>MRF). The T1 and T2 values of MRF are relatively overestimated as compared to GS and TMRF. The scan efficiency for TMRF is 1.72 min-1 which is higher compared to GS (0.32 min-1) and MRF (0.90 min-1).
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Affiliation(s)
- Pavan Poojar
- Icahn School of Medicine at Mt. Sinai, New York, NY, USA; Columbia Magnetic Resonance Research Center, Columbia University in the city of New York, NY, USA
| | - Enlin Qian
- Columbia Magnetic Resonance Research Center, Columbia University in the city of New York, NY, USA
| | - Tiago T Fernandes
- Institute for Systems and Robotics and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Rita G Nunes
- Institute for Systems and Robotics and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Maggie Fung
- GE Healthcare Applied Sciences Laboratory East, New York, NY, USA
| | | | - Sachin R Jambawalikar
- Department of Radiology, Columbia University Irving Medical Center, Columbia University in the city of New York, NY, USA
| | - Angela Lignelli
- Department of Radiology, Columbia University Irving Medical Center, Columbia University in the city of New York, NY, USA
| | - Sairam Geethanath
- Icahn School of Medicine at Mt. Sinai, New York, NY, USA; Columbia Magnetic Resonance Research Center, Columbia University in the city of New York, NY, USA.
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Beracha I, Seginer A, Tal A. Adaptive model-based Magnetic Resonance. Magn Reson Med 2023. [PMID: 37154407 DOI: 10.1002/mrm.29688] [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: 10/06/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 05/10/2023]
Abstract
PURPOSE Conventional sequences are static in nature, fixing measurement parameters in advance in anticipation of a wide range of expected tissue parameter values. We set out to design and benchmark a new, personalized approach-termed adaptive MR-in which incoming subject data is used to update and fine-tune the pulse sequence parameters in real time. METHODS We implemented an adaptive, real-time multi-echo (MTE) experiment for estimating T2 s. Our approach combined a Bayesian framework with model-based reconstruction. It maintained and continuously updated a prior distribution of the desired tissue parameters, including T2 , which was used to guide the selection of sequence parameters in real time. RESULTS Computer simulations predicted accelerations between 1.7- and 3.3-fold for adaptive multi-echo sequences relative to static ones. These predictions were corroborated in phantom experiments. In healthy volunteers, our adaptive framework accelerated the measurement of T2 for n-acetyl-aspartate by a factor of 2.5. CONCLUSION Adaptive pulse sequences that alter their excitations in real time could provide substantial reductions in acquisition times. Given the generality of our proposed framework, our results motivate further research into other adaptive model-based approaches to MRI and MRS.
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Affiliation(s)
- Inbal Beracha
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | | | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
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Wicaksono KP, Fushimi Y, Nakajima S, Sakata A, Okuchi S, Hinoda T, Oshima S, Otani S, Tagawa H, Urushibata Y, Nakamoto Y. Accuracy, repeatability, and reproducibility of T 1 and T 2 relaxation times measurement by 3D magnetic resonance fingerprinting with different dictionary resolutions. Eur Radiol 2023; 33:2895-2904. [PMID: 36422648 PMCID: PMC10017611 DOI: 10.1007/s00330-022-09244-x] [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: 05/07/2022] [Revised: 08/29/2022] [Accepted: 10/14/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES To assess the accuracy, repeatability, and reproducibility of T1 and T2 relaxation time measurements by three-dimensional magnetic resonance fingerprinting (3D MRF) using various dictionary resolutions. METHODS The ISMRM/NIST phantom was scanned daily for 10 days in two 3 T MR scanners using a 3D MRF sequence reconstructed using four dictionaries with varying step sizes and one dictionary with wider ranges. Thirty-nine healthy volunteers were enrolled: 20 subjects underwent whole-brain MRF scans in both scanners and the rest in one scanner. ROI/VOI analyses were performed on phantom and brain MRF maps. Accuracy, repeatability, and reproducibility metrics were calculated. RESULTS In the phantom study, all dictionaries showed high T1 linearity to the reference values (R2 > 0.99), repeatability (CV < 3%), and reproducibility (CV < 3%) with lower linearity (R2 > 0.98), repeatability (CV < 6%), and reproducibility (CV ≤ 4%) for T2 measurement. The volunteer study demonstrated high T1 reproducibility of within-subject CV (wCV) < 4% by all dictionaries with the same ranges, both in the brain parenchyma and CSF. Yet, reproducibility was moderate for T2 measurement (wCV < 8%). In CSF measurement, dictionaries with a smaller range showed a seemingly better reproducibility (T1, wCV 3%; T2, wCV 8%) than the much wider range dictionary (T1, wCV 5%; T2, wCV 13%). Truncated CSF relaxometry values were evident in smaller range dictionaries. CONCLUSIONS The accuracy, repeatability, and reproducibility of 3D MRF across various dictionary resolutions were high for T1 and moderate for T2 measurements. A lower-resolution dictionary with a well-defined range may be adequate, thus significantly reducing the computational load. KEY POINTS • A lower-resolution dictionary with a well-defined range may be sufficient for 3D MRF reconstruction. • CSF relaxation times might be underestimated due to truncation by the upper dictionary range. • Dictionary with a higher upper range might be advisable, especially for CSF evaluation and elderly subjects whose perivascular spaces are more prominent.
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Affiliation(s)
- Krishna Pandu Wicaksono
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan.
| | - Satoshi Nakajima
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Akihiko Sakata
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Sachi Okuchi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Takuya Hinoda
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Sonoko Oshima
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Sayo Otani
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Hiroshi Tagawa
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | | | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
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11
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Ullah I, Hassan AM, Saad RM, Omer H. GPU accelerated grouped magnetic resonance fingerprinting using clustering techniques. Magn Reson Imaging 2023; 97:13-23. [PMID: 36581213 DOI: 10.1016/j.mri.2022.12.019] [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: 08/17/2022] [Revised: 10/19/2022] [Accepted: 12/23/2022] [Indexed: 12/28/2022]
Abstract
Magnetic Resonance Fingerprinting (MRF) is a new quantitative technique of Magnetic Resonance Imaging (MRI). Conventionally, MRF requires sequential correlation of the acquired MRF signals with all the signals of (a large sized) MRF dictionary. This is a computationally intensive matching process and is a major challenge in MRF image reconstruction. This paper introduces the use of clustering techniques (to reduce the effective size of MRF dictionary) by splitting MRF dictionary into multiple small sized MRF dictionary components called MRF signal groups. The proposed method has been further optimized for parallel processing to reduce the computation time of MRF pattern matching. A multi-core GPU based parallel framework has been developed that enables the MRF algorithm to process multiple MRF signals simultaneously. Experiments have been performed on human head and phantom datasets. The results show that the proposed method accelerates the conventional MRF (MATLAB based) reconstruction time up to 25× with single-core CPU implementation, 300× with multi- core CPU implementation and 1035× with the proposed multi-core GPU based framework by keeping the SNR of the resulting images in a clinically acceptable range. Furthermore, experimental results show that the memory requirements of MRF dictionary get significantly reduced (due to efficient memory utilization) in the proposed method.
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Affiliation(s)
- Irfan Ullah
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan.
| | - Abdul Moiz Hassan
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Rana Muhammad Saad
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Hammad Omer
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
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12
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Cheng F, Liu Y, Chen Y, Yap PT. High-Resolution 3D Magnetic Resonance Fingerprinting With a Graph Convolutional Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:674-683. [PMID: 36269931 PMCID: PMC10081960 DOI: 10.1109/tmi.2022.3216527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging framework for rapid and simultaneous quantification of multiple tissue properties. 3D MRF allows higher through-plane resolution, but the acquisition process is slow when whole-brain coverage is needed. Existing methods for acceleration mainly rely on GRAPPA for k-space interpolation in the partition-encoding direction, limiting the acceleration factor to 2 or 3. In this work, we replace GRAPPA with a deep learning approach for accurate tissue quantification with greater acceleration. Specifically, a graph convolution network (GCN) is developed to cater to the non-Cartesian spiral sampling trajectories typical in MRF acquisition. The GCN maintains high quantification accuracy with up to 6-fold acceleration and allows 1mm isotropic resolution whole-brain 3D MRF data to be acquired in 3min and submillimeter 3D MRF (0.8mm) in 5min, greatly improving the feasibility of MRF in clinical settings.
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13
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Ganter C. Approximate B 1 + scaling of the SSFP steady state. Magn Reson Med 2023; 89:2264-2269. [PMID: 36705048 DOI: 10.1002/mrm.29598] [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: 10/06/2022] [Revised: 01/09/2023] [Accepted: 01/09/2023] [Indexed: 01/28/2023]
Abstract
PURPOSE It is shown that the steady state of rapid, TR-periodic steady-state free precession (SSFP) sequences at small to moderate flip angles exhibits a universal, approximate scaling law with respect to variations of B 1 + $$ {B}_1^{+} $$ . Implications for the accuracy and precision of relaxometry experiments are discussed. METHODS The approximate scaling law is derived from and numerically tested against known analytical solutions. To assess the attainable estimator precision in a typical relaxometry experiment, we calculate the Cramér-Rao bound (CRB) and perform Monte Carlo (MC) simulations. RESULTS The approximate universal scaling holds well up to moderate flip angles. For pure steady state relaxometry, we observe a significant precision penalty for simultaneous estimation of R 1 $$ {R}_1 $$ and B 1 + $$ {B}_1^{+} $$ , whereas good R 2 $$ {R}_2 $$ estimates can be obtained without even knowing the correct actual flip angle. CONCLUSION Simultaneous estimation of R 1 $$ {R}_1 $$ and B 1 + $$ {B}_1^{+} $$ from a set of SSFP steady states alone is not advisable. Apart from separate B 1 + $$ {B}_1^{+} $$ measurements, the problem can be addressed by adding transient state information, but, depending on the situation, residual effects due to the scaling may still require some attention.
