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Zimmermann M, Abbas Z, Sommer Y, Lewin A, Ramkiran S, Felder J, Worthoff WA, Oros-Peusquens AM, Yun SD, Shah NJ. QRAGE-Simultaneous multiparametric quantitative MRI of water content, T 1, T 2*, and magnetic susceptibility at ultrahigh field strength. Magn Reson Med 2024. [PMID: 39219160 DOI: 10.1002/mrm.30272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 07/26/2024] [Accepted: 08/10/2024] [Indexed: 09/04/2024]
Abstract
PURPOSE To introduce quantitative rapid gradient-echo (QRAGE), a novel approach for the simultaneous mapping of multiple quantitative MRI parameters, including water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength. METHODS QRAGE leverages a newly developed multi-echo MPnRAGE sequence, facilitating the acquisition of 171 distinct contrast images across a range of inversion and TE points. To maintain a short acquisition time, we introduce MIRAGE2, a novel model-based reconstruction method that exploits prior knowledge of temporal signal evolution, represented as damped complex exponentials. MIRAGE2 minimizes local Block-Hankel and Casorati matrices. Parameter maps are derived from the reconstructed contrast images through postprocessing steps. We validate QRAGE through extensive simulations, phantom studies, and in vivo experiments, demonstrating its capability for high-precision imaging. RESULTS In vivo brain measurements show the promising performance of QRAGE, with test-retest SDs and deviations from reference methods of < 0.8% for water content, < 17 ms for T1, and < 0.7 ms for T2*. QRAGE achieves whole-brain coverage at a 1-mm isotropic resolution in just 7 min and 15 s, comparable to the acquisition time of an MP2RAGE scan. In addition, QRAGE generates a contrast image akin to the UNI image produced by MP2RAGE. CONCLUSION QRAGE is a new, successful approach for simultaneously mapping multiple MR parameters at ultrahigh field.
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Affiliation(s)
- Markus Zimmermann
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
| | - Zaheer Abbas
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
| | - Yannic Sommer
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
| | - Alexander Lewin
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-11, Jülich, Germany
| | - Shukti Ramkiran
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Jörg Felder
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
- RWTH Aachen University, Aachen, Germany
| | - Wieland A Worthoff
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
| | | | - Seong Dae Yun
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
| | - N Jon Shah
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-4, Jülich, Germany
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine-11, Jülich, Germany
- JARA-BRAIN-Translational Medicine, Aachen, Germany
- Department of Neurology, RWTH Aachen University, Aachen, Germany
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Versteeg E, Liu H, van der Heide O, Fuderer M, van den Berg CAT, Sbrizzi A. High SNR full brain relaxometry at 7T by accelerated MR-STAT. Magn Reson Med 2024; 92:226-235. [PMID: 38326909 DOI: 10.1002/mrm.30052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/21/2023] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE To demonstrate the feasibility and robustness of the Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) framework for fast, high SNR relaxometry at 7T. METHODS To deploy MR-STAT on 7T-systems, we designed optimized flip-angles using the BLAKJac-framework that incorporates the SAR-constraints. Transmit RF-inhomogeneities were mitigated by including a measuredB 1 + $$ {B}_1^{+} $$ -map in the reconstruction. Experiments were performed on a gel-phantom and on five volunteers to explore the robustness of the sequence and its sensitivity toB 1 + $$ {B}_1^{+} $$ inhomogeneities. The SNR-gain at 7T was explored by comparing phantom and in vivo results to MR-STAT at 3T in terms of SNR-efficiency. RESULTS The higher SNR at 7T enabled two-fold acceleration with respect to current 2D MR-STAT protocols at lower field strengths. The resulting scan had whole-brain coverage, with 1 x 1 x 3 mm3 resolution (1.5 mm slice-gap) and was acquired within 3 min including theB 1 + $$ {B}_1^{+} $$ -mapping. AfterB 1 + $$ {B}_1^{+} $$ -correction, the estimated T1 and T2 in a phantom showed a mean relative error of, respectively, 1.7% and 4.4%. In vivo, the estimated T1 and T2 in gray and white matter corresponded to the range of values reported in literature with a variation over the subjects of 1.0%-2.1% (WM-GM) for T1 and 4.3%-5.3% (WM-GM) for T2. We measured a higher SNR-efficiency at 7T (R = 2) than at 3T for both T1 and T2 with, respectively, a 4.1 and 2.3 times increase in SNR-efficiency. CONCLUSION We presented an accelerated version of MR-STAT tailored to high field (7T) MRI using a low-SAR flip-angle train and showed high quality parameter maps with an increased SNR-efficiency compared to MR-STAT at 3T.
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Affiliation(s)
- Edwin Versteeg
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Hongyan Liu
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Oscar van der Heide
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Miha Fuderer
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alessandro Sbrizzi
- Computational Imaging Group, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
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Xuan L, Zhang Y, Wu J, He Y, Xu Z. Quantitative brain mapping using magnetic resonance fingerprinting on a 50-mT portable MRI scanner. NMR IN BIOMEDICINE 2024; 37:e5077. [PMID: 38057971 DOI: 10.1002/nbm.5077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/17/2023] [Accepted: 11/02/2023] [Indexed: 12/08/2023]
Abstract
Ultralow-field magnetic resonance imaging (ULF-MRI) has broad application prospects because of its portable hardware system and low cost. However, the low B0 magnitude of ULF-MRI results in a reduced signal-to-noise ratio in qualitative images compared with that of commercial high-field MRI, which can affect the visibility and delineation of tissues and lesions. In this work, a magnetic resonance fingerprinting (MRF) approach is applied to a homemade 50-mT ULF-MRI scanner to achieve efficient quantitative brain imaging, which is an original and promising disease-diagnosis approach for portable MRI systems. An inversion recovery fast imaging with steady-state precession-based sequence is utilized for MRF through Cartesian acquisition. A microdictionary analysis method is proposed to select the optimal repetition time and flip angle variation schedule and ensure the best possible tissue discriminative ability of MRF. The T1 and T2 relaxation properties and the B1 + distribution are considered for estimation, and the results are compared with those of gold standard (GS) quantitative imaging or qualitative imaging methods. The phantom experiment indicates that the quantitative values obtained by schedule-optimized MRF show good agreement, and the bias from the GS results is acceptable. The in vivo experiment shows that the relaxation times of white and gray matter estimated by MRF are slightly lower than the reference data, and the relaxation times of lipid are within the range of the reference data. Compared with qualitative MRI under ULF, MRF can intuitively reflect various items of brain tissue information in a single scan, so it is a valuable addition to point-of-care imaging approaches.
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Affiliation(s)
- Liang Xuan
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Yuxiang Zhang
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Jiamin Wu
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
| | - Yucheng He
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
| | - Zheng Xu
- School of Electrical Engineering, Chongqing University, Chongqing, China
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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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Liu H, van der Heide O, Versteeg E, Froeling M, Fuderer M, Xu F, van den Berg CAT, Sbrizzi A. A three-dimensional Magnetic Resonance Spin Tomography in Time-domain protocol for high-resolution multiparametric quantitative magnetic resonance imaging. NMR IN BIOMEDICINE 2024; 37:e5050. [PMID: 37857335 DOI: 10.1002/nbm.5050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/04/2023] [Accepted: 09/11/2023] [Indexed: 10/21/2023]
Abstract
Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) is a multiparametric quantitative MR framework, which allows for simultaneously acquiring quantitative tissue parameters such as T1, T2, and proton density from one single short scan. A typical two-dimensional (2D) MR-STAT acquisition uses a gradient-spoiled, gradient-echo sequence with a slowly varying RF flip-angle train and Cartesian readouts, and the quantitative tissue maps are reconstructed by an iterative, model-based optimization algorithm. In this work, we design a three-dimensional (3D) MR-STAT framework based on previous 2D work, in order to achieve better image signal-to-noise ratio, higher though-plane resolution, and better tissue characterization. Specifically, we design a 7-min, high-resolution 3D MR-STAT sequence, and the corresponding two-step reconstruction algorithm for the large-scale dataset. To reduce the long acquisition time, Cartesian undersampling strategies such as SENSE are adopted in our transient-state quantitative framework. To reduce the computational burden, a data-splitting scheme is designed for decoupling the 3D reconstruction problem into independent 2D reconstructions. The proposed 3D framework is validated by numerical simulations, phantom experiments, and in vivo experiments. High-quality knee quantitative maps with 0.8 × 0.8 × 1.5 mm3 resolution and bilateral lower leg maps with 1.6 mm isotropic resolution can be acquired using the proposed 7-min acquisition sequence and the 3-min-per-slice decoupled reconstruction algorithm. The proposed 3D MR-STAT framework could have wide clinical applications in the future.
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Affiliation(s)
- Hongyan Liu
- Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Oscar van der Heide
- Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Edwin Versteeg
- Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martijn Froeling
- Department of Radiology, Imaging Division, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Miha Fuderer
- Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Fei Xu
- Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alessandro Sbrizzi
- Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
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Cencini M, Lancione M, Pasquariello R, Peretti L, Pirkl CM, Schulte RF, Buonincontri G, Arduino A, Zilberti L, Biagi L, Tosetti M. Fast high-resolution electric properties tomography using three-dimensional quantitative transient-state imaging-based water fraction estimation. NMR IN BIOMEDICINE 2024; 37:e5039. [PMID: 37714527 DOI: 10.1002/nbm.5039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/18/2023] [Accepted: 08/28/2023] [Indexed: 09/17/2023]
Abstract
In this study, we aimed to develop a fast and robust high-resolution technique for clinically feasible electrical properties tomography based on water content maps (wEPT) using Quantitative Transient-state Imaging (QTI), a multiparametric transient state-based method that is similar to MR fingerprinting. Compared with the original wEPT implementation based on standard spin-echo acquisition, QTI provides robust electrical properties quantification towards B1 + inhomogeneities and full quantitative relaxometry data. To validate the proposed approach, 3D QTI data of 12 healthy volunteers were acquired on a 1.5 T scanner. QTI-provided T1 maps were used to compute water content maps of the tissues using an empirical relationship based on literature ex-vivo measurements. Assuming that electrical properties are modulated mainly by tissue water content, the water content maps were used to derive electrical conductivity and relative permittivity maps. The proposed technique was compared with a conventional phase-only Helmholtz EPT (HH-EPT) acquisition both within whole white matter, gray matter, and cerebrospinal fluid masks, and within different white and gray matter subregions. In addition, QTI-based wEPT was retrospectively applied to four multiple sclerosis adolescent and adult patients, compared with conventional contrast-weighted imaging in terms of lesion delineation, and quantitatively assessed by measuring the variation of electrical properties in lesions. Results obtained with the proposed approach agreed well with theoretical predictions and previous in vivo findings in both white and gray matter. The reconstructed maps showed greater anatomical detail and lower variability compared with standard phase-only HH-EPT. The technique can potentially improve delineation of pathology when compared with conventional contrast-weighted imaging and was able to detect significant variations in lesions with respect to normal-appearing tissues. In conclusion, QTI can reliably measure conductivity and relative permittivity of brain tissues within a short scan time, opening the way to the study of electric properties in clinical settings.
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Affiliation(s)
- Matteo Cencini
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
| | | | | | | | | | | | | | | | - Luca Zilberti
- Istituto Nazionale di Ricerca Metrologica (INRiM), Torino, Italy
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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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Widmaier M, Lim SI, Wenz D, Xin L. Fast in vivo assay of creatine kinase activity in the human brain by 31 P magnetic resonance fingerprinting. NMR IN BIOMEDICINE 2023; 36:e4998. [PMID: 37424110 DOI: 10.1002/nbm.4998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 05/15/2023] [Accepted: 06/12/2023] [Indexed: 07/11/2023]
Abstract
A new and efficient magnetisation transfer 31 P magnetic resonance fingerprinting (MT-31 P-MRF) approach is introduced to measure the creatine kinase metabolic ratek CK between phosphocreatine (PCr) and adenosine triphosphate (ATP) in human brain. The MRF framework is extended to overcome challenges in conventional 31 P measurement methods in the human brain, enabling reduced acquisition time and specific absorption rate (SAR). To address the challenge of creating and matching large multiparametric dictionaries in an MRF scheme, a nested iteration interpolation method (NIIM) is introduced. As the number of parameters to estimate increases, the size of the dictionary grows exponentially. NIIM can reduce the computational load by breaking dictionary matching into subsolutions of linear computational order. MT-31 P-MRF combined with NIIM providesT 1 PCr ,T 1 ATP andk CK estimates in good agreement with those obtained by the exchange kinetics by band inversion transfer (EBIT) method and literature values. Furthermore, the test-retest reproducibility results showed that MT-31 P-MRF achieves a similar or better coefficient of variation (<12%) forT 1 ATP andk CK measurements in 4 min 15 s, than EBIT with 17 min 4 s scan time, enabling a fourfold reduction in scan time. We conclude that MT-31 P-MRF in combination with NIIM is a fast, accurate, and reproducible approach for in vivok CK assays in the human brain, which enables the potential to investigate energy metabolism in a clinical setting.
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Affiliation(s)
- Mark Widmaier
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Laboratory for Functional and Metabolic Imaging, École polytechnique fédérale de Lausanne, Lausanne, Switzerland
- Animal Imaging and Technology, École polytechnique fédérale de Lausanne, Lausanne, Switzerland
| | - Song-I Lim
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Laboratory for Functional and Metabolic Imaging, École polytechnique fédérale de Lausanne, Lausanne, Switzerland
- Animal Imaging and Technology, École polytechnique fédérale de Lausanne, Lausanne, Switzerland
| | - Daniel Wenz
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, École polytechnique fédérale de Lausanne, Lausanne, Switzerland
| | - Lijing Xin
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, École polytechnique fédérale de Lausanne, Lausanne, Switzerland
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Moinian S, Vegh V, Reutens D. Towards automated in vivo parcellation of the human cerebral cortex using supervised classification of magnetic resonance fingerprinting residuals. Cereb Cortex 2023; 33:1550-1565. [PMID: 35483706 PMCID: PMC9977388 DOI: 10.1093/cercor/bhac155] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Accurate parcellation of the cerebral cortex in an individual is a guide to its underlying organization. The most promising in vivo quantitative magnetic resonance (MR)-based microstructural cortical mapping methods are yet to achieve a level of parcellation accuracy comparable to quantitative histology. METHODS We scanned 6 participants using a 3D echo-planar imaging MR fingerprinting (EPI-MRF) sequence on a 7T Siemens scanner. After projecting MRF signals to the individual-specific inflated model of the cortical surface, normalized autocorrelations of MRF residuals of vertices of 8 microstructurally distinct areas (BA1, BA2, BA4a, BA6, BA44, BA45, BA17, and BA18) from 3 cortical regions were used as feature vector inputs into linear support vector machine (SVM), radial basis function SVM (RBF-SVM), random forest, and k-nearest neighbors supervised classification algorithms. The algorithms' prediction performance was compared using: (i) features from each vertex or (ii) features from neighboring vertices. RESULTS The neighborhood-based RBF-SVM classifier achieved the highest prediction score of 0.85 for classification of MRF residuals in the central region from a held-out participant. CONCLUSIONS We developed an automated method of cortical parcellation using a combination of MR fingerprinting residual analysis and machine learning classification. Our findings provide the basis for employing unsupervised learning algorithms for whole-cortex structural parcellation in individuals.
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Affiliation(s)
- Shahrzad Moinian
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| | - David Reutens
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia.,ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
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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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11
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Cao X, Liao C, Iyer SS, Wang Z, Zhou Z, Dai E, Liberman G, Dong Z, Gong T, He H, Zhong J, Bilgic B, Setsompop K. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. Magn Reson Med 2022; 88:133-150. [PMID: 35199877 DOI: 10.1002/mrm.29194] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/16/2021] [Accepted: 01/21/2022] [Indexed: 11/07/2022]
Abstract
PURPOSE To improve image quality and accelerate the acquisition of 3D MR fingerprinting (MRF). METHODS Building on the multi-axis spiral-projection MRF technique, a subspace reconstruction with locally low-rank constraint and a modified spiral-projection spatiotemporal encoding scheme called tiny golden-angle shuffling were implemented for rapid whole-brain high-resolution quantitative mapping. Reconstruction parameters such as the locally low-rank regularization parameter and the subspace rank were tuned using retrospective in vivo data and simulated examinations. B0 inhomogeneity correction using multifrequency interpolation was incorporated into the subspace reconstruction to further improve the image quality by mitigating blurring caused by off-resonance effect. RESULTS The proposed MRF acquisition and reconstruction framework yields high-quality 1-mm isotropic whole-brain quantitative maps in 2 min at better quality compared with 6-min acquisitions of prior approaches. The proposed method was validated to not induce bias in T1 and T2 mapping. High-quality whole-brain MRF data were also obtained at 0.66-mm isotropic resolution in 4 min using the proposed technique, where the increased resolution was shown to improve visualization of subtle brain structures. CONCLUSIONS The proposed tiny golden-angle shuffling, MRF with optimized spiral-projection trajectory and subspace reconstruction enables high-resolution quantitative mapping in ultrafast acquisition time.
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Affiliation(s)
- Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Siddharth Srinivasan Iyer
- Department of Radiology, Stanford University, Stanford, California, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Zhixing Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Zihan Zhou
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Erpeng Dai
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Gilad Liberman
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Ting Gong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.,Department of Imaging Sciences, University of Rochester, Rochester, New York, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Cambridge, Massachusetts, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Electrical Engineering, Stanford University, Stanford, California, USA
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12
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Henningsson M. Cartesian dictionary-based native T 1 and T 2 mapping of the myocardium. Magn Reson Med 2022; 87:2347-2362. [PMID: 34985143 DOI: 10.1002/mrm.29143] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/08/2021] [Accepted: 12/14/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To implement and evaluate a new dictionary-based technique for native myocardial T1 and T2 mapping using Cartesian sampling. METHODS The proposed technique (Multimapping) consisted of single-shot Cartesian image acquisitions in 10 consecutive cardiac cycles, with inversion pulses in cycle 1 and 5, and T2 preparation (TE: 30 ms, 50 ms, and 70 ms) in cycles 8-10. Multimapping was simulated for different T1 and T2 , where entries corresponding to the k-space centers were matched to acquired data. Experiments were performed in a phantom, 16 healthy subjects, and 3 patients with cardiovascular disease. RESULTS Multimapping phantom measurements showed good agreement with reference values for both T1 and T2 , with no discernable heart-rate dependency for T1 and T2 within the range of myocardium. In vivo mean T1 in healthy subjects was significantly higher using Multimapping (T1 = 1114 ± 14 ms) compared to the reference (T1 = 991 ± 26 ms) (p < 0.01). Mean Multimapping T2 (47.1 ± 1.3 ms) and T2 spatial variability (5.8 ± 1.0 ms) was significantly lower compared to the reference (T2 = 54.7 ± 2.2 ms, p < 0.001; spatial variability = 8.4 ± 2.0 ms, p < 0.01). Increased T1 and T2 was detected in all patients using Multimapping. CONCLUSIONS Multimapping allows for simultaneous native myocardial T1 and T2 mapping with a conventional Cartesian trajectory, demonstrating promising in vivo image quality and parameter quantification results.
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Affiliation(s)
- Markus Henningsson
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring Sciences (HMV), Linköping University, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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13
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Zou L, Liang D, Ye H, Su S, Zhu Y, Liu X, Zheng H, Wang H. Quantitative MR relaxation using MR fingerprinting with fractional-order signal evolution. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2021; 330:107042. [PMID: 34333244 DOI: 10.1016/j.jmr.2021.107042] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 06/19/2021] [Accepted: 07/19/2021] [Indexed: 06/13/2023]
Abstract
The fractional-order Bloch equations have been shown to describe a wider range of experimental situations involving heterogeneous, porous, or composite materials. This paper introduces a novel dictionary of quantitative MR fingerprinting generated by signal evolution model with fractional-order Bloch equations to describe magnetic resonance (MR) relaxation. Here, the fractional-order relaxation models are implemented into Bloch equations through phase transitions using EPG simulation. In the phantom experiments, the fractional-order analysis showed smaller root mean squared error (T1: RMSE = 5.21%, T2: RMSE=3.75%) using the proposed method compared to using conventional method. Among the in vivo experiments of human brains, the estimated T1 and T2 values (mean ± SD) were 843 ± 46.3 ms and 70 ± 4.7 ms in white matter, 1323 ± 28.5 ms and 95 ± 3.8 ms in gray matter. So the proposed method can provide well extensions of current MR fingerprinting and has shown potential to apply into the phantom experiments and the in vivo applications to approach the standard methods for quantitative imaging.
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Affiliation(s)
- Lixian Zou
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Dong Liang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China; Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Huihui Ye
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shi Su
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Xin Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China.
| | - Haifeng Wang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China.
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14
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van Riel MHC, Yu Z, Hodono S, Xia D, Chandarana H, Fujimoto K, Cloos MA. Free-breathing abdominal T 1 mapping using an optimized MR fingerprinting sequence. NMR IN BIOMEDICINE 2021; 34:e4531. [PMID: 33902155 PMCID: PMC8218311 DOI: 10.1002/nbm.4531] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 04/03/2021] [Accepted: 04/05/2021] [Indexed: 05/31/2023]
Abstract
In this work, we propose a free-breathing magnetic resonance fingerprinting (MRF) method that can be used to obtain B1+ -robust quantitative T1 maps of the abdomen in a clinically acceptable time. A three-dimensional MRF sequence with a radial stack-of-stars trajectory was implemented, and its k-space acquisition ordering was adjusted to improve motion-robustness in the context of MRF. The flip angle pattern was optimized using the Cramér-Rao Lower Bound, and the encoding efficiency of sequences with 300, 600, 900 and 1800 flip angles was evaluated. To validate the sequence, a movable multicompartment phantom was developed. Reference multiparametric maps were acquired under stationary conditions using a previously validated MRF method. Periodic motion of the phantom was used to investigate the motion-robustness of the proposed sequence. The best performing sequence length (600 flip angles) was used to image the abdomen during a free-breathing volunteer scan. When using a series of 600 or more flip angles, the estimated T1 values in the stationary phantom showed good agreement with the reference scan. Phantom experiments revealed that motion-related artifacts can appear in the quantitative maps and confirmed that a motion-robust k-space ordering is essential. The in vivo scan demonstrated that the proposed sequence can produce clean parameter maps while the subject breathes freely. Using this sequence, it is possible to generate B1+ -robust quantitative maps of T1 and B1+ next to M0 -weighted images under free-breathing conditions at a clinically usable resolution within 5 min.
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Affiliation(s)
- Max H. C. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Zidan Yu
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
| | - Shota Hodono
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
- Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia
| | - Ding Xia
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Koji Fujimoto
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Martijn A. Cloos
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
- Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia
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15
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MacAskill CJ, Markley M, Farr S, Parsons A, Perino JR, McBennett K, Kutney K, Drumm ML, Pritts N, Griswold MA, Ma D, Dell KM, Flask CA, Chen Y. Rapid B 1-Insensitive MR Fingerprinting for Quantitative Kidney Imaging. Radiology 2021; 300:380-387. [PMID: 34100680 DOI: 10.1148/radiol.2021202302] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background MR fingerprinting (MRF) provides rapid and simultaneous quantification of multiple tissue parameters in a single scan. Purpose To evaluate a rapid kidney MRF technique at 3.0 T in phantoms, healthy volunteers, and patients. Materials and Methods A 15-second kidney MRF acquisition was designed with 12 acquisition segments, a range of low flip angles (5°-12°), multiple magnetization preparation schema (T1, T2, and fat suppression), and an undersampled spiral trajectory. This technique was first validated in vitro using standardized T1 and T2 phantoms. Kidney T1 and T2 maps were then obtained for 10 healthy adult volunteers (mean age ± standard deviation, 35 years ± 13; six men) and three pediatric patients with autosomal recessive polycystic kidney disease (ARPKD) (mean age, 10 years ± 3; two boys) between August 2019 and October 2020 to evaluate the method in vivo. Results Results in nine phantoms showed good agreement with spin-echo-based T1 and T2 values (R2 > 0.99). In vivo MRF kidney T1 and T2 assessments in healthy adult volunteers (cortex: T1, 1362 msec ± 5; T2, 64 msec ± 5; medulla: T1, 1827 msec ± 94; T2, 69 msec ± 3) were consistent with values in the literature but with improved precision in comparison with prior MRF implementations. In vivo MRF-based kidney T1 and T2 values with and without B1 correction were in good agreement (R2 > 0.96, P < .001), demonstrating limited sensitivity to B1 field inhomogeneities. Additional MRF reconstructions using the first nine segments of the MRF profiles (11-second acquisition time) were in good agreement with the reconstructions using 12 segments (15-second acquisition time) (R2 > 0.87, P < .001). Repeat kidney MRF scans for the three patients with ARPKD on successive days also demonstrated good reproducibility (T1 and T2: <3% difference). Conclusion A kidney MR fingerprinting method provided in vivo kidney T1 and T2 maps at 3.0 T in a single breath hold with improved precision and no need for B1 correction. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Laustsen in this issue.
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Affiliation(s)
- Christina J MacAskill
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Michael Markley
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Susan Farr
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Ashlee Parsons
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Jacob R Perino
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Kimberly McBennett
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Katherine Kutney
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Mitchell L Drumm
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Nicole Pritts
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Mark A Griswold
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Dan Ma
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Katherine M Dell
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Chris A Flask
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
| | - Yong Chen
- From the Departments of Radiology (C.J.M., S.F., J.R.P., N.P., M.A.G., D.M., C.A.F., Y.C.), Genetics and Genome Sciences (M.L.D.), Pediatrics (M.L.D., K.M.D., C.A.F.), and Biomedical Engineering (M.A.G., D.M., C.A.F.), Case Western Reserve University, 11100 Euclid Ave, Bowell Building, Room B131, Cleveland, OH 44106; Departments of Radiology (M.M.) and Pediatrics (K.M., K.K.), University Hospitals Cleveland Medical Center, Cleveland, Ohio; and Center for Pediatric Nephrology, Cleveland Clinic Children's Hospital, Cleveland, Ohio (A.P., K.M.D.)
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16
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Buonincontri G, Kurzawski JW, Kaggie JD, Matys T, Gallagher FA, Cencini M, Donatelli G, Cecchi P, Cosottini M, Martini N, Frijia F, Montanaro D, Gómez PA, Schulte RF, Retico A, Tosetti M. Three dimensional MRF obtains highly repeatable and reproducible multi-parametric estimations in the healthy human brain at 1.5T and 3T. Neuroimage 2021; 226:117573. [PMID: 33221451 DOI: 10.1016/j.neuroimage.2020.117573] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 12/19/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is highly promising as a quantitative MRI technique due to its accuracy, robustness, and efficiency. Previous studies have found high repeatability and reproducibility of 2D MRF acquisitions in the brain. Here, we have extended our investigations to 3D MRF acquisitions covering the whole brain using spiral projection k-space trajectories. Our travelling head study acquired test/retest data from the brains of 12 healthy volunteers and 8 MRI systems (3 systems at 3 T and 5 at 1.5 T, all from a single vendor), using a study design not requiring all subjects to be scanned at all sites. The pulse sequence and reconstruction algorithm were the same for all acquisitions. After registration of the MRF-derived PD T1 and T2 maps to an anatomical atlas, coefficients of variation (CVs) were computed to assess test/retest repeatability and inter-site reproducibility in each voxel, while a General Linear Model (GLM) was used to determine the voxel-wise variability between all confounders, which included test/retest, subject, field strength and site. Our analysis demonstrated a high repeatability (CVs 0.7-1.3% for T1, 2.0-7.8% for T2, 1.4-2.5% for normalized PD) and reproducibility (CVs of 2.0-5.8% for T1, 7.4-10.2% for T2, 5.2-9.2% for normalized PD) in gray and white matter. Both repeatability and reproducibility improved when compared to similar experiments using 2D acquisitions. Three-dimensional MRF obtains highly repeatable and reproducible estimations of T1 and T2, supporting the translation of MRF-based fast quantitative imaging into clinical applications.
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Affiliation(s)
| | - Jan W Kurzawski
- IRCCS Stella Maris, Pisa, Italy; National Institute for Nuclear Physics (INFN), Pisa, Italy
| | - Joshua D Kaggie
- Department of Radiology, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Tomasz Matys
- Department of Radiology, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Ferdia A Gallagher
- Department of Radiology, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Matteo Cencini
- IRCCS Stella Maris, Pisa, Italy; Imago7 Foundation, Pisa, Italy
| | - Graziella Donatelli
- Imago7 Foundation, Pisa, Italy; U.O. Neuroradiologia, Azienda Ospedaliera Universitaria Pisana (AOUP), Pisa, Italy
| | - Paolo Cecchi
- U.O. Neuroradiologia, Azienda Ospedaliera Universitaria Pisana (AOUP), Pisa, Italy
| | - Mirco Cosottini
- Imago7 Foundation, Pisa, Italy; U.O. Neuroradiologia, Azienda Ospedaliera Universitaria Pisana (AOUP), Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Nicola Martini
- U.O.C. Bioingegneria e Ing. Clinica, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Francesca Frijia
- U.O.C. Bioingegneria e Ing. Clinica, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Domenico Montanaro
- U.O.C. Risonanza Magnetica Specialistica e Neuroradiologia, Fondazione CNR/Regione Toscana G. Monasterio, Pisa-Massa, Italy
| | - Pedro A Gómez
- Imago7 Foundation, Pisa, Italy; Technical University of Munich, Munich, Germany
| | | | | | - Michela Tosetti
- IRCCS Stella Maris, Pisa, Italy; Imago7 Foundation, Pisa, Italy.
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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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18
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Nolte T, Scholten H, Gross-Weege N, Amthor T, Koken P, Doneva M, Schulz V. Confounding factors in breast magnetic resonance fingerprinting: B 1 + , slice profile, and diffusion effects. Magn Reson Med 2020; 85:1865-1880. [PMID: 33118649 DOI: 10.1002/mrm.28545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 09/03/2020] [Accepted: 09/14/2020] [Indexed: 11/09/2022]
Abstract
PURPOSE Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane B 1 + , slice profile and diffusion effects on T1 and T2 estimation in the female breast at 1.5T. METHODS Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE. RESULTS Theoretic analysis predicts T1 under-/T2 overestimation if the nominal flip angles are underestimated and inversely, T1 under-/T2 overestimation if omitting slice profile correction, and T1 under-/T2 underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane B 1 + effects occurred in vivo, causing T2 left-right inhomogeneity between both breasts. Their correction decreased the T2 difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T2 most strongly, resulting in -22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions. CONCLUSION Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.
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Affiliation(s)
- Teresa Nolte
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Hannah Scholten
- Department of Diagnostic and Interventional Radiology, University of Würzburg, Würzburg, Germany
| | - Nicolas Gross-Weege
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Thomas Amthor
- Tomographic Imaging Systems, Philips Research Europe, Hamburg, Germany
| | - Peter Koken
- Tomographic Imaging Systems, Philips Research Europe, Hamburg, Germany
| | - Mariya Doneva
- Tomographic Imaging Systems, Philips Research Europe, Hamburg, Germany
| | - Volkmar Schulz
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.,Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.,Physics Institute III B, RWTH Aachen University, Aachen, Germany
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Hamilton JI, Currey D, Rajagopalan S, Seiberlich N. Deep learning reconstruction for cardiac magnetic resonance fingerprinting T 1 and T 2 mapping. Magn Reson Med 2020; 85:2127-2135. [PMID: 33107162 DOI: 10.1002/mrm.28568] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 09/30/2020] [Accepted: 10/02/2020] [Indexed: 02/01/2023]
Abstract
PURPOSE To develop a deep learning method for rapidly reconstructing T1 and T2 maps from undersampled electrocardiogram (ECG) triggered cardiac magnetic resonance fingerprinting (cMRF) images. METHODS A neural network was developed that outputs T1 and T2 values when given a measured cMRF signal time course and cardiac RR interval times recorded by an ECG. Over 8 million cMRF signals, corresponding to 4000 random cardiac rhythms, were simulated for training. The training signals were corrupted by simulated k-space undersampling artifacts and random phase shifts to promote robust learning. The deep learning reconstruction was evaluated in Monte Carlo simulations for a variety of cardiac rhythms and compared with dictionary-based pattern matching in 58 healthy subjects at 1.5T. RESULTS In simulations, the normalized root-mean-square error (nRMSE) for T1 was below 1% in myocardium, blood, and liver for all tested heart rates. For T2 , the nRMSE was below 4% for myocardium and liver and below 6% for blood for all heart rates. The difference in the mean myocardial T1 or T2 observed in vivo between dictionary matching and deep learning was 3.6 ms for T1 and -0.2 ms for T2 . Whereas dictionary generation and pattern matching required more than 4 min per slice, the deep learning reconstruction only required 336 ms. CONCLUSION A neural network is introduced for reconstructing cMRF T1 and T2 maps directly from undersampled spiral images in under 400 ms and is robust to arbitrary cardiac rhythms, which paves the way for rapid online display of cMRF maps.
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Affiliation(s)
- Jesse I Hamilton
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Danielle Currey
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sanjay Rajagopalan
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.,Division of Cardiovascular Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
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20
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Aringhieri G, Zampa V, Tosetti M. Musculoskeletal MRI at 7 T: do we need more or is it more than enough? Eur Radiol Exp 2020; 4:48. [PMID: 32761480 PMCID: PMC7410909 DOI: 10.1186/s41747-020-00174-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 07/01/2020] [Indexed: 12/18/2022] Open
Abstract
Ultra-high field magnetic resonance imaging (UHF-MRI) provides important diagnostic improvements in musculoskeletal imaging. The higher signal-to-noise ratio leads to higher spatial and temporal resolution which results in improved anatomic detail and higher diagnostic confidence. Several methods, such as T2, T2*, T1rho mapping, delayed gadolinium-enhanced, diffusion, chemical exchange saturation transfer, and magnetisation transfer techniques, permit a better tissue characterisation. Furthermore, UHF-MRI enables in vivo measurements by low-γ nuclei (23Na, 31P, 13C, and 39K) and the evaluation of different tissue metabolic pathways. European Union and Food and Drug Administration approvals for clinical imaging at UHF have been the first step towards a more routinely use of this technology, but some drawbacks are still present limiting its widespread clinical application. This review aims to provide a clinically oriented overview about the application of UHF-MRI in the different anatomical districts and tissues of musculoskeletal system and its pros and cons. Further studies are needed to consolidate the added value of the use of UHF-MRI in the routine clinical practice and promising efforts in technology development are already in progress.
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Affiliation(s)
- Giacomo Aringhieri
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Risorgimento, 36, Pisa, Italy.
| | - Virna Zampa
- Diagnostic and Interventional Radiology, University Hospital of Pisa, Via paradisa, 2, Pisa, Italy
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21
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Hamilton JI, Jiang Y, Eck B, Griswold M, Seiberlich N. Cardiac cine magnetic resonance fingerprinting for combined ejection fraction, T 1 and T 2 quantification. NMR IN BIOMEDICINE 2020; 33:e4323. [PMID: 32500541 PMCID: PMC7772953 DOI: 10.1002/nbm.4323] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 04/23/2020] [Accepted: 04/24/2020] [Indexed: 05/07/2023]
Abstract
This study introduces a technique called cine magnetic resonance fingerprinting (cine-MRF) for simultaneous T1 , T2 and ejection fraction (EF) quantification. Data acquired with a free-running MRF sequence are retrospectively sorted into different cardiac phases using an external electrocardiogram (ECG) signal. A low-rank reconstruction with a finite difference sparsity constraint along the cardiac motion dimension yields images resolved by cardiac phase. To improve SNR and precision in the parameter maps, these images are nonrigidly registered to the same phase and matched to a dictionary to generate T1 and T2 maps. Cine images for computing left ventricular volumes and EF are also derived from the same data. Cine-MRF was tested in simulations using a numerical relaxation phantom. Phantom and in vivo scans of 19 subjects were performed at 3 T during a 10.9 seconds breath-hold with an in-plane resolution of 1.6 x 1.6 mm2 and 24 cardiac phases. Left ventricular EF values obtained with cine-MRF agreed with the conventional cine images (mean bias -1.0%). Average myocardial T1 times in diastole/systole were 1398/1391 ms with cine-MRF, 1394/1378 ms with ECG-triggered cardiac MRF (cMRF) and 1234/1212 ms with MOLLI; and T2 values were 30.7/30.3 ms with cine-MRF, 32.6/32.9 ms with ECG-triggered cMRF and 37.6/41.0 ms with T2 -prepared FLASH. Cine-MRF and ECG-triggered cMRF relaxation times were in good agreement. Cine-MRF T1 values were significantly longer than MOLLI, and cine-MRF T2 values were significantly shorter than T2 -prepared FLASH. In summary, cine-MRF can potentially streamline cardiac MRI exams by combining left ventricle functional assessment and T1 -T2 mapping into one time-efficient acquisition.
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Affiliation(s)
- Jesse I. Hamilton
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Corresponding author at 1137 Catherine Street, Room 1590B, Ann Arbor, MI 48109, JI Hamilton –
| | - Yun Jiang
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Brendan Eck
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Mark Griswold
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
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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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23
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Nolte T, Gross‐Weege N, Doneva M, Koken P, Elevelt A, Truhn D, Kuhl C, Schulz V. Spiral blurring correction with water–fat separation for magnetic resonance fingerprinting in the breast. Magn Reson Med 2019; 83:1192-1207. [DOI: 10.1002/mrm.27994] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 08/23/2019] [Accepted: 08/23/2019] [Indexed: 12/12/2022]
Affiliation(s)
- Teresa Nolte
- Physics of Molecular Imaging Systems Experimental Molecular Imaging RWTH Aachen University Aachen Germany
| | - Nicolas Gross‐Weege
- Physics of Molecular Imaging Systems Experimental Molecular Imaging RWTH Aachen University Aachen Germany
| | - Mariya Doneva
- Tomographic Imaging Systems Philips Research Europe Hamburg Germany
| | - Peter Koken
- Tomographic Imaging Systems Philips Research Europe Hamburg Germany
| | - Aaldert Elevelt
- Oncology Solutions Philips Research Europe Eindhoven The Netherlands
| | - Daniel Truhn
- Clinic for Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany
| | - Christiane Kuhl
- Clinic for Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany
| | - Volkmar Schulz
- Physics of Molecular Imaging Systems Experimental Molecular Imaging RWTH Aachen University Aachen Germany
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24
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Panda A, O’Connor G, Lo WC, Jiang Y, Margevicius S, Schluchter M, Ponsky LE, Gulani V. Targeted Biopsy Validation of Peripheral Zone Prostate Cancer Characterization With Magnetic Resonance Fingerprinting and Diffusion Mapping. Invest Radiol 2019; 54:485-493. [PMID: 30985480 PMCID: PMC6602844 DOI: 10.1097/rli.0000000000000569] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE This study aims for targeted biopsy validation of magnetic resonance fingerprinting (MRF) and diffusion mapping for characterizing peripheral zone (PZ) prostate cancer and noncancers. MATERIALS AND METHODS One hundred four PZ lesions in 85 patients who underwent magnetic resonance imaging were retrospectively analyzed with apparent diffusion coefficient (ADC) mapping, MRF, and targeted biopsy (cognitive or in-gantry). A radiologist blinded to pathology drew regions of interest on targeted lesions and visually normal peripheral zone on MRF and ADC maps. Mean T1, T2, and ADC were analyzed using linear mixed models. Generalized estimating equations logistic regression analyses were used to evaluate T1 and T2 relaxometry combined with ADC in differentiating pathologic groups. RESULTS Targeted biopsy revealed 63 cancers (low-grade cancer/Gleason score 6 = 10, clinically significant cancer/Gleason score ≥7 = 53), 15 prostatitis, and 26 negative biopsies. Prostate cancer T1, T2, and ADC (mean ± SD, 1660 ± 270 milliseconds, 56 ± 20 milliseconds, 0.70 × 10 ± 0.24 × 10 mm/s) were significantly lower than prostatitis (mean ± SD, 1730 ± 350 milliseconds, 77 ± 36 milliseconds, 1.00 × 10 ± 0.30 × 10 mm/s) and negative biopsies (mean ± SD, 1810 ± 250 milliseconds, 71 ± 37 milliseconds, 1.00 × 10 ± 0.33 × 10 mm/s). For cancer versus prostatitis, ADC was sensitive and T2 specific with comparable area under curve (AUC; (AUCT2 = 0.71, AUCADC = 0.79, difference between AUCs not significant P = 0.37). T1 + ADC (AUCT1 + ADC = 0.83) provided the best separation between cancer and negative biopsies. Low-grade cancer T2 and ADC (mean ± SD, 75 ± 29 milliseconds, 0.96 × 10 ± 0.34 × 10 mm/s) were significantly higher than clinically significant cancers (mean ± SD, 52 ± 16 milliseconds, 0.65 ± 0.18 × 10 mm/s), and T2 + ADC (AUCT2 + ADC = 0.91) provided the best separation. CONCLUSIONS T1 and T2 relaxometry combined with ADC mapping may be useful for quantitative characterization of prostate cancer grades and differentiating cancer from noncancers for PZ lesions seen on T2-weighted images.
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Affiliation(s)
- Ananya Panda
- Department of Radiology, Mayo Clinic, Rochester, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Gregory O’Connor
- Department of Case Western University School of Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Wei-Ching Lo
- Department of Biomedical Engineering, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Yun Jiang
- Department of Radiology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Seunghee Margevicius
- Department of Epidemiology and Biostatistics, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Mark Schluchter
- Department of Epidemiology and Biostatistics, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Lee E. Ponsky
- Department of Urology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Vikas Gulani
- Department of Case Western University School of Medicine, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
- Department of Biomedical Engineering, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
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25
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Poorman ME, Martin MN, Ma D, McGivney DF, Gulani V, Griswold MA, Keenan KE. Magnetic resonance fingerprinting Part 1: Potential uses, current challenges, and recommendations. J Magn Reson Imaging 2019; 51:675-692. [PMID: 31264748 DOI: 10.1002/jmri.26836] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 05/31/2019] [Indexed: 12/11/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a powerful quