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Rashid I, Lima da Cruz G, Seiberlich N, Hamilton JI. Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications. J Magn Reson Imaging 2023:10.1002/jmri.29206. [PMID: 38153855 PMCID: PMC11211246 DOI: 10.1002/jmri.29206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 12/30/2023] Open
Abstract
Cardiovascular magnetic resonance (CMR) is an established imaging modality with proven utility in assessing cardiovascular diseases. The ability of CMR to characterize myocardial tissue using T1 - and T2 -weighted imaging, parametric mapping, and late gadolinium enhancement has allowed for the non-invasive identification of specific pathologies not previously possible with modalities like echocardiography. However, CMR examinations are lengthy and technically complex, requiring multiple pulse sequences and different anatomical planes to comprehensively assess myocardial structure, function, and tissue composition. To increase the overall impact of this modality, there is a need to simplify and shorten CMR exams to improve access and efficiency, while also providing reproducible quantitative measurements. Multiparametric MRI techniques that measure multiple tissue properties offer one potential solution to this problem. This review provides an in-depth look at one such multiparametric approach, cardiac magnetic resonance fingerprinting (MRF). The article is structured as follows. First, a brief review of single-parametric and (non-Fingerprinting) multiparametric CMR mapping techniques is presented. Second, a general overview of cardiac MRF is provided covering pulse sequence implementation, dictionary generation, fast k-space sampling methods, and pattern recognition. Third, recent technical advances in cardiac MRF are covered spanning a variety of topics, including simultaneous multislice and 3D sampling, motion correction algorithms, cine MRF, synthetic multicontrast imaging, extensions to measure additional clinically important tissue properties (proton density fat fraction, T2 *, and T1ρ ), and deep learning methods for image reconstruction and parameter estimation. The last section will discuss potential clinical applications, concluding with a perspective on how multiparametric techniques like MRF may enable streamlined CMR protocols. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Imran Rashid
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Gastao Lima da Cruz
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, USA
| | - Nicole Seiberlich
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, USA
| | - Jesse I. Hamilton
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, USA
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Nagtegaal M, Hartsema E, Koolstra K, Vos F. Multicomponent MR fingerprinting reconstruction using joint-sparsity and low-rank constraints. Magn Reson Med 2023; 89:286-298. [PMID: 36121015 PMCID: PMC9825911 DOI: 10.1002/mrm.29442] [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: 04/04/2022] [Revised: 08/12/2022] [Accepted: 08/12/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE To develop an efficient algorithm for multicomponent MR fingerprinting (MC-MRF) reconstructions directly from highly undersampled data without making prior assumptions about tissue relaxation times and expected number of tissues. METHODS The proposed method reconstructs MC-MRF maps from highly undersampled data by iteratively applying a joint-sparsity constraint to the estimated tissue components. Intermediate component maps are obtained by a low-rank multicomponent alternating direction method of multipliers (MC-ADMM) including the non-negativity of tissue weights as an extra regularization term. Over iterations, the used dictionary compression is adjusted. The proposed method (k-SPIJN) is compared with a two-step approach in which image reconstruction and multicomponent estimations are performed sequentially and tested in numerical simulations and in vivo by applying different undersampling factors in eight healthy volunteers. In the latter case, fully sampled data serves as the reference. RESULTS The proposed method shows improved precision and accuracy in simulations compared with a state-of-art sequential approach. Obtained in vivo magnetization fraction maps for different tissue types show reduced systematic errors and reduced noise-like effects. Root mean square errors in estimated magnetization fraction maps significantly reduce from 13.0% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 5.8% with the conventional, two-step approach to 9.6% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 3.9% and 9.6% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 3.2% with the proposed MC-ADMM and k-SPIJN methods, respectively. Mean standard deviation in homogeneous white matter regions reduced significantly from 8.6% to 2.9% (two step vs. k-SPIJN). CONCLUSION The proposed MC-ADMM and k-SPIJN reconstruction methods estimate MC-MRF maps from highly undersampled data resulting in improved image quality compared with the existing method.
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Affiliation(s)
- Martijn Nagtegaal
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Emiel Hartsema
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Kirsten Koolstra
- Division of Image Processing, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Frans Vos
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands,Department of RadiologyErasmus MCRotterdamThe Netherlands
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Velasco C, Fletcher TJ, Botnar RM, Prieto C. Artificial intelligence in cardiac magnetic resonance fingerprinting. Front Cardiovasc Med 2022; 9:1009131. [PMID: 36204566 PMCID: PMC9530662 DOI: 10.3389/fcvm.2022.1009131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.
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Affiliation(s)
- Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- *Correspondence: Carlos Velasco
| | - Thomas J. Fletcher
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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4
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Hamilton JI. A Self-Supervised Deep Learning Reconstruction for Shortening the Breathhold and Acquisition Window in Cardiac Magnetic Resonance Fingerprinting. Front Cardiovasc Med 2022; 9:928546. [PMID: 35811730 PMCID: PMC9260051 DOI: 10.3389/fcvm.2022.928546] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/06/2022] [Indexed: 01/14/2023] Open
Abstract
The aim of this study is to shorten the breathhold and diastolic acquisition window in cardiac magnetic resonance fingerprinting (MRF) for simultaneous T1, T2, and proton spin density (M0) mapping to improve scan efficiency and reduce motion artifacts. To this end, a novel reconstruction was developed that combines low-rank subspace modeling with a deep image prior, termed DIP-MRF. A system of neural networks is used to generate spatial basis images and quantitative tissue property maps, with training performed using only the undersampled k-space measurements from the current scan. This approach avoids difficulties with obtaining in vivo MRF training data, as training is performed de novo for each acquisition. Calculation of the forward model during training is accelerated by using GRAPPA operator gridding to shift spiral k-space data to Cartesian grid points, and by using a neural network to rapidly generate fingerprints in place of a Bloch equation simulation. DIP-MRF was evaluated in simulations and at 1.5 T in a standardized phantom, 18 healthy subjects, and 10 patients with suspected cardiomyopathy. In addition to conventional mapping, two cardiac MRF sequences were acquired, one with a 15-heartbeat(HB) breathhold and 254 ms acquisition window, and one with a 5HB breathhold and 150 ms acquisition window. In simulations, DIP-MRF yielded decreased nRMSE compared to dictionary matching and a sparse and locally low rank (SLLR-MRF) reconstruction. Strong correlation (R2 > 0.999) with T1 and T2 reference values was observed in the phantom using the 5HB/150 ms scan with DIP-MRF. DIP-MRF provided better suppression of noise and aliasing artifacts in vivo, especially for the 5HB/150 ms scan, and lower intersubject and intrasubject variability compared to dictionary matching and SLLR-MRF. Furthermore, it yielded a better agreement between myocardial T1 and T2 from 15HB/254 ms and 5HB/150 ms MRF scans, with a bias of −9 ms for T1 and 2 ms for T2. In summary, this study introduces an extension of the deep image prior framework for cardiac MRF tissue property mapping, which does not require pre-training with in vivo scans, and has the potential to reduce motion artifacts by enabling a shortened breathhold and acquisition window.
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Affiliation(s)
- Jesse I. Hamilton
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- *Correspondence: Jesse I. Hamilton,
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5
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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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6
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Koolstra K, Börnert P, Lelieveldt BPF, Webb A, Dzyubachyk O. Stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 35:223-234. [PMID: 34687369 PMCID: PMC8995272 DOI: 10.1007/s10334-021-00963-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 09/08/2021] [Accepted: 09/23/2021] [Indexed: 11/28/2022]
Abstract
Objective To visualize the encoding capability of magnetic resonance fingerprinting (MRF) dictionaries. Materials and methods High-dimensional MRF dictionaries were simulated and embedded into a lower-dimensional space using t-distributed stochastic neighbor embedding (t-SNE). The embeddings were visualized via colors as a surrogate for location in low-dimensional space. First, we illustrate this technique on three different MRF sequences. We then compare the resulting embeddings and the color-coded dictionary maps to these obtained with a singular value decomposition (SVD) dimensionality reduction technique. We validate the t-SNE approach with measures based on existing quantitative measures of encoding capability using the Euclidean distance. Finally, we use t-SNE to visualize MRF sequences resulting from an MRF sequence optimization algorithm. Results t-SNE was able to show clear differences between the color-coded dictionary maps of three MRF sequences. SVD showed smaller differences between different sequences. These findings were confirmed by quantitative measures of encoding. t-SNE was also able to visualize differences in encoding capability between subsequent iterations of an MRF sequence optimization algorithm. Discussion This visualization approach enables comparison of the encoding capability of different MRF sequences. This technique can be used as a confirmation tool in MRF sequence optimization. Supplementary Information The online version contains supplementary material available at 10.1007/s10334-021-00963-8.
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Affiliation(s)
- Kirsten Koolstra
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
| | - Peter Börnert
- C. J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.,Philips Research Hamburg, Röntgenstrasse 24, 22335, Hamburg, Germany
| | - Boudewijn P F Lelieveldt
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.,Intelligent Systems Department, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands
| | - Andrew Webb
- C. J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Oleh Dzyubachyk
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.,Electron Microscopy Facility, Department of Cell and Chemical Biology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
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7
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Soyak R, Navruz E, Ersoy EO, Cruz G, Prieto C, King AP, Unay D, Oksuz I. Channel Attention Networks for Robust MR Fingerprint Matching. IEEE Trans Biomed Eng 2021; 69:1398-1405. [PMID: 34591755 DOI: 10.1109/tbme.2021.3116877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varying acquisition parameters pseudo-randomly, so that each tissue generates its unique signal evolution during scanning. Even though MRF provides faster scanning, it has disadvantages such as erroneous and slow generation of the corresponding parametric maps, which needs to be improved. Moreover, there is a need for explainable architectures for understanding the guiding signals to generate accurate parametric maps. In this paper, we addressed both of these shortcomings by proposing a novel neural network architecture (CONV-ICA) consisting of a channel-wise attention module and a fully convolutional network. The proposed approach, evaluated over 3 simulated MRF signals, reduces error in the reconstruction of tissue parameters by 8.88% for T1 and 75.44% for T2 with respect to state-of-the-art methods. Another contribution of this study is a new channel selection method: attention-based channel selection. Furthermore, the effect of patch size and temporal frames of MRF signal on channel reduction are analyzed by employing a channel-wise attention.
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8
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A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations. Phys Med 2021; 89:80-92. [PMID: 34352679 DOI: 10.1016/j.ejmp.2021.07.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 07/12/2021] [Accepted: 07/13/2021] [Indexed: 11/22/2022] Open
Abstract
MR fingerprinting (MRF) is an innovative approach to quantitative MRI. A typical disadvantage of dictionary-based MRF is the explosive growth of the dictionary as a function of the number of reconstructed parameters, an instance of the curse of dimensionality, which determines an explosion of resource requirements. In this work, we describe a deep learning approach for MRF parameter map reconstruction using a fully connected architecture. Employing simulations, we have investigated how the performance of the Neural Networks (NN) approach scales with the number of parameters to be retrieved, compared to the standard dictionary approach. We have also studied optimal training procedures by comparing different strategies for noise addition and parameter space sampling, to achieve better accuracy and robustness to noise. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1, and IR-bSSFP-B1. A comparison between NN and the dictionary approaches in reconstructing parameter maps as a function of the number of parameters to be retrieved was performed using a numerical brain phantom. Results demonstrated that training with random sampling and different levels of noise variance yielded the best performance. NN performance was at least as good as the dictionary-based approach in reconstructing parameter maps using Gaussian noise as a source of artifacts: the difference in performance increased with the number of estimated parameters because the dictionary method suffers from the coarse resolution of the parameter space sampling. The NN proved to be more efficient in memory usage and computational burden, and has great potential for solving large-scale MRF problems.
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9
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Qi H, Cruz G, Botnar R, Prieto C. Synergistic multi-contrast cardiac magnetic resonance image reconstruction. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200197. [PMID: 33966456 DOI: 10.1098/rsta.2020.0197] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
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Affiliation(s)
- Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
| | - Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
| | - René Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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10
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Arberet S, Chen X, Mailhé B, Speier P, Körzdörfer G, Nittka M, Meyer H, Nadar MS. A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting. Magn Reson Imaging 2021; 82:74-90. [PMID: 34157408 DOI: 10.1016/j.mri.2021.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 05/28/2021] [Accepted: 06/16/2021] [Indexed: 11/18/2022]
Abstract
Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction. The reason is that it is indeed challenging to incorporate effectively multiple regularizations in a single MRF optimization algorithm. As a result most of these methods are not robust to noise especially when the sequence length is short. In this paper, we propose a family of new methods where spatial and low-rank regularizations, in addition to the Bloch manifold regularization, are applied on the images. We show on digital phantom and NIST phantom scans, as well as volunteer scans that the proposed methods bring significant improvement in the quality of the estimated tissue maps.
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Affiliation(s)
- Simon Arberet
- Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA.
| | - Xiao Chen
- Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Boris Mailhé
- Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Peter Speier
- Magnetic Resonance, Siemens Healthineers, Erlangen, Germany
| | | | - Mathias Nittka
- Magnetic Resonance, Siemens Healthineers, Erlangen, Germany
| | - Heiko Meyer
- Magnetic Resonance, Siemens Healthineers, Erlangen, Germany
| | - Mariappan S Nadar
- Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA
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11
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Tippareddy C, Zhao W, Sunshine JL, Griswold M, Ma D, Badve C. Magnetic resonance fingerprinting: an overview. Eur J Nucl Med Mol Imaging 2021; 48:4189-4200. [PMID: 34037831 DOI: 10.1007/s00259-021-05384-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 04/25/2021] [Indexed: 12/17/2022]
Abstract
Magnetic resonance fingerprinting (MRF) is an evolving quantitative MRI framework consisting of unique data acquisition, processing, visualization, and interpretation steps. MRF is capable of simultaneously producing multiple high-resolution property maps including T1, T2, M0, ADC, and T2* measurements. While a relatively new technology, MRF has undergone rapid development for a variety of clinical applications from brain tumor characterization and epilepsy imaging to characterization of prostate cancer, cardiac imaging, among others. This paper will provide a brief overview of current state of MRF technology including highlights of technical and clinical advances. We will conclude with a brief discussion of the challenges that need to be overcome to establish MRF as a quantitative imaging biomarker.
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Affiliation(s)
- Charit Tippareddy
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Walter Zhao
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Jeffrey L Sunshine
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Mark Griswold
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA.,Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA.
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12
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Kiselev VG, Körzdörfer G, Gall P. Toward Quantification: Microstructure and Magnetic Resonance Fingerprinting. Invest Radiol 2021; 56:1-9. [PMID: 33186141 DOI: 10.1097/rli.0000000000000738] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Quantitative magnetic resonance imaging (MRI) is a long-standing challenge. We advocate that the origin of the problem is the simplification applied in commonly used models of the MRI signal relation to the target parameters of biological tissues. Two research fields are briefly reviewed as ways to respond to the challenge of quantitative MRI, both experiencing an exponential growth right now. Microstructure MRI strives to build physiology-based models from cells to signal and, given the signal, back to the cells again. Magnetic resonance fingerprinting aims at efficient simultaneous determination of multiple signal parameters. The synergy of these yet disjoined approaches promises truly quantitative MRI with specific target-oriented diagnostic tools rather than universal imaging methods.
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Affiliation(s)
- Valerij G Kiselev
- From the Medical Physics, Department of Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg
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Ropella-Panagis K, Seiberlich N. Magnetic Resonance Fingerprinting: Basic Concepts and Applications in Molecular Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00067-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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14
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Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks. Med Image Anal 2020; 69:101945. [PMID: 33421921 DOI: 10.1016/j.media.2020.101945] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 11/09/2020] [Accepted: 12/15/2020] [Indexed: 12/12/2022]
Abstract
We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training samples. We propose a deep and compact encoder-decoder network with residual blocks in order to embed Bloch manifold projections through multi-scale piecewise affine approximations, and to replace the non-scalable dictionary-matching baseline. Tested on a number of datasets we demonstrate effectiveness of the proposed scheme for recovering accurate and consistent quantitative information from novel and aggressively subsampled 2D/3D quantitative MRI acquisition protocols.
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Acceleration of 2D-MR fingerprinting by reducing the number of echoes with increased in-plane resolution: a volunteer study. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 33:783-791. [PMID: 32248322 PMCID: PMC7669790 DOI: 10.1007/s10334-020-00842-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 03/22/2020] [Accepted: 03/24/2020] [Indexed: 12/03/2022]
Abstract
Objective To compare the absolute values and repeatability of magnetic resonance fingerprinting (MRF) with 3000 and 1500 echoes/slice acquired in 41 s and 20 s (MRF3k and MRF1.5k, respectively). Materials and methods MRF3k and MRF1.5k scans based on fast imaging with steady precession (FISP) were conducted using a 3 T scanner. Inter-scan agreement and intra-scan repeatability were investigated in 41 and 28 subjects, respectively. Region-of-interest (ROI) analysis was conducted on T1 values of MRF3k by two raters, and their agreement was evaluated using intraclass correlation coefficients (ICCs). Between MRF3k and MRF1.5k, differences in T1 and T2 values and inter-measurement correlation coefficients (CCs) were investigated. Intra-measurement repeatability was evaluated using coefficients of variation (CVs). A p value < 0.05 was considered statistically significant. Results The ICCs of ROI measurements were 0.77–0.96. Differences were observed between the two MRF scans, but the CCs of the overall ROIs were 0.99 and 0.97 for the T1 and T2 values, respectively. The mean and median CVs of repeatability were equal to or less than 1.58% and 3.13% in each of the ROIs for T1 and T2, respectively; there were some significant differences between MRF3k and MRF1.5k, but they were small, measuring less than 1%. Discussion Both MRF3k and MRF1.5k had high repeatability, and a strong to very strong correlation was observed, with a trend toward slightly higher values in MRF1.5k. Electronic supplementary material The online version of this article (10.1007/s10334-020-00842-8) contains supplementary material, which is available to authorized users.
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Roeloffs V, Uecker M, Frahm J. Joint T1 and T2 Mapping With Tiny Dictionaries and Subspace-Constrained Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1008-1014. [PMID: 31484113 DOI: 10.1109/tmi.2019.2939130] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
A novel method is developed that adaptively generates tiny dictionaries for joint T1-T2 mapping in magnetic resonance imaging. This work breaks the bond between dictionary size and representation accuracy (i) by approximating the Bloch-response manifold by piece-wise linear functions and (ii) by adaptively refining the sampling grid depending on the locally-linear approximation error. Data acquisition is accomplished with use of an 2D radially sampled Inversion-Recovery Hybrid-State Free Precession sequence. Adaptive dictionaries are generated with different error tolerances and compared to a heuristically designed dictionary. Based on simulation results, tiny dictionaries were used for T1-T2 mapping in phantom and in vivo studies. Reconstruction and parameter mapping were performed entirely in subspace. All experiments demonstrated excellent agreement between the proposed mapping technique and template matching using heuristic dictionaries. Adaptive dictionaries in combination with manifold projection allow to reduce the necessary dictionary sizes by one to two orders of magnitude.
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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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18
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Hamilton JI, Seiberlich N. Machine Learning for Rapid Magnetic Resonance Fingerprinting Tissue Property Quantification. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:69-85. [PMID: 33132408 PMCID: PMC7595247 DOI: 10.1109/jproc.2019.2936998] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Magnetic Resonance Fingerprinting (MRF) is an MRI-based method that can provide quantitative maps of multiple tissue properties simultaneously from a single rapid acquisition. Tissue property maps are generated by matching the complex signal evolutions collected at the scanner to a dictionary of signals derived using Bloch equation simulations. However, in some circumstances, the process of dictionary generation and signal matching can be time-consuming, reducing the utility of this technique. Recently, several groups have proposed using machine learning to accelerate the extraction of quantitative maps from MRF data. This article will provide an overview of current research that combines MRF and machine learning, as well as present original research demonstrating how machine learning can speed up dictionary generation for cardiac MRF.
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Affiliation(s)
- Jesse I Hamilton
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106 USA, and the Department of Radiology, University of Michigan, Ann Arbor, MI 48109
| | - Nicole Seiberlich
- Department of Biomedical Engineering and the Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106 USA, the Department of Radiology and Cardiology, University Hospitals, Cleveland, OH 44106 USA, and the Department of Radiology, University of Michigan, Ann Arbor, MI 48109
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Fang Z, Chen Y, Hung SC, Zhang X, Lin W, Shen D. Submillimeter MR fingerprinting using deep learning-based tissue quantification. Magn Reson Med 2019; 84:579-591. [PMID: 31854461 DOI: 10.1002/mrm.28136] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 11/06/2019] [Accepted: 11/27/2019] [Indexed: 11/09/2022]
Abstract
PURPOSE To develop a rapid 2D MR fingerprinting technique with a submillimeter in-plane resolution using a deep learning-based tissue quantification approach. METHODS A rapid and high-resolution MR fingerprinting technique was developed for brain T1 and T2 quantification. The 2D acquisition was performed using a FISP-based MR fingerprinting sequence and a spiral trajectory with 0.8-mm in-plane resolution. A deep learning-based method was used to replace the standard template matching method for improved tissue characterization. A novel network architecture (i.e., residual channel attention U-Net) was proposed to improve high-resolution details in the estimated tissue maps. Quantitative brain imaging was performed with 5 adults and 2 pediatric subjects, and the performance of the proposed approach was compared with several existing methods in the literature. RESULTS In vivo measurements with both adult and pediatric subjects show that high-quality T1 and T2 mapping with 0.8-mm in-plane resolution can be achieved in 7.5 seconds per slice. The proposed deep learning method outperformed existing algorithms in tissue quantification with improved accuracy. Compared with the standard U-Net, high-resolution details in brain tissues were better preserved by the proposed residual channel attention U-Net. Experiments on pediatric subjects further demonstrated the potential of the proposed technique for fast pediatric neuroimaging. Alongside reduced data acquisition time, a 5-fold acceleration in tissue property mapping was also achieved with the proposed method. CONCLUSION A rapid and high-resolution MR fingerprinting technique was developed, which enables high-quality T1 and T2 quantification with 0.8-mm in-plane resolution in 7.5 seconds per slice.
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Affiliation(s)
- Zhenghan Fang
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Yong Chen
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sheng-Che Hung
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Xiaoxia Zhang
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Weili Lin
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Dinggang Shen
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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20
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Benjamin AJV, Gómez PA, Golbabaee M, Mahbub ZB, Sprenger T, Menzel MI, Davies M, Marshall I. Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting. Magn Reson Imaging 2019; 61:20-32. [DOI: 10.1016/j.mri.2019.04.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 04/19/2019] [Accepted: 04/29/2019] [Indexed: 01/08/2023]
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Perlman O, Herz K, Zaiss M, Cohen O, Rosen MS, Farrar CT. CEST MR-Fingerprinting: Practical considerations and insights for acquisition schedule design and improved reconstruction. Magn Reson Med 2019; 83:462-478. [PMID: 31400034 DOI: 10.1002/mrm.27937] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/19/2019] [Accepted: 07/17/2019] [Indexed: 01/13/2023]
Abstract
PURPOSE To understand the influence of various acquisition parameters on the ability of CEST MR-Fingerprinting (MRF) to discriminate different chemical exchange parameters and to provide tools for optimal acquisition schedule design and parameter map reconstruction. METHODS Numerical simulations were conducted using a parallel computing implementation of the Bloch-McConnell equations, examining the effect of TR, TE, flip-angle, water T 1 and T 2 , saturation-pulse duration, power, and frequency on the discrimination ability of CEST-MRF. A modified Euclidean distance matching metric was evaluated and compared to traditional dot product matching. L-Arginine phantoms of various concentrations and pH were scanned at 4.7T and the results compared to numerical findings. RESULTS Simulations for dot product matching demonstrated that the optimal flip-angle and saturation times are 30 ∘ and 1100 ms, respectively. The optimal maximal saturation power was 3.4 μT for concentrated solutes with a slow exchange rate, and 5.2 μT for dilute solutes with medium-to-fast exchange rates. Using the Euclidean distance matching metric, much lower maximum saturation powers were required (1.6 and 2.4 μT, respectively), with a slightly longer saturation time (1500 ms) and 90 ∘ flip-angle. For both matching metrics, the discrimination ability increased with the repetition time. The experimental results were in agreement with simulations, demonstrating that more than a 50% reduction in scan-time can be achieved by Euclidean distance-based matching. CONCLUSIONS Optimization of the CEST-MRF acquisition schedule is critical for obtaining the best exchange parameter accuracy. The use of Euclidean distance-based matching of signal trajectories simultaneously improved the discrimination ability and reduced the scan time and maximal saturation power required.
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Affiliation(s)
- Or Perlman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
| | - Kai Herz
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany
| | - Moritz Zaiss
- Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Ouri Cohen
- Memorial Sloan Kettering Cancer Center, New York, New York
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts.,Department of Physics, Harvard University, Cambridge, Massachusetts
| | - Christian T Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
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22
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Magnetic Resonance Fingerprinting Using a Fast Dictionary Searching Algorithm: MRF-ZOOM. IEEE Trans Biomed Eng 2019; 66:1526-1535. [DOI: 10.1109/tbme.2018.2874992] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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23
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Bustin A, Lima da Cruz G, Jaubert O, Lopez K, Botnar RM, Prieto C. High-dimensionality undersampled patch-based reconstruction (HD-PROST) for accelerated multi-contrast MRI. Magn Reson Med 2019; 81:3705-3719. [PMID: 30834594 PMCID: PMC6646908 DOI: 10.1002/mrm.27694] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 01/23/2019] [Accepted: 01/23/2019] [Indexed: 12/14/2022]
Abstract
PURPOSE To develop a new high-dimensionality undersampled patch-based reconstruction (HD-PROST) for highly accelerated 2D and 3D multi-contrast MRI. METHODS HD-PROST jointly reconstructs multi-contrast MR images by exploiting the highly redundant information, on a local and non-local scale, and the strong correlation shared between the multiple contrast images. This is achieved by enforcing multi-dimensional low-rank in the undersampled images. 2D magnetic resonance fingerprinting (MRF) phantom and in vivo brain acquisitions were performed to evaluate the performance of HD-PROST for highly accelerated simultaneous T1 and T2 mapping. Additional in vivo experiments for reconstructing multiple undersampled 3D magnetization transfer (MT)-weighted images were conducted to illustrate the impact of HD-PROST for high-resolution multi-contrast 3D imaging. RESULTS In the 2D MRF phantom study, HD-PROST provided accurate and precise estimation of the T1 and T2 values in comparison to gold standard spin echo acquisitions. HD-PROST achieved good quality maps for the in vivo 2D MRF experiments in comparison to conventional low-rank inversion reconstruction. T1 and T2 values of white matter and gray matter were in good agreement with those reported in the literature for MRF acquisitions with reduced number of time point images (500 time point images, ~2.5 s scan time). For in vivo MT-weighted 3D acquisitions (6 different contrasts), HD-PROST achieved similar image quality than the fully sampled reference image for an undersampling factor of 6.5-fold. CONCLUSION HD-PROST enables multi-contrast 2D and 3D MR images in a short acquisition time without compromising image quality. Ultimately, this technique may increase the potential of conventional parameter mapping.
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Affiliation(s)
- Aurélien Bustin
- Department of Biomedical Engineering, School of Imaging Sciences & Biomedical EngineeringKing’s College London, King’s Health PartnersLondonUnited Kingdom
| | - Gastão Lima da Cruz
- Department of Biomedical Engineering, School of Imaging Sciences & Biomedical EngineeringKing’s College London, King’s Health PartnersLondonUnited Kingdom
| | - Olivier Jaubert
- Department of Biomedical Engineering, School of Imaging Sciences & Biomedical EngineeringKing’s College London, King’s Health PartnersLondonUnited Kingdom
| | - Karina Lopez
- Department of Biomedical Engineering, School of Imaging Sciences & Biomedical EngineeringKing’s College London, King’s Health PartnersLondonUnited Kingdom
| | - René M. Botnar
- Department of Biomedical Engineering, School of Imaging Sciences & Biomedical EngineeringKing’s College London, King’s Health PartnersLondonUnited Kingdom
- Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Imaging Sciences & Biomedical EngineeringKing’s College London, King’s Health PartnersLondonUnited Kingdom
- Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile
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Ropella-Panagis KM, Seiberlich N, Gulani V. Magnetic Resonance Fingerprinting: Implications and Opportunities for PET/MR. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019; 3:388-399. [PMID: 32864537 DOI: 10.1109/trpms.2019.2897425] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Magnetic Resonance Imaging (MRI) can be used to assess anatomical structure, and its sensitivity to a variety of tissue properties enables superb contrast between tissues as well as the ability to characterize these tissues. However, despite vast potential for quantitative and functional evaluation, MRI is typically used qualitatively, in which the underlying tissue properties are not measured, and thus the brightness of each pixel is not quantitatively meaningful. Positron Emission Tomography (PET) is an inherently quantitative imaging modality that interrogates functional activity within a tissue, probed by a molecule of interest coupled with an appropriate tracer. These modalities can complement one another to provide clinical information regarding both structure and function, but there are still technical and practical hurdles in the way of the integrated use of both modalities. Recent advances in MRI have moved the field in an increasingly quantitative direction, which is complementary to PET, and could also potentially help solve some of the challenges in PET/MR. Magnetic Resonance Fingerprinting (MRF) is a recently described MRI-based technique which can efficiently and simultaneously quantitatively map several tissue properties in a single exam. Here, the basic principles behind the quantitative approach of MRF are laid out, and the potential implications for combined PET/MR are discussed.
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Affiliation(s)
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106 USA
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25
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Heo HY, Han Z, Jiang S, Schär M, van Zijl PCM, Zhou J. Quantifying amide proton exchange rate and concentration in chemical exchange saturation transfer imaging of the human brain. Neuroimage 2019; 189:202-213. [PMID: 30654175 DOI: 10.1016/j.neuroimage.2019.01.034] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Revised: 01/09/2019] [Accepted: 01/13/2019] [Indexed: 12/31/2022] Open
Abstract
Current chemical exchange saturation transfer (CEST) neuroimaging protocols typically acquire CEST-weighted images, and, as such, do not essentially provide quantitative proton-specific exchange rates (or brain pH) and concentrations. We developed a dictionary-free MR fingerprinting (MRF) technique to allow CEST parameter quantification with a reduced data set. This was accomplished by subgrouping proton exchange models (SPEM), taking amide proton transfer (APT) as an example, into two-pool (water and semisolid macromolecules) and three-pool (water, semisolid macromolecules, and amide protons) models. A variable radiofrequency saturation scheme was used to generate unique signal evolutions for different tissues, reflecting their CEST parameters. The proposed MRF-SPEM method was validated using Bloch-McConnell equation-based digital phantoms with known ground-truth, which showed that MRF-SPEM can achieve a high degree of accuracy and precision for absolute CEST parameter quantification and CEST phantoms. For in-vivo studies at 3 T, using the same model as in the simulations, synthetic Z-spectra were generated using rates and concentrations estimated from the MRF-SPEM reconstruction and compared with experimentally measured Z-spectra as the standard for optimization. The MRF-SPEM technique can provide rapid and quantitative human brain CEST mapping.
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Affiliation(s)
- Hye-Young Heo
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Zheng Han
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Shanshan Jiang
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Michael Schär
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Peter C M van Zijl
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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Liu Y, Hamilton J, Rajagopalan S, Seiberlich N. Cardiac Magnetic Resonance Fingerprinting: Technical Overview and Initial Results. JACC Cardiovasc Imaging 2018; 11:1837-1853. [PMID: 30522686 PMCID: PMC6394856 DOI: 10.1016/j.jcmg.2018.08.028] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 08/29/2018] [Accepted: 08/30/2018] [Indexed: 01/03/2023]
Abstract
Cardiovascular magnetic resonance is a versatile tool that enables noninvasive characterization of cardiac tissue structure and function. Parametric mapping techniques have allowed unparalleled differentiation of pathophysiological differences in the myocardium such as the delineation of myocardial fibrosis, hemorrhage, and edema. These methods are increasingly used as part of a tool kit to characterize disease states such as cardiomyopathies and coronary artery disease more accurately. Currently conventional mapping techniques require separate acquisitions for T1 and T2 mapping, the values of which may depend on specifics of the magnetic resonance imaging system hardware, pulse sequence implementation, and physiological variables including blood pressure and heart rate. The cardiac magnetic resonance fingerprinting (cMRF) technique has recently been introduced for simultaneous and reproducible measurement of T1 and T2 maps in a single scan. The potential for this technique to provide consistent tissue property values independent of variables including scanner, pulse sequence, and physiology could allow an unbiased framework for the assessment of intrinsic properties of cardiac tissue including structure, perfusion, and parameters such as extracellular volume without the administration of exogenous contrast agents. This review seeks to introduce the basics of the cMRF technique, including pulse sequence design, dictionary generation, and pattern matching. The potential applications of cMRF in assessing diseases such as nonischemic cardiomyopathy are also briefly discussed, and ongoing areas of research are described.
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Affiliation(s)
- Yuchi Liu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jesse Hamilton
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Sanjay Rajagopalan
- Department of Cardiovascular Medicine, University Hospitals, Harrington Heart and Vascular Institute, Cleveland Medical Center and Case Western Reserve School of Medicine, Cleveland, Ohio
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio; Department of Cardiovascular Medicine, University Hospitals, Harrington Heart and Vascular Institute, Cleveland Medical Center and Case Western Reserve School of Medicine, Cleveland, Ohio; Department of Radiology, Case Western Reserve University, Cleveland, Ohio.
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27
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Wang CY, Coppo S, Mehta BB, Seiberlich N, Yu X, Griswold MA. Magnetic resonance fingerprinting with quadratic RF phase for measurement of T 2 * simultaneously with δ f , T 1 , and T 2. Magn Reson Med 2018; 81:1849-1862. [PMID: 30499221 DOI: 10.1002/mrm.27543] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 08/29/2018] [Accepted: 08/30/2018] [Indexed: 11/07/2022]
Abstract
PURPOSE This study explores the possibility of using a gradient moment balanced sequence with a quadratically varied RF excitation phase in the magnetic resonance fingerprinting (MRF) framework to quantify T2 * in addition to δ f , T1 , and T2 tissue properties. METHODS The proposed quadratic RF phase-based MRF method (qRF-MRF) combined a varied RF excitation phase with the existing balanced SSFP (bSSFP)-based MRF method to generate signals that were uniquely sensitive to δ f , T1 , T2 , as well as the distribution width of intravoxel frequency dispersion, Γ . A dictionary, generated through Bloch simulation, containing possible signal evolutions within the physiological range of δ f , T1 , T2 , and Γ , was used to perform parameter estimation. The estimated T2 and Γ were subsequently used to estimate T2 * . The proposed method was evaluated in phantom experiments and healthy volunteers (N = 5). RESULTS The T1 and T2 values from the phantom by qRF-MRF demonstrated good agreement with values obtained by traditional gold standard methods (r2 = 0.995 and 0.997, respectively; concordance correlation coefficient = 0.978 and 0.995, respectively). The T2 * values from the phantom demonstrated good agreement with values obtained through the multi-echo gradient-echo method (r2 = 0.972, concordance correlation coefficient = 0.983). In vivo qRF-MRF-measured T1 , T2 , and T2 * values were compared with measurements by existing methods and literature values. CONCLUSION The proposed qRF-MRF method demonstrated the potential for simultaneous quantification of δ f , T1 , T2 , and T2 * tissue properties.
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Affiliation(s)
- Charlie Yi Wang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Simone Coppo
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio
| | | | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.,Department of Radiology, Case Western Reserve University, Cleveland, Ohio
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.,Department of Radiology, Case Western Reserve University, Cleveland, Ohio
| | - Mark Alan Griswold
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.,Department of Radiology, Case Western Reserve University, Cleveland, Ohio
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28
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Maier O, Schoormans J, Schloegl M, Strijkers GJ, Lesch A, Benkert T, Block T, Coolen BF, Bredies K, Stollberger R. Rapid T 1 quantification from high resolution 3D data with model-based reconstruction. Magn Reson Med 2018; 81:2072-2089. [PMID: 30346053 PMCID: PMC6588000 DOI: 10.1002/mrm.27502] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 08/01/2018] [Accepted: 08/02/2018] [Indexed: 12/25/2022]
Abstract
Purpose Magnetic resonance imaging protocols for the assessment of quantitative information suffer from long acquisition times since multiple measurements in a parametric dimension are required. To facilitate the clinical applicability, accelerating the acquisition is of high importance. To this end, we propose a model‐based optimization framework in conjunction with undersampling 3D radial stack‐of‐stars data. Theory and Methods High resolution 3D T1 maps are generated from subsampled data by employing model‐based reconstruction combined with a regularization functional, coupling information from the spatial and parametric dimension, to exploit redundancies in the acquired parameter encodings and across parameter maps. To cope with the resulting non‐linear, non‐differentiable optimization problem, we propose a solution strategy based on the iteratively regularized Gauss‐Newton method. The importance of 3D‐spectral regularization is demonstrated by a comparison to 2D‐spectral regularized results. The algorithm is validated for the variable flip angle (VFA) and inversion recovery Look‐Locker (IRLL) method on numerical simulated data, MRI phantoms, and in vivo data. Results Evaluation of the proposed method using numerical simulations and phantom scans shows excellent quantitative agreement and image quality. T1 maps from accelerated 3D in vivo measurements, e.g. 1.8 s/slice with the VFA method, are in high accordance with fully sampled reference reconstructions. Conclusions The proposed algorithm is able to recover T1 maps with an isotropic resolution of 1 mm3 from highly undersampled radial data by exploiting structural similarities in the imaging volume and across parameter maps.
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Affiliation(s)
- Oliver Maier
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Jasper Schoormans
- Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam Zuidoost, The Netherlands
| | - Matthias Schloegl
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Gustav J Strijkers
- Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam Zuidoost, The Netherlands
| | - Andreas Lesch
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Thomas Benkert
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, New York.,Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York
| | - Tobias Block
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, New York.,Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, New York
| | - Bram F Coolen
- Department of Biomedical Engineering and Physics, Academic Medical Center, Amsterdam Zuidoost, The Netherlands
| | - Kristian Bredies
- BioTechMed-Graz, Graz, Austria.,Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - Rudolf Stollberger
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
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29
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Körzdörfer G, Jiang Y, Speier P, Pang J, Ma D, Pfeuffer J, Hensel B, Gulani V, Griswold M, Nittka M. Magnetic resonance field fingerprinting. Magn Reson Med 2018; 81:2347-2359. [PMID: 30320925 DOI: 10.1002/mrm.27558] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 09/12/2018] [Accepted: 09/14/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE To develop and evaluate the magnetic resonance field fingerprinting method that simultaneously generates T1 , T2 , B0 , and B 1 + maps from a single continuous measurement. METHODS An encoding pattern was designed to integrate true fast imaging with steady-state precession (TrueFISP), fast imaging with steady-state precession (FISP), and fast low-angle shot (FLASH) sequence segments with varying flip angles, radio frequency (RF) phases, TEs, and gradient moments in a continuous acquisition. A multistep matching process was introduced that includes steps for integrated spiral deblurring and the correction of intravoxel phase dispersion. The method was evaluated in phantoms as well as in vivo studies in brain and lower abdomen. RESULTS Simultaneous measurement of T1 , T2 , B0 , and B 1 + is achieved with T1 and T2 subsequently being less afflicted by B0 and B 1 + variations. Phantom results demonstrate the stability of generated parameter maps. Higher undersampling factors and spatial resolution can be achieved with the proposed method as compared with solely FISP-based magnetic resonance fingerprinting. High-resolution B0 maps can potentially be further used as diagnostic information. CONCLUSION The proposed magnetic resonance field fingerprinting method can estimate T1 , T2 , B0 , and B 1 + maps accurately in phantoms, in the brain, and in the lower abdomen.
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Affiliation(s)
- Gregor Körzdörfer
- Siemens Healthcare GmbH, Erlangen, Germany.,Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Yun Jiang
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio
| | | | - Jianing Pang
- Siemens Medical Solutions USA, Chicago, Illinois
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio
| | | | - Bernhard Hensel
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio.,Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Mark Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio.,Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
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30
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Bipin Mehta B, Coppo S, Frances McGivney D, Ian Hamilton J, Chen Y, Jiang Y, Ma D, Seiberlich N, Gulani V, Alan Griswold M. Magnetic resonance fingerprinting: a technical review. Magn Reson Med 2018; 81:25-46. [PMID: 30277265 DOI: 10.1002/mrm.27403] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 05/01/2018] [Accepted: 05/21/2018] [Indexed: 01/31/2023]
Abstract
Multiparametric quantitative imaging is gaining increasing interest due to its widespread advantages in clinical applications. Magnetic resonance fingerprinting is a recently introduced approach of fast multiparametric quantitative imaging. In this article, magnetic resonance fingerprinting acquisition, dictionary generation, reconstruction, and validation are reviewed.
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Affiliation(s)
- Bhairav Bipin Mehta
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Simone Coppo
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Debra Frances McGivney
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Jesse Ian Hamilton
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Yong Chen
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Yun Jiang
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Dan Ma
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio.,Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Mark Alan Griswold
- Department of Radiology, Case Western Reserve Universityand University Hospitals Cleveland Medical Center, Cleveland, Ohio.,Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
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31
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Panda A, Mehta BB, Coppo S, Jiang Y, Ma D, Seiberlich N, Griswold MA, Gulani V. Magnetic Resonance Fingerprinting-An Overview. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2017; 3:56-66. [PMID: 29868647 DOI: 10.1016/j.cobme.2017.11.001] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition. The ability to reproducibly and quantitatively measure tissue properties could enable more objective tissue diagnosis, comparisons of scans acquired at different locations and time points, longitudinal follow-up of individual patients and development of imaging biomarkers. This review provides a general overview of MRF technology, current preclinical and clinical applications and potential future directions. MRF has been initially evaluated in brain, prostate, liver, cardiac, musculoskeletal imaging, and measurement of perfusion and microvascular properties through MR vascular fingerprinting.
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Affiliation(s)
- Ananya Panda
- Department of Radiology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Bhairav B Mehta
- Department of Radiology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Simone Coppo
- Department of Radiology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Yun Jiang
- Department of Biomedical Engineering, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Nicole Seiberlich
- Department of Radiology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.,Department of Biomedical Engineering, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Mark A Griswold
- Department of Radiology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.,Department of Biomedical Engineering, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.,Department of Biomedical Engineering, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
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