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Affiliation(s)
- Carl Ganter
- School of Medicine, Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar der TUM, Technical University of Munich, Munich, Germany
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14
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Gatefait CGF, Ellison SLR, Nyangoma S, Schmitter S, Kolbitsch C. Optimisation of data acquisition towards continuous cardiac Magnetic Resonance Fingerprinting applications. Phys Med 2023; 105:102514. [PMID: 36608390 DOI: 10.1016/j.ejmp.2022.102514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 11/10/2022] [Accepted: 12/12/2022] [Indexed: 01/06/2023] Open
Abstract
PURPOSE Assess and optimise acquisition parameters for continuous cardiac Magnetic Resonance Fingerprinting (MRF). METHODS Different acquisition schemes (flip angle amplitude, lobe size, T2-preparation pulses) for cardiac MRF were assessed in simulations and phantom and demonstrated in one healthy volunteer. Three different experimental designs were evaluated using central composite and fractional factorial designs. Relative errors for T1 and T2 were calculated for a wide range of realistic T1 and T2 value combinations. The effect of different designs on the accuracy of T1 and T2 was assessed using response surface modelling and Cohen's f calculations. RESULTS Larger flip angle amplitudes lead to an improvement of T2 accuracy and precision for simulations and phantom experiments. Similar effects could also be shown qualitatively in in-vivo scans. Accuracy and precision of T1 were robust to different design parameters with improved values for faster flip angle variation. Cohen's f showed that T2-preparation pulses influence the accuracy of T2. The number of pulses used is the most important parameter. Without T2-preparation pulses, RMSE were 3.0 ± 8.09 % for T1 and 16.24 ± 14.47 % for T2. Using those pulses reduced the RMSE to 2.3 ± 8.4 % for T1 and 14.11 ± 13.46 % for T2. Nonetheless, even if the improvement is significant, RMSE are still too high for reliable quantification. CONCLUSION In contrast to previous study using triggered MRF sequences using < 30° flip angles, large flip angle amplitudes led to better results for continuous cardiac MRF sequences. T2-preparation pulse can improve the accuracy of T2 estimation but lead to longer scan times.
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15
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Liver T1 Relaxation Quantification Using a 3-Dimensional Interleaved Look-Locker Acquisition With T2 Preparation Pulse Sequence (3D-QALAS): Comparison With Conventional 2-Dimensional MOLLI. J Comput Assist Tomogr 2022; 47:00004728-990000000-00111. [PMID: 36573324 DOI: 10.1097/rct.0000000000001409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
BACKGROUND Changes in liver magnetic resonance imaging T1 relaxation times are associated with histologic inflammation and fibrosis. OBJECTIVE To compare liver T1 measurements obtained using a novel single-breath-hold 3-dimensional (3D) whole-liver T1 estimation method (3D-QALAS) to standard-of-care 2-dimensional (2D) modified Look-Locker (2D-MOLLI) measurements. METHODS With institutional review board approval, research magnetic resonance imaging examinations were performed in 19 participants at 1.5 T. T1 relaxometry of the liver was performed using a novel 3D whole-liver T1 estimation method (3D-QALAS) as well as a 2D modified Look-Locker (2D-MOLLI) method. The 3D method covered the entire liver in a single breath hold, whereas 2D imaging was performed at 4 anatomic levels in 4 consecutive breath holds. T1 measurements from parametric maps were obtained by a single operator, and region-of-interest area-weighted mean T1 values were calculated. Pearson correlation ( r ) was used to assess correlation between T1 estimation methods, and the paired t test and Bland-Altman analysis were used to compare agreement in T1 measurements. RESULTS In 18 participants (1 participant was excluded from analysis because of respiratory motion artifacts on 3D-QALAS images), 2D-MOLLI and 3D-QALAS mean T1 measurements were strongly correlated ( r = 0.95, [95% CI: 0.87-0.98]; P < 0.0001). 2D-MOLLI T1 values were significantly longer than 3D-QALAS values (647.2 ± 87.3 milliseconds vs. 554.7 ± 75.8 milliseconds; P < 0.0001) with mean bias = 92.5 milliseconds (95% limits of agreement, 36.8, 148.2 milliseconds). CONCLUSION Whole-liver T1 measurements obtained using a novel single-breath-hold 3D T1 estimation method correlate with a standard-of-care multiple consecutive-breath-hold 2D single-slice method but demonstrate systematic bias that should be considered or corrected when used in a clinical or research setting.
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16
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Nykänen O, Nevalainen M, Casula V, Isosalo A, Inkinen SI, Nikki M, Lattanzi R, Cloos MA, Nissi MJ, Nieminen MT. Deep-Learning-Based Contrast Synthesis From MRF Parameter Maps in the Knee Joint. J Magn Reson Imaging 2022. [PMID: 36562500 DOI: 10.1002/jmri.28573] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast-weighted images that are required by radiologists, limiting reachable total scan time improvement. Contrast synthesis from MRF could significantly decrease the imaging time. PURPOSE To improve clinical utility of MRF by synthesizing contrast-weighted MR images from the quantitative data provided by MRF, using U-nets that were trained for the synthesis task utilizing L1- and perceptual loss functions, and their combinations. STUDY TYPE Retrospective. POPULATION Knee joint MRI data from 184 subjects from Northern Finland 1986 Birth Cohort (ages 33-35, gender distribution not available). FIELD STRENGTH AND SEQUENCE A 3 T, multislice-MRF, proton density (PD)-weighted 3D-SPACE (sampling perfection with application optimized contrasts using different flip angle evolution), fat-saturated T2-weighted 3D-space, water-excited double echo steady state (DESS). ASSESSMENT Data were divided into training, validation, test, and radiologist's assessment sets in the following way: 136 subjects to training, 3 for validation, 3 for testing, and 42 for radiologist's assessment. The synthetic and target images were evaluated using 5-point Likert scale by two musculoskeletal radiologists blinded and with quantitative error metrics. STATISTICAL TESTS Friedman's test accompanied with post hoc Wilcoxon signed-rank test and intraclass correlation coefficient. The statistical cutoff P <0.05 adjusted by Bonferroni correction as necessary was utilized. RESULTS The networks trained in the study could synthesize conventional images with high image quality (Likert scores 3-4 on a 5-point scale). Qualitatively, the best synthetic images were produced with combination of L1- and perceptual loss functions and perceptual loss alone, while L1-loss alone led to significantly poorer image quality (Likert scores below 3). The interreader and intrareader agreement were high (0.80 and 0.92, respectively) and significant. However, quantitative image quality metrics indicated best performance for the pure L1-loss. DATA CONCLUSION Synthesizing high-quality contrast-weighted images from MRF data using deep learning is feasible. However, more studies are needed to validate the diagnostic accuracy of these synthetic images. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
- Olli Nykänen
- Department of Applied Physics, Faculty of Science and Forestry, University of Eastern Finland, Kuopio, Finland.,Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Mika Nevalainen
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland.,Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Victor Casula
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - Antti Isosalo
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Satu I Inkinen
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Helsinki University Hospital, Helsinki, Finland
| | - Marko Nikki
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Riccardo Lattanzi
- Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Martijn A Cloos
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Mikko J Nissi
- Department of Applied Physics, Faculty of Science and Forestry, University of Eastern Finland, Kuopio, Finland
| | - Miika T Nieminen
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland.,Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
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17
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Afzali M, Mueller L, Sakaie K, Hu S, Chen Y, Szczepankiewicz F, Griswold MA, Jones DK, Ma D. MR Fingerprinting with b-Tensor Encoding for Simultaneous Quantification of Relaxation and Diffusion in a Single Scan. Magn Reson Med 2022; 88:2043-2057. [PMID: 35713357 PMCID: PMC9420788 DOI: 10.1002/mrm.29352] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 11/08/2022]
Abstract
PURPOSE Although both relaxation and diffusion imaging are sensitive to tissue microstructure, studies have reported limited sensitivity and robustness of using relaxation or conventional diffusion alone to characterize tissue microstructure. Recently, it has been shown that tensor-valued diffusion encoding and joint relaxation-diffusion quantification enable more reliable quantification of compartment-specific microstructural properties. However, scan times to acquire such data can be prohibitive. Here, we aim to simultaneously quantify relaxation and diffusion using MR fingerprinting (MRF) and b-tensor encoding in a clinically feasible time. METHODS We developed multidimensional MRF scans (mdMRF) with linear and spherical b-tensor encoding (LTE and STE) to simultaneously quantify T1, T2, and ADC maps from a single scan. The image quality, accuracy, and scan efficiency were compared between the mdMRF using LTE and STE. Moreover, we investigated the robustness of different sequence designs to signal errors and their impact on the maps. RESULTS T1 and T2 maps derived from the mdMRF scans have consistently high image quality, while ADC maps are sensitive to different sequence designs. Notably, the fast imaging steady state precession (FISP)-based mdMRF scan with peripheral pulse gating provides the best ADC maps that are free of image distortion and shading artifacts. CONCLUSION We demonstrated the feasibility of quantifying T1, T2, and ADC maps simultaneously from a single mdMRF scan in around 24 s/slice. The map quality and quantitative values are consistent with the reference scans.
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Affiliation(s)
- Maryam Afzali
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of Leeds
LeedsUK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff UniversityCardiffUK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of Leeds
LeedsUK
| | - Ken Sakaie
- Imaging Institute, Cleveland ClinicClevelandOhioUSA
| | - Siyuan Hu
- Biomedical EngineeringCase Western Reserve UniversityClevelandOhioUSA
| | - Yong Chen
- RadiologyCase Western Reserve UniversityClevelandOhioUSA
| | | | | | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff UniversityCardiffUK
| | - Dan Ma
- Biomedical EngineeringCase Western Reserve UniversityClevelandOhioUSA
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18
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Kang J, Nam Y. [Applications of Artificial Intelligence in MR Image Acquisition and Reconstruction]. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:1229-1239. [PMID: 36545429 PMCID: PMC9748458 DOI: 10.3348/jksr.2022.0156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 12/12/2022]
Abstract
Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.
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19
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Hu Y, Li P, Chen H, Zou L, Wang H. High-Quality MR Fingerprinting Reconstruction Using Structured Low-Rank Matrix Completion and Subspace Projection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1150-1164. [PMID: 34871169 DOI: 10.1109/tmi.2021.3133329] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Due to the capability of fast multiparametric quantitative imaging, magnetic resonance fingerprinting (MRF) is becoming a promising quantitative magnetic resonance imaging approach. However, the artifacts caused by the highly undersampled data acquisition lead to inaccurate estimation of the tissue parameter maps. Based on the assumption that the 3-D MRF data can be modeled as a piecewise smooth signal, with the discontinuities localized to the zero sets of a bandlimited function, we exploit the low-rank property of the structured Toeplitz matrix constructed from the Fourier measurements. In addition, we adopt the subspace projection scheme to improve the accuracy of parameter estimation. In order to efficiently solve the regularized problem, we propose an iterative two-stage algorithm, which alternately updates the k -space data and projects the space-time matrix into the dictionary space. Numerical experiments demonstrate that the proposed algorithm shows significant improvement in MRF time-series images reconstruction and can provide more accurate parameter maps over the state-of-the-art algorithms.
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20
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Barbieri M, Lee PK, Brizi L, Giampieri E, Solera F, Castellani G, Hargreaves BA, Testa C, Lodi R, Remondini D. Circumventing the curse of dimensionality in magnetic resonance fingerprinting through a deep learning approach. NMR IN BIOMEDICINE 2022; 35:e4670. [PMID: 35088466 DOI: 10.1002/nbm.4670] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 11/15/2021] [Accepted: 12/02/2021] [Indexed: 06/14/2023]
Abstract
Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI. A typical drawback of dictionary-based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the "curse of dimensionality", which determines an explosion of resource requirements. Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy. In this work, we design a deep learning approach to MRF using a fully connected network (FCN). In the first part we investigate, by means of simulations, how the NN performance scales with the number of parameters to be retrieved in comparison with the standard dictionary approach. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1 , and IR-bSSFP-B1 , the latter two designed to be more specific for B1+ parameter encoding. Estimation accuracy, memory usage, and computational time required to perform the estimation task were considered to compare the scalability capabilities of the dictionary-based and the NN approaches. In the second part we study optimal training procedures by including different data augmentation and preprocessing strategies during training to achieve better accuracy and robustness to noise and undersampling artifacts. The study is conducted using the IR-FISP MRF sequence exploiting both simulations and in vivo acquisitions. Results demonstrate that the NN approach outperforms the dictionary-based approach in terms of scalability capabilities. Results also allow us to heuristically determine the optimal training strategy to make an FCN able to predict T1 , T2 , and M0 maps that are in good agreement with those obtained with the original dictionary approach. k-SVD denoising is proposed and found to be critical as a preprocessing step to handle undersampled data.
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Affiliation(s)
- Marco Barbieri
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- Department of Radiology, Stanford University, California, United States
| | - Philip K Lee
- Department of Electrical Engineering, Stanford University, California, United States
| | - Leonardo Brizi
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- INFN, Sezione di Bologna, Bologna, Italy
| | - Enrico Giampieri
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | | | - Gastone Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, California, United States
- Department of Electrical Engineering, Stanford University, California, United States
- Department of Bioengineering, Stanford University, California, United States
| | - Claudia Testa
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy
| | - Raffaele Lodi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Daniel Remondini
- Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy
- INFN, Sezione di Bologna, Bologna, Italy
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21
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Schick F. Automatic segmentation and volumetric assessment of internal organs and fatty tissue: what are the benefits? MAGNETIC RESONANCE MATERIALS IN PHYSICS, BIOLOGY AND MEDICINE 2022; 35:187-192. [PMID: 34919193 PMCID: PMC8995273 DOI: 10.1007/s10334-021-00986-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/03/2021] [Accepted: 12/05/2021] [Indexed: 02/07/2023]
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22
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Fujita S, Hagiwara A, Yasaka K, Akai H, Kunimatsu A, Kiryu S, Fukunaga I, Kato S, Akashi T, Kamagata K, Wada A, Abe O, Aoki S. Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features. Eur Radiol 2022; 32:4791-4800. [PMID: 35304637 PMCID: PMC9213334 DOI: 10.1007/s00330-022-08555-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 11/23/2021] [Accepted: 12/23/2021] [Indexed: 11/17/2022]
Abstract
Objectives We aimed to investigate the influence of magnetic resonance fingerprinting (MRF) dictionary design on radiomic features using in vivo human brain scans. Methods Scan-rescans of three-dimensional MRF and conventional T1-weighted imaging were performed on 21 healthy volunteers (9 males and 12 females; mean age, 41.3 ± 14.6 years; age range, 22–72 years). Five patients with multiple sclerosis (3 males and 2 females; mean age, 41.2 ± 7.3 years; age range, 32–53 years) were also included. MRF data were reconstructed using various dictionaries with different step sizes. First- and second-order radiomic features were extracted from each dataset. Intra-dictionary repeatability and inter-dictionary reproducibility were evaluated using intraclass correlation coefficients (ICCs). Features with ICCs > 0.90 were considered acceptable. Relative changes were calculated to assess inter-dictionary biases. Results The overall scan-rescan ICCs of MRF-based radiomics ranged from 0.86 to 0.95, depending on dictionary step size. No significant differences were observed in the overall scan-rescan repeatability of MRF-based radiomic features and conventional T1-weighted imaging (p = 1.00). Intra-dictionary repeatability was insensitive to dictionary step size differences. MRF-based radiomic features varied among dictionaries (overall ICC for inter-dictionary reproducibility, 0.62–0.99), especially when step sizes were large. First-order and gray level co-occurrence matrix features were the most reproducible feature classes among different step size dictionaries. T1 map-derived radiomic features provided higher repeatability and reproducibility among dictionaries than those obtained with T2 maps. Conclusion MRF-based radiomic features are highly repeatable in various dictionary step sizes. Caution is warranted when performing MRF-based radiomics using datasets containing maps generated from different dictionaries. Key Points • MRF-based radiomic features are highly repeatable in various dictionary step sizes. • Use of different MRF dictionaries may result in variable radiomic features, even when the same MRF acquisition data are used. • Caution is needed when performing radiomic analysis using data reconstructed from different dictionaries. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08555-3.
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Affiliation(s)
- Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan. .,Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8654, Japan.
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Koichiro Yasaka
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shiroganedai, Minato, Tokyo, 108-8639, Japan
| | - Hiroyuki Akai
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shiroganedai, Minato, Tokyo, 108-8639, Japan
| | - Akira Kunimatsu
- Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shiroganedai, Minato, Tokyo, 108-8639, Japan
| | - Shigeru Kiryu
- Department of Radiology, International University of Health and Welfare Narita Hospital, 852, Hatakeda, Narita, Chiba, 286-8520, Japan
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Shimpei Kato
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan.,Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8654, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo, Tokyo, 113-8654, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo, 113-8421, Japan
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Li S, Wu J, Ma L, Cai S, Cai C. A simultaneous multi-slice T 2 mapping framework based on overlapping-echo detachment planar imaging and deep learning reconstruction. Magn Reson Med 2022; 87:2239-2253. [PMID: 35014727 DOI: 10.1002/mrm.29128] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 11/29/2021] [Accepted: 11/29/2021] [Indexed: 12/16/2022]
Abstract
PURPOSE Quantitative MRI (qMRI) is of great importance to clinical medicine and scientific research. However, most qMRI techniques are time-consuming and sensitive to motion, especially when a large 3D volume is imaged. To accelerate the acquisition, a framework is proposed to realize reliable simultaneous multi-slice T2 mapping. METHODS The simultaneous multi-slice T2 mapping framework is based on overlapping-echo detachment (OLED) planar imaging (dubbed SMS-OLED). Multi-slice overlapping-echo signals were generated by multiple excitation pulses together with echo-shifting gradients. The signals were excited and acquired with a single-channel coil. U-Net was used to reconstruct T2 maps from the acquired overlapping-echo image. RESULTS Single-shot double-slice and two-shot triple-slice SMS-OLED scan schemes were designed according to the framework for evaluation. Simulations, water phantom, and in vivo rat brain experiments were carried out. Overlapping-echo signals were acquired, and T2 maps were reconstructed and compared with references. The results demonstrate the superior performance of our method. CONCLUSION Two slices of T2 maps can be obtained in a single shot within hundreds of milliseconds. Higher quality multi-slice T2 maps can be obtained via multiple shots. SMS-OLED provides a lower specific absorption rate scheme compared with conventional SMS methods with a coil with only a single receiver channel. The new method is of potential in dynamic qMRI and functional qMRI where temporal resolution is vital.
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Affiliation(s)
- Simin Li
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Jian Wu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Lingceng Ma
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Shuhui Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
| | - Congbo Cai
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
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24
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West DJ, Cruz G, Teixeira RPAG, Schneider T, Tournier JD, Hajnal JV, Prieto C, Malik SJ. An MR fingerprinting approach for quantitative inhomogeneous magnetization transfer imaging. Magn Reson Med 2022; 87:220-235. [PMID: 34418151 PMCID: PMC7614010 DOI: 10.1002/mrm.28984] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/15/2021] [Accepted: 08/05/2021] [Indexed: 01/07/2023]
Abstract
PURPOSE Magnetization transfer (MT) and inhomogeneous MT (ihMT) contrasts are used in MRI to provide information about macromolecular tissue content. In particular, MT is sensitive to macromolecules, and ihMT appears to be specific to myelinated tissue. This study proposes a technique to characterize MT and ihMT properties from a single acquisition, producing both semiquantitative contrast ratios and quantitative parameter maps. THEORY AND METHODS Building on previous work that uses multiband RF pulses to efficiently generate ihMT contrast, we propose a cyclic steady-state approach that cycles between multiband and single-band pulses to boost the achieved contrast. Resultant time-variable signals are reminiscent of an MR fingerprinting acquisition, except that the signal fluctuations are entirely mediated by MT effects. A dictionary-based low-rank inversion method is used to reconstruct the resulting images and to produce both semiquantitative MT ratio and ihMT ratio maps, as well as quantitative parameter estimates corresponding to an ihMT tissue model. RESULTS Phantom and in vivo brain data acquired at 1.5 Tesla demonstrate the expected contrast trends, with ihMT ratio maps showing contrast more specific to white matter, as has been reported by others. Quantitative estimation of semisolid fraction and dipolar T1 was also possible and yielded measurements consistent with literature values in the brain. CONCLUSION By cycling between multiband and single-band pulses, an entirely MT-mediated fingerprinting method was demonstrated. This proof-of-concept approach can be used to generate semiquantitative maps and quantitatively estimate some macromolecular-specific tissue parameters.
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Affiliation(s)
- Daniel J. West
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Gastao Cruz
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Rui P. A. G. Teixeira
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | | | - Jacques-Donald Tournier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Joseph V. Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Shaihan J. Malik
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
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25
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Shpringer G, Bendahan D, Ben-Eliezer N. Accelerated reconstruction of dictionary-based T 2 relaxation maps based on dictionary compression and gradient descent search algorithms. Magn Reson Imaging 2021; 87:56-66. [PMID: 34973389 DOI: 10.1016/j.mri.2021.12.006] [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: 06/18/2021] [Revised: 11/19/2021] [Accepted: 12/22/2021] [Indexed: 11/18/2022]
Abstract
Background Quantitative T2-relaxation-based contrast maps have shown to be highly beneficial for clinical diagnosis and follow-up. The generation of quantitative maps, however, is impaired by long acquisition times, and time-consuming post-processing schemes. The EMC platform is a dictionary-based technique, which involves simulating theoretical signal curves for different physical and experimental values, followed by matching the experimentally acquired signals to the set simulated ones. Purpose Although the EMC technique has shown to produce accurate T2 maps, it involves computationally intensive post-processing procedures. In this work we present an approach for accelerating the reconstruction of T2 relaxation maps. Methods This work presents two alternative post-processing approaches for accelerating the reconstruction of EMC-based T2 relaxation maps. These are (a) Dictionary compression using principal component analysis (PCA) and (b) gradient-descent search algorithm. Additional acceleration was achieved by finding the optimal MATLAB C++ compiler. The utility of the two suggested approaches was examined by calculating the relative error, produced by each technique. Results Gradient descent method was in perfect agreement with the ground truth exhaustive search matching process. PCA based acceleration produced root mean square error (RMSE) of up to 4% compared to exhaustive matching process. Overall acceleration of x16 was achieved using gradient descent in addition to x7 acceleration by choosing the optimal MATLAB C++ compiler. Conclusions Postprocessing of EMC-based T2 relaxation maps can be accelerated without impairing the accuracy of the ensuing T2 values.
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Affiliation(s)
- Guy Shpringer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - David Bendahan
- Aix Marseille University, CNRS UMR 7339, CRMBM, Marseille, France
| | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel; Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Medical Center, New York, NY, USA.
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26
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Omer N, Galun M, Stern N, Blumenfeld-Katzir T, Ben-Eliezer N. Data-driven algorithm for myelin water imaging: Probing subvoxel compartmentation based on identification of spatially global tissue features. Magn Reson Med 2021; 87:2521-2535. [PMID: 34958690 DOI: 10.1002/mrm.29125] [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: 07/19/2021] [Revised: 11/23/2021] [Accepted: 11/26/2021] [Indexed: 01/10/2023]
Abstract
PURPOSE Multicomponent analysis of MRI T2 relaxation time (mcT2 ) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T2 values. This voxel-based approach is challenging due to the large ambiguity in the multi-T2 space and the low SNR of MRI signals. Herein, we present a data-driven mcT2 analysis, which utilizes the statistical strength of identifying spatially global mcT2 motifs in white matter segments before deconvolving the local signal at each voxel. METHODS Deconvolution is done using a tailored optimization scheme, which incorporates the global mcT2 motifs without additional prior assumptions regarding the number of microscopic components. The end results of this process are voxel-wise myelin water fraction maps. RESULTS Validations are shown for computer-generated signals, uniquely designed subvoxel mcT2 phantoms, and in vivo human brain. Results demonstrated excellent fitting accuracy, both for the numerical and the physical mcT2 phantoms, exhibiting excellent agreement between calculated myelin water fraction and ground truth. Proof-of-concept in vivo validation is done by calculating myelin water fraction maps for white matter segments of the human brain. Interscan stability of myelin water fraction values was also estimated, showing good correlation between scans. CONCLUSION We conclude that studying global tissue motifs prior to performing voxel-wise mcT2 analysis stabilizes the optimization scheme and efficiently overcomes the ambiguity in the T2 space. This new approach can improve myelin water imaging and the investigation of microstructural compartmentation in general.
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Affiliation(s)
- Noam Omer
- The Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Meirav Galun
- Department of Computer Science and Applied Mathematics, Weitzman Institute of Science, Rehovot, Israel
| | - Neta Stern
- The Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | | | - Noam Ben-Eliezer
- The Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Medical Center, New York, New York, USA
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27
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Snyder J, McPhee KC, Wilman AH. T 2 quantification in brain using 3D fast spin-echo imaging with long echo trains. Magn Reson Med 2021; 87:2145-2160. [PMID: 34894641 PMCID: PMC9299830 DOI: 10.1002/mrm.29113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 11/15/2022]
Abstract
Purpose Three‐dimensional fast spin‐echo (FSE) sequences commonly use very long echo trains (>64 echoes) and severely reduced refocusing angles. They are increasingly used in brain exams due to high, isotropic resolution and reasonable scan time when using long trains and short interecho spacing. In this study, T2 quantification in 3D FSE is investigated to achieve increased resolution when comparing with established 2D (proton‐density dual‐echo and multi‐echo spin‐echo) methods. Methods The FSE sequence design was explored to use long echo trains while minimizing T2 fitting error and maintaining typical proton density and T2‐weighted contrasts. Constant and variable flip angle trains were investigated using extended phase graph and Bloch equation simulations. Optimized parameters were analyzed in phantom experiments and validated in vivo in comparison to 2D methods for eight regions of interest in brain, including deep gray‐matter structures and white‐matter tracts. Results Phantom and healthy in vivo brain T2 measurements showed that optimized variable echo‐train 3D FSE performs similarly to previous 2D methods, while achieving three‐fold‐higher slice resolution, evident visually in the 3D T2 maps. Optimization resulted in better T2 fitting and compared well with standard multi‐echo spin echo (within the 8‐ms confidence limits defined based on Bland‐Altman analysis). Conclusion T2 mapping using 3D FSE with long echo trains and variable refocusing angles provides T2 accuracy in agreement with 2D methods with additional high‐resolution benefits, allowing isotropic views while avoiding incidental magnetization transfer effects. Consequently, optimized 3D sequences should be considered when choosing T2 mapping methods for high anatomic detail.
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Affiliation(s)
- Jeff Snyder
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
| | | | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Canada
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28
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Coronado R, Cruz G, Castillo-Passi C, Tejos C, Uribe S, Prieto C, Irarrazaval P. A Spatial Off-Resonance Correction in Spirals for Magnetic Resonance Fingerprinting. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3832-3842. [PMID: 34310296 DOI: 10.1109/tmi.2021.3100293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In MR Fingerprinting (MRF), balanced Steady-State Free Precession (bSSFP) has advantages over unbalanced SSFP because it retains the spin history achieving a higher signal-to-noise ratio (SNR) and scan efficiency. However, bSSFP-MRF is not frequently used because it is sensitive to off-resonance, producing artifacts and blurring, and affecting the parametric map quality. Here we propose a novel Spatial Off-resonance Correction (SOC) approach for reducing these artifacts in bSSFP-MRF with spiral trajectories. SOC-MRF uses each pixel's Point Spread Function to create system matrices that encode both off-resonance and gridding effects. We iteratively compute the inverse of these matrices to reduce the artifacts. We evaluated the proposed method using brain simulations and actual MRF acquisitions of a standardized T1/T2 phantom and five healthy subjects. The results show that the off-resonance distortions in T1/T2 maps were considerably reduced using SOC-MRF. For T2, the Normalized Root Mean Square Error (NRMSE) was reduced from 17.3 to 8.3% (simulations) and from 35.1 to 14.9% (phantom). For T1, the NRMS was reduced from 14.7 to 7.7% (simulations) and from 17.7 to 6.7% (phantom). For in-vivo, the mean and standard deviation in different ROI in white and gray matter were significantly improved. For example, SOC-MRF estimated an average T2 for white matter of 77ms (the ground truth was 74ms) versus 50 ms of MRF. For the same example the standard deviation was reduced from 18 ms to 6ms. The corrections achieved with the proposed SOC-MRF may expand the potential applications of bSSFP-MRF, taking advantage of its better SNR property.
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29
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Li G, Sun B, Li Y, Luo C, He Z, Sun J. Small-Molecule Prodrug Nanoassemblies: An Emerging Nanoplatform for Anticancer Drug Delivery. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2101460. [PMID: 34342126 DOI: 10.1002/smll.202101460] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 05/21/2021] [Indexed: 06/13/2023]
Abstract
The antitumor efficiency and clinical translation of traditional nanomedicines is mainly restricted by low drug loading, complex preparation technology, and potential toxicity caused by the overused carrier materials. In recent decades, small-molecule prodrug nanoassemblies (SMP-NAs), which are formed by the self-assembly of prodrugs themselves, have been widely investigated with distinct advantages of ultrahigh drug-loading and negligible excipients-trigged adverse reaction. Benefited from the simple preparation process, SMP-NAs are widely used for chemotherapy, phototherapy, immunotherapy, and tumor diagnosis. In addition, combination therapy based on the accurate co-delivery behavior of SMP-NAs can effectively address the challenges of tumor heterogeneity and multidrug resistance. Recent trends in SMP-NAs are outlined, and the corresponding self-assembly mechanisms are discussed in detail. Besides, the smart stimuli-responsive SMP-NAs and the combination therapy based on SMP-NAs are summarized, with special emphasis on the structure-function relationships. Finally, the outlooks and potential challenges of SMP-NAs in cancer therapy are highlighted.
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Affiliation(s)
- Guanting Li
- Department of Pharmaceutics, Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, 110016, China
| | - Bingjun Sun
- Department of Pharmaceutics, Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, 110016, China
| | - Yaqiao Li
- Department of Pharmaceutics, Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, 110016, China
| | - Cong Luo
- Department of Pharmaceutics, Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, 110016, China
| | - Zhonggui He
- Department of Pharmaceutics, Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, 110016, China
| | - Jin Sun
- Department of Pharmaceutics, Wuya College of Innovation, Shenyang Pharmaceutical University, Shenyang, 110016, China
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30
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Statton BK, Smith J, Finnegan ME, Koerzdoerfer G, Quest RA, Grech-Sollars M. Temperature dependence, accuracy, and repeatability of T 1 and T 2 relaxation times for the ISMRM/NIST system phantom measured using MR fingerprinting. Magn Reson Med 2021; 87:1446-1460. [PMID: 34752644 DOI: 10.1002/mrm.29065] [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: 05/13/2021] [Revised: 09/23/2021] [Accepted: 10/14/2021] [Indexed: 01/09/2023]
Abstract
PURPOSE Before MR fingerprinting (MRF) can be adopted clinically, the derived quantitative values must be proven accurate and repeatable over a range of T1 and T2 values and temperatures. Correct assessment of accuracy and precision as well as comparison between measurements can only be performed when temperature is either controlled or corrected for. The purpose of this study was to investigate the temperature dependence of T1 and T2 MRF values and evaluate the accuracy and repeatability of temperature-corrected relaxation values derived from a B1 -corrected MRF-fast imaging with steady-state precession implementation using 2 different dictionary sizes. METHODS The International Society of MR in Medicine/National Institute of Standards and Technology phantom was scanned using an MRF sequence of 2 different lengths, a variable flip angle T1 , and a multi-echo spin echo T2 at 14 temperatures ranging from 15°C to 28°C and investigated with a linear regression model. Temperature-corrected accuracy was evaluated by correlating T1 and T2 times from each MRF dictionary with reference values. Repeatability was assessed using the coefficient of variation, with measurements taken over 30 separate sessions. RESULTS There was a statistically significant fit of the model for MRF-derived T1 and T2 and temperature (p < 0.05) for all the spheres with a T1 > 500 ms. Both MRF methods showed a strong linear correlation with reference values for T1 (R2 = 0.996) and T2 (R2 = 0.982). MRF repeatability for T1 values was ≤1.4% and for T2 values was ≤3.4%. CONCLUSION MRF demonstrated relaxation times with a temperature dependence similar to that of conventional mapping methods. Temperature-corrected T1 and T2 values from both dictionaries showed adequate accuracy and excellent repeatability in this phantom study.
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Affiliation(s)
- Ben K Statton
- Medical Research Council, London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Joely Smith
- Department of Imaging, Imperial College Healthcare NHS Trust, London, United Kingdom.,Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Mary E Finnegan
- Department of Imaging, Imperial College Healthcare NHS Trust, London, United Kingdom.,Department of Bioengineering, Imperial College London, London, United Kingdom
| | | | - Rebecca A Quest
- Department of Imaging, Imperial College Healthcare NHS Trust, London, United Kingdom.,Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Matthew Grech-Sollars
- Department of Medical Physics, Royal Surrey NHS Foundation Trust, Guildford, United Kingdom.,Department of Surgery and Cancer, Imperial College London, London, United Kingdom
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Ding H, Velasco C, Ye H, Lindner T, Grech-Sollars M, O’Callaghan J, Hiley C, Chouhan MD, Niendorf T, Koh DM, Prieto C, Adeleke S. Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer. Cancers (Basel) 2021; 13:4742. [PMID: 34638229 PMCID: PMC8507535 DOI: 10.3390/cancers13194742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/08/2021] [Accepted: 09/16/2021] [Indexed: 11/25/2022] Open
Abstract
Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment.
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Affiliation(s)
- Hao Ding
- Imperial College School of Medicine, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK;
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London SE1 7EH, UK; (C.V.); (C.P.)
| | - Huihui Ye
- State Key Laboratory of Modern Optical instrumentation, Zhejiang University, Hangzhou 310027, China;
| | - Thomas Lindner
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg Eppendorf, 20246 Hamburg, Germany;
| | - Matthew Grech-Sollars
- Department of Medical Physics, Royal Surrey NHS Foundation Trust, Surrey GU2 7XX, UK;
- Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, UK
| | - James O’Callaghan
- UCL Centre for Medical Imaging, Division of Medicine, University College London, London W1W 7TS, UK; (J.O.); (M.D.C.)
| | - Crispin Hiley
- Cancer Research UK, Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK;
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Manil D. Chouhan
- UCL Centre for Medical Imaging, Division of Medicine, University College London, London W1W 7TS, UK; (J.O.); (M.D.C.)
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck, Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany;
| | - Dow-Mu Koh
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SM2 5NG, UK;
- Department of Radiology, Royal Marsden Hospital, London SW3 6JJ, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London SE1 7EH, UK; (C.V.); (C.P.)
| | - Sola Adeleke
- High Dimensional Neurology Group, Queen’s Square Institute of Neurology, University College London, London WC1N 3BG, UK
- Department of Oncology, Guy’s & St Thomas’ Hospital, London SE1 9RT, UK
- School of Cancer & Pharmaceutical Sciences, King’s College London, London WC2R 2LS, UK
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Lena B, Bos C, Ferrer CJ, Moonen CTW, Viergever MA, Bartels LW. Rapid 2D variable flip angle method for accurate and precise T 1 measurements over a wide range of T 1 values. NMR IN BIOMEDICINE 2021; 34:e4542. [PMID: 34031938 PMCID: PMC8365751 DOI: 10.1002/nbm.4542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 04/23/2021] [Accepted: 04/26/2021] [Indexed: 05/07/2023]
Abstract
PURPOSE To perform dynamic T1 mapping using a 2D variable flip angle (VFA) method, a correction for the slice profile effect is needed. In this work we investigated the impact of flip angle selection and excitation RF pulse profile on the performance of slice profile correction when applied to T1 mapping over a range of T1 values. METHODS A correction of the slice profile effect is proposed, based on Bloch simulation of steady-state signals. With this correction, Monte Carlo simulations were performed to assess the accuracy and precision of 2D VFA T1 mapping in the presence of noise, for RF pulses with time-bandwidth products of 2, 3 and 10 and with flip angle pairs in the range [1°-90°]. To evaluate its performance over a wide range of T1 , maximum errors were calculated for six T1 values between 50 ms and 1250 ms. The method was demonstrated using in vitro and in vivo experiments. RESULTS Without corrections, 2D VFA severely underestimates T1 . Slice profile errors were effectively reduced with the correction based on simulations, both in vitro and in vivo. The precision and accuracy of the method depend on the nominal T1 values, the FA pair, and the RF pulse shape. FA pairs leading to <5% errors in T1 can be identified for the common RF shapes, for T1 values between 50 ms and 1250 ms. CONCLUSIONS 2D VFA T1 mapping with Bloch-simulation-based correction can deliver T1 estimates that are accurate and precise to within 5% over a wide T1 range.
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Affiliation(s)
- Beatrice Lena
- Image Sciences Institute, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
- Image Guided Molecular Interventions Group, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Clemens Bos
- Image Guided Molecular Interventions Group, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Cyril J. Ferrer
- Image Guided Molecular Interventions Group, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Chrit T. W. Moonen
- Image Guided Molecular Interventions Group, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Max A. Viergever
- Image Sciences Institute, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Lambertus W. Bartels
- Image Sciences Institute, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
- Image Guided Molecular Interventions Group, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
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33
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Boux F, Forbes F, Arbel J, Lemasson B, Barbier EL. Bayesian Inverse Regression for Vascular Magnetic Resonance Fingerprinting. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1827-1837. [PMID: 33729931 DOI: 10.1109/tmi.2021.3066781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Standard parameter estimation from vascular magnetic resonance fingerprinting (MRF) data is based on matching the MRF signals to their best counterparts in a grid of coupled simulated signals and parameters, referred to as a dictionary. To reach a good accuracy, the matching requires an informative dictionary whose cost, in terms of design, storage and exploration, is rapidly prohibitive for even moderate numbers of parameters. In this work, we propose an alternative dictionary-based statistical learning (DB-SL) approach made of three steps: 1) a quasi-random sampling strategy to produce efficiently an informative dictionary, 2) an inverse statistical regression model to learn from the dictionary a correspondence between fingerprints and parameters, and 3) the use of this mapping to provide both parameter estimates and their confidence indices. The proposed DB-SL approach is compared to both the standard dictionary-based matching (DBM) method and to a dictionary-based deep learning (DB-DL) method. Performance is illustrated first on synthetic signals including scalable and standard MRF signals with spatial undersampling noise. Then, vascular MRF signals are considered both through simulations and real data acquired in tumor bearing rats. Overall, the two learning methods yield more accurate parameter estimates than matching and to a range not limited to the dictionary boundaries. DB-SL in particular resists to higher noise levels and provides in addition confidence indices on the estimates at no additional cost. DB-SL appears as a promising method to reduce simulation needs and computational requirements, while modeling sources of uncertainty and providing both accurate and interpretable results.
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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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Quantification of T1, T2 relaxation times from Magnetic Resonance Fingerprinting radially undersampled data using analytical transformations. Magn Reson Imaging 2021; 80:81-89. [PMID: 33932541 DOI: 10.1016/j.mri.2021.04.013] [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: 03/17/2021] [Revised: 04/16/2021] [Accepted: 04/25/2021] [Indexed: 01/03/2023]
Abstract
Quantitative magnetic resonance imaging (MRI) estimates magnetic parameters related to tissue, such as T1, T2 relaxation times and proton density. MR fingerprinting (MRF) is a new concept that uses pseudo-random, incoherent measurements to create a unique fingerprint for each tissue type to quantify magnet parameters. This paper aims to enhance MRF performance by investigating (i) the most suitable acquisition trajectory, and (ii) analytical transformations, suitable for radial acquisitions. Highly undersampled MRF brain (k, t)-space data have been simulated and non-linearly reconstructed to exploit the low-rank property of dynamic imaging. Based on our findings, the radial trajectory is the most suitable for MRF compared to Cartesian and spiral acquisitions. Perhaps this is due to the fact that its aliasing artifacts are more noise-like, and that unlike spiral trajectories, it can use analytical transformations that do not require re-gridding. One such analytical algorithm is the spline reconstruction technique (SRT) that is based on a novel numerical implementation of an analytic representation of the inverse Radon transform. Here, for the first time, this algorithm is applied to MR radial data. Reconstructions using SRT were compared to the ones using filtered back-projection. SRT provided images of higher contrast, lower bias, which resulted in more accurate T1, T2 values.
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Shridhar Konar A, Qian E, Geethanath S, Buonincontri G, Obuchowski NA, Fung M, Gomez P, Schulte R, Cencini M, Tosetti M, Schwartz LH, Shukla-Dave A. Quantitative imaging metrics derived from magnetic resonance fingerprinting using ISMRM/NIST MRI system phantom: An international multicenter repeatability and reproducibility study. Med Phys 2021; 48:2438-2447. [PMID: 33690905 DOI: 10.1002/mp.14833] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 02/04/2021] [Accepted: 02/23/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To compare the bias and inherent reliability of the quantitative (T1 and T2 ) imaging metrics generated from the magnetic resonance fingerprinting (MRF) technique using the ISMRM/NIST system phantom in an international multicenter setting. METHOD ISMRM/NIST MRI system phantom provides standard reference T1 and T2 relaxation values (vendor-provided) for each of the 14 vials in T1 and T2 arrays. MRF-SSFP scans repeated over 30 days on GE 1.5 and 3.0 T scanners at three collaborative centers. MRF estimated T1, and T2 values averaged over 30 days were compared with the phantom vendor-provided and spin-echo (SE) based convention gold standard (GS) method. Repeatability and reproducibility were characterized by the within-case coefficient of variation (wCV) of the MRF data acquired over 30 days, along with the biases. RESULT For the wide ranges of MRF estimated T1 values, vials #1-8 (T1 relaxation time between 2033 and 184 ms) exhibited a wCV less than 3% and 4%, respectively, on 3.0 and 1.5 T scanners. T2 values in vials #1-8 (T2 relaxation, 1044-45 ms) have shown wCV to be <7% on both 3.0 and 1.5 T scanners. A stronger linear correlation overall for T1 (R2 = 0.9960 and 0.9963 at center-1 and center-2 on 3.0 T scanner, and R2 = 0.9951 and 0.9988 at center-1 and center-3 on 1.5 T scanner) compared to T2 (R2 = 0.9971 and 0.9972 at center-1 and center-2 on 3.0 T scanner, and R2 = 0.9815 and 0.9754 at center-1 and center-3 on 1.5 T scanner). Bland-Altman (BA) analysis showed MRF based T1 and T2 values were within the limit of agreement (LOA) except for one data point. The mean difference or bias and 95% lower bound (LB) and upper bound (UB) LOA are reported in the format; mean bias: 95% LB LOA: 95% UB LOA. The biases for T1 values were 21.34: -50.00: 92.69, 21.32: -47.29: 89.94 ms, and for T2 values were -19.88: -42.37: 2.61, -19.06: -43.58: 5.45 ms on 3.0 T scanner at center-1 and center-2, respectively. Similarly, on 1.5 T scanner biases for T1 values were 26.54: -53.41: 106.50, 9.997: -51.94: 71.94 ms, and for T2 values were -23.84: -135.40: 87.76, -37.30: 134.30: 59.73 ms at center-1 and center-3, respectively. Additionally, the correlation between the SE based GS and MRF estimated T1 and T2 values (R2 = 0.9969 and 0.9977) showed a similar trend as we observed between vendor-provided and MRF estimated T1 and T2 values (R2 = 0.9963 and 0.9972). In addition to correlation, BA analysis showed that all the vials are within the LOA between the GS and vendor-provided for the T1 values and except one vial for T2 . All the vials are within the LOA between GS and MRF except one vial in T1 and T2 array. The wCV for reproducibility was <3% for both T1 and T2 values in vials #1-8, for all the 14 vials, wCV calculated for reproducibility was <4% for T1 values and <5% for T2 . CONCLUSION This study shows that MRF is highly repeatable (wCV <4% for T1 and <7% for T2 ) and reproducible (wCV < 3% for both T1 and T2 ) in certain vials (vials #1-8).
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Affiliation(s)
- Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Enlin Qian
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY, 10027, USA
| | - Sairam Geethanath
- Columbia Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY, 10027, USA
| | - Guido Buonincontri
- Imago7 Foundation and IRCCS Stella Maris Foundation, Pisa, PI, 56128, Italy
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH, 44106, USA
| | | | - Pedro Gomez
- Munich School of Bioengineering, Technical University of Munich, Munich, BY, 85748, Germany
| | | | - Matteo Cencini
- Imago7 Foundation and IRCCS Stella Maris Foundation, Pisa, PI, 56128, Italy
| | - Michela Tosetti
- Imago7 Foundation and IRCCS Stella Maris Foundation, Pisa, PI, 56128, Italy
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, New York, NY, 10032, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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Sharafi A, Medina K, Zibetti MWV, Rao S, Cloos MA, Brown R, Regatte RR. Simultaneous T 1 , T 2 , and T 1ρ relaxation mapping of the lower leg muscle with MR fingerprinting. Magn Reson Med 2021; 86:372-381. [PMID: 33554369 DOI: 10.1002/mrm.28704] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 12/31/2020] [Accepted: 01/09/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE To develop a novel MR-fingerprinting (MRF) pulse sequence that is insensitive to B 1 + and B0 imperfections for simultaneous T1 , T2 , and T1ρ relaxation mapping. METHODS We implemented a totally balanced spin-lock (TB-SL) module to encode T1ρ relaxation into an existing MRF framework that encoded T1 and T2 . The spin-lock module used two 180° pulses with compensatory phases to reduce T1ρ sensitivity to B1 and B0 inhomogeneities. We compared T1ρ measured using TB-SL MRF in Bloch simulations, model agar phantoms, and in vivo experiments to those with a self-compensated spin-lock preparation module (SC-SL). The TB-SL MRF repeatability was evaluated in maps acquired in the lower leg skeletal muscle of 12 diabetic peripheral neuropathy patients, scanned two times each during visits separated by about 30 days. RESULTS The phantom relaxation times measured with TB-SL and SC-SL MRF were in good agreement with reference values in regions with low B1 inhomogeneities. Compared with SC-SL, TB-SL MRF showed in experiments greater robustness against severe B1 inhomogeneities and in Bloch simulations greater robustness against B1 and B0 . We measured with TB-SL MRF an average T1 = 950.1 ± 28.7 ms, T2 = 26.0 ± 1.2 ms, and T1ρ = 31.7 ± 3.2 ms in skeletal muscle across patients. Bland-Altman analysis demonstrated low bias between TB-SL and SC-SL MRF and between TB-SL MRF maps acquired in two visits. The coefficient of variation was less than 3% for all measurements. CONCLUSION The proposed TB-SL MRF sequence is fast and insensitive to B 1 + and B0 imperfections. It can simultaneously map T1 , T2 , T1ρ , and B 1 + in a single scan and can potentially be used to study muscle composition.
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Affiliation(s)
- Azadeh Sharafi
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Katherine Medina
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Marcelo W V Zibetti
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Smita Rao
- Department of Physical Therapy, New York University, New York, New York, USA
| | - Martijn A Cloos
- Center of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ryan Brown
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.,Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA
| | - Ravinder R Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.,Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA
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Hermann I, Martínez-Heras E, Rieger B, Schmidt R, Golla AK, Hong JS, Lee WK, Yu-Te W, Nagtegaal M, Solana E, Llufriu S, Gass A, Schad LR, Weingärtner S, Zöllner FG. Accelerated white matter lesion analysis based on simultaneous T 1 and T 2 ∗ quantification using magnetic resonance fingerprinting and deep learning. Magn Reson Med 2021; 86:471-486. [PMID: 33547656 DOI: 10.1002/mrm.28688] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/27/2020] [Accepted: 12/28/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. METHODS MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of T 1 and T 2 ∗ in 50 subjects with multiple sclerosis (MS) and 10 healthy volunteers along 2 centers. MRF T 1 and T 2 ∗ parametric maps were distortion corrected and denoised. A CNN was trained to reconstruct the T 1 and T 2 ∗ parametric maps, and the WM and GM probability maps. RESULTS Deep learning-based postprocessing reduced reconstruction and image processing times from hours to a few seconds while maintaining high accuracy, reliability, and precision. Mean absolute error performed the best for T 1 (deviations 5.6%) and the logarithmic hyperbolic cosinus loss the best for T 2 ∗ (deviations 6.0%). CONCLUSIONS MRF is a fast and robust tool for quantitative T 1 and T 2 ∗ mapping. Its long reconstruction and several postprocessing steps can be facilitated and accelerated using deep learning.
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Affiliation(s)
- Ingo Hermann
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - Eloy Martínez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Benedikt Rieger
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Ralf Schmidt
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Alena-Kathrin Golla
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jia-Sheng Hong
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Wei-Kai Lee
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Wu Yu-Te
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.,Institute of Biophotonics and Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Martijn Nagtegaal
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Achim Gass
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lothar R Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Sushentsev N, Kaggie JD, Slough RA, Carmo B, Barrett T. Reproducibility of magnetic resonance fingerprinting-based T1 mapping of the healthy prostate at 1.5 and 3.0 T: A proof-of-concept study. PLoS One 2021; 16:e0245970. [PMID: 33513165 PMCID: PMC7846281 DOI: 10.1371/journal.pone.0245970] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 01/11/2021] [Indexed: 11/18/2022] Open
Abstract
Facilitating clinical translation of quantitative imaging techniques has been suggested as means of improving interobserver agreement and diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) of the prostate. One such technique, magnetic resonance fingerprinting (MRF), has significant competitive advantages over conventional mapping techniques in terms of its multi-site reproducibility, short scanning time and inherent robustness to motion. It has also been shown to improve the detection of clinically significant prostate cancer when added to standard mpMRI sequences, however, the existing studies have all been conducted on 3.0 T MRI systems, limiting the technique's use on 1.5 T MRI scanners that are still more widely used for prostate imaging across the globe. The aim of this proof-of-concept study was, therefore, to evaluate the cross-system reproducibility of prostate MRF T1 in healthy volunteers (HVs) using 1.5 and 3.0 T MRI systems. The initial validation of MRF T1 against gold standard inversion recovery fast spin echo (IR-FSE) T1 in the ISMRM/NIST MRI system revealed a strong linear correlation between phantom-derived MRF and IR-FSE T1 values was observed at both field strengths (R2 = 0.998 at 1.5T and R2 = 0.993 at 3T; p = < 0.0001 for both). In young HVs, inter-scanner CVs demonstrated marginal differences across all tissues with the highest difference of 3% observed in fat (2% at 1.5T vs 5% at 3T). At both field strengths, MRF T1 could confidently differentiate prostate peripheral zone from transition zone, which highlights the high quantitative potential of the technique given the known difficulty of tissue differentiation in this age group. The high cross-system reproducibility of MRF T1 relaxometry of the healthy prostate observed in this preliminary study, therefore, supports the technique's prospective clinical validation as part of larger trials employing 1.5 T MRI systems, which are still widely used clinically for routine mpMRI of the prostate.
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Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, United Kingdom
- * E-mail:
| | - Joshua D. Kaggie
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, United Kingdom
| | - Rhys A. Slough
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, United Kingdom
| | - Bruno Carmo
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, United Kingdom
| | - Tristan Barrett
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, United Kingdom
- CamPARI Prostate Cancer Group, Addenbrooke’s Hospital and University of Cambridge, Cambridge, United Kingdom
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Kim JH, Dodd S, Ye FQ, Knutsen AK, Nguyen D, Wu H, Su S, Mastrogiacomo S, Esparza TJ, Swenson RE, Brody DL. Sensitive detection of extremely small iron oxide nanoparticles in living mice using MP2RAGE with advanced image co-registration. Sci Rep 2021; 11:106. [PMID: 33420210 PMCID: PMC7794370 DOI: 10.1038/s41598-020-80181-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 12/15/2020] [Indexed: 02/05/2023] Open
Abstract
Magnetic resonance imaging (MRI) is a widely used non-invasive methodology for both preclinical and clinical studies. However, MRI lacks molecular specificity. Molecular contrast agents for MRI would be highly beneficial for detecting specific pathological lesions and quantitatively evaluating therapeutic efficacy in vivo. In this study, an optimized Magnetization Prepared—RApid Gradient Echo (MP-RAGE) with 2 inversion times called MP2RAGE combined with advanced image co-registration is presented as an effective non-invasive methodology to quantitatively detect T1 MR contrast agents. The optimized MP2RAGE produced high quality in vivo mouse brain T1 (or R1 = 1/T1) map with high spatial resolution, 160 × 160 × 160 µm3 voxel at 9.4 T. Test–retest signal to noise was > 20 for most voxels. Extremely small iron oxide nanoparticles (ESIONPs) having 3 nm core size and 11 nm hydrodynamic radius after polyethylene glycol (PEG) coating were intracranially injected into mouse brain and detected as a proof-of-concept. Two independent MP2RAGE MR scans were performed pre- and post-injection of ESIONPs followed by advanced image co-registration. The comparison of two T1 (or R1) maps after image co-registration provided precise and quantitative assessment of the effects of the injected ESIONPs at each voxel. The proposed MR protocol has potential for future use in the detection of T1 molecular contrast agents.
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Affiliation(s)
- Joong H Kim
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD, USA.,Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Stephen Dodd
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, and National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD, USA
| | - Duong Nguyen
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Haitao Wu
- Chemistry and Synthesis Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shiran Su
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Simone Mastrogiacomo
- Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Thomas J Esparza
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD, USA.,Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Rolf E Swenson
- Chemistry and Synthesis Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - David L Brody
- Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD, USA. .,Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA. .,Department of Neurology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
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Sushentsev N, Kaggie JD, Buonincontri G, Schulte RF, Graves MJ, Gnanapragasam VJ, Barrett T. The effect of gadolinium-based contrast agent administration on magnetic resonance fingerprinting-based T 1 relaxometry in patients with prostate cancer. Sci Rep 2020; 10:20475. [PMID: 33235229 PMCID: PMC7686305 DOI: 10.1038/s41598-020-77331-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/03/2020] [Indexed: 12/16/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a rapidly developing fast quantitative mapping technique able to produce multiple property maps with reduced sensitivity to motion. MRF has shown promise in improving the diagnosis of clinically significant prostate cancer but requires further validation as part of a prostate multiparametric (mp) MRI protocol. mpMRI protocol mandates the inclusion of dynamic contrast enhanced (DCE) imaging, known for its significant T1 shortening effect. MRF could be used to measure both pre- and post-contrast T1 values, but its utility must be assessed. In this proof-of-concept study, we sought to evaluate the variation in MRF T1 measurements post gadolinium-based contrast agent (GBCA) injection and the utility of such T1 measurements to differentiate peripheral and transition zone tumours from normal prostatic tissue. We found that the T1 variation in all tissues increased considerably post-GBCA following the expected significant T1 shortening effect, compromising the ability of MRF T1 to identify transition zone lesions. We, therefore, recommend performing MRF T1 prior to DCE imaging to maintain its benefit for improving detection of both peripheral and transition zone lesions while reducing additional scanning time. Demonstrating the effect of GBCA on MRF T1 relaxometry in patients also paves the way for future clinical studies investigating the added value of post-GBCA MRF in PCa, including its dynamic analysis as in DCE-MRF.
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Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Joshua D Kaggie
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | | | | | - Martin J Graves
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Vincent J Gnanapragasam
- Department of Urology, Addenbrooke's Hospital, Cambridge, UK
- Academic Urology Group, Department of Surgery and Oncology, University of Cambridge, Cambridge, UK
- Cambridge Urology Translational Research and Clinical Trials Office, University of Cambridge, Cambridge, UK
| | - Tristan Barrett
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK.
- CamPARI Prostate Cancer Group, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK.
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42
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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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43
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Lizarbe B, Campillo B, Guadilla I, López-Larrubia P, Cerdán S. Magnetic resonance assessment of the cerebral alterations associated with obesity development. J Cereb Blood Flow Metab 2020; 40:2135-2151. [PMID: 32703110 PMCID: PMC7585928 DOI: 10.1177/0271678x20941263] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 05/28/2020] [Accepted: 06/14/2020] [Indexed: 12/15/2022]
Abstract
Obesity is a current threat to health care systems, affecting approximately 13% of the world's adult population, and over 18% children and adolescents. The rise of obesity is fuelled by inadequate life style habits, as consumption of diets rich in fats and sugars which promote, additionally, the development of associated comorbidities. Obesity results from a neuroendocrine imbalance in the cerebral mechanisms controlling food intake and energy expenditure, including the hypothalamus and the reward and motivational centres. Specifically, high-fat diets are known to trigger an early inflammatory response in the hypothalamus that precedes weight gain, is time-dependent, and eventually extends to the remaining appetite regulating regions in the brain. Multiple magnetic resonance imaging (MRI) and spectroscopy (MRS) methods are currently available to characterize different features of cerebral obesity, including diffusion weighted, T2 and volumetric imaging and 1H and 13C spectroscopic evaluations. In particular, consistent evidences have revealed increased water diffusivity and T2 values, decreased grey matter volumes, and altered metabolic profiles and fluxes, in the brain of animal models and in obese humans. This review provides an integrative interpretation of the physio-pathological processes associated with obesity development in the brain, and the MRI and MRS methods implemented to characterize them.
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Affiliation(s)
- Blanca Lizarbe
- Instituto de Investigaciones Biomédicas “Alberto Sols” CSIC/UAM, Madrid, Spain
| | - Basilio Campillo
- Instituto de Investigaciones Biomédicas “Alberto Sols” CSIC/UAM, Madrid, Spain
| | - Irene Guadilla
- Instituto de Investigaciones Biomédicas “Alberto Sols” CSIC/UAM, Madrid, Spain
| | | | - Sebastián Cerdán
- Instituto de Investigaciones Biomédicas “Alberto Sols” CSIC/UAM, Madrid, Spain
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44
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Fujita S, Buonincontri G, Cencini M, Fukunaga I, Takei N, Schulte RF, Hagiwara A, Uchida W, Hori M, Kamagata K, Abe O, Aoki S. Repeatability and reproducibility of human brain morphometry using three-dimensional magnetic resonance fingerprinting. Hum Brain Mapp 2020; 42:275-285. [PMID: 33089962 PMCID: PMC7775993 DOI: 10.1002/hbm.25232] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/13/2020] [Accepted: 09/29/2020] [Indexed: 12/12/2022] Open
Abstract
Three-dimensional (3D) Magnetic resonance fingerprinting (MRF) permits whole-brain volumetric quantification of T1 and T2 relaxation values, potentially replacing conventional T1-weighted structural imaging for common brain imaging analysis. The aim of this study was to evaluate the repeatability and reproducibility of 3D MRF in evaluating brain cortical thickness and subcortical volumetric analysis in healthy volunteers using conventional 3D T1-weighted images as a reference standard. Scan-rescan tests of both 3D MRF and conventional 3D fast spoiled gradient recalled echo (FSPGR) were performed. For each sequence, the regional cortical thickness and volume of the subcortical structures were measured using standard automatic brain segmentation software. Repeatability and reproducibility were assessed using the within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), and mean percent difference and ICC, respectively. The wCV and ICC of cortical thickness were similar across all regions with both 3D MRF and FSPGR. The percent relative difference in cortical thickness between 3D MRF and FSPGR across all regions was 8.0 ± 3.2%. The wCV and ICC of the volume of subcortical structures across all structures were similar between 3D MRF and FSPGR. The percent relative difference in the volume of subcortical structures between 3D MRF and FSPGR across all structures was 7.1 ± 3.6%. 3D MRF measurements of human brain cortical thickness and subcortical volumes are highly repeatable, and consistent with measurements taken on conventional 3D T1-weighted images. A slight, consistent bias was evident between the two, and thus careful attention is required when combining data from MRF and conventional acquisitions.
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Affiliation(s)
- Shohei Fujita
- Department of Radiology, Juntendo University, Tokyo, Japan.,Department of Radiology, The University of Tokyo, Tokyo, Japan
| | | | - Matteo Cencini
- Imago7 Foundation, Pisa, Italy.,IRCCS Stella Maris, Pisa, Italy
| | - Issei Fukunaga
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Naoyuki Takei
- MR Applications and Workflow, GE Healthcare, Tokyo, Japan
| | | | | | - Wataru Uchida
- Department of Radiology, Juntendo University, Tokyo, Japan.,Department of Radiological Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, Tokyo, Japan
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45
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Lehman VT, Cogswell PM, Rinaldo L, Brinjikji W, Huston J, Klaas JP, Lanzino G. Contemporary and emerging magnetic resonance imaging methods for evaluation of moyamoya disease. Neurosurg Focus 2020; 47:E6. [PMID: 31786551 DOI: 10.3171/2019.9.focus19616] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 09/06/2019] [Indexed: 11/06/2022]
Abstract
Numerous recent technological advances offer the potential to substantially enhance the MRI evaluation of moyamoya disease (MMD). These include high-resolution volumetric imaging, high-resolution vessel wall characterization, improved cerebral angiographic and perfusion techniques, high-field imaging, fast scanning methods, and artificial intelligence. This review discusses the current state-of-the-art MRI applications in these realms, emphasizing key imaging findings, clinical utility, and areas that will benefit from further investigation. Although these techniques may apply to imaging of a wide array of neurovascular or other neurological conditions, consideration of their application to MMD is useful given the comprehensive multidimensional MRI assessment used to evaluate MMD. These MRI techniques span from basic cross-sectional to advanced functional sequences, both qualitative and quantitative.The aim of this review was to provide a comprehensive summary and analysis of current key relevant literature of advanced MRI techniques for the evaluation of MMD with image-rich case examples. These imaging methods can aid clinical characterization, help direct treatment, assist in the evaluation of treatment response, and potentially improve the understanding of the pathophysiology of MMD.
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Affiliation(s)
| | | | | | | | | | - James P Klaas
- 3Neurology, Mayo Clinic College of Graduate Medical Education, Rochester, Minnesota
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46
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Cheng F, Chen Y, Zong X, Lin W, Shen D, Yap PT. Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2020; 12262:158-166. [PMID: 38504822 PMCID: PMC10950303 DOI: 10.1007/978-3-030-59713-9_16] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Magnetic resonance fingerprinting (MRF) is a novel imaging framework for fast and simultaneous quantification of multiple tissue properties. Recently, 3D MRF methods have been developed, but the acquisition speed needs to be improved before they can be adopted for clinical use. The purpose of this study is to develop a novel deep learning approach to accelerate 3D MRF acquisition along the slice-encoding direction in k-space. We introduce a graph-based convolutional neural network that caters to non-Cartesian spiral trajectories commonly used for MRF acquisition. We improve tissue quantification accuracy compared with the state of the art. Our method enables fast 3D MRF with high spatial resolution, allowing whole-brain coverage within 5min, making MRF more feasible in clinical settings.
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Affiliation(s)
- Feng Cheng
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Yong Chen
- Case Western Reserve University, Cleveland, OH, USA
| | - Xiaopeng Zong
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
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47
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Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence. Invest Radiol 2020; 55:601-616. [PMID: 32209816 PMCID: PMC7413678 DOI: 10.1097/rli.0000000000000666] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.
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Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| | | | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
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48
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Sodium relaxometry using
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Na MR fingerprinting: A proof of concept. Magn Reson Med 2020; 84:2577-2591. [DOI: 10.1002/mrm.28316] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 04/01/2020] [Accepted: 04/20/2020] [Indexed: 02/06/2023]
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49
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Assländer J. A Perspective on MR Fingerprinting. J Magn Reson Imaging 2020; 53:676-685. [PMID: 32286717 DOI: 10.1002/jmri.27134] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 02/28/2020] [Accepted: 02/28/2020] [Indexed: 12/18/2022] Open
Abstract
This article reviews the basic concept of MR fingerprinting (MRF) with the goal of highlighting MRF's key contributions, putting them in the context of other quantitative MRI literature, and refining MRF's terminology. The article discusses the robustness and flexibility of MRF's signature dictionary-matching reconstruction along with more advanced MRF reconstructions. A key feature of MRF is the lack of assumptions about the signal evolution, which gives scientists the flexibility to tailor sequences for their needs. The article argues that the concept of unique fingerprints does not capture the requirements for successful parameter mapping and that an analysis of the signal's derivatives with respect to biophysical parameters, such as relaxation times, is more informative, as it allows one to evaluate the efficiency of a pulse sequence. The article points at the source of MRF's efficiency, namely, flip angle variations at the time scale of the relaxation times, and reveals that MRF's advantages are strongest at long scan times, as required for 3D imaging. Further, it outlines how MRF's flexibility can be used to design mutually tailored pulse sequences and biophysical models with the goal of improving the reproducibility of parameter mapping biological tissue. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Jakob Assländer
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), Dept. of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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50
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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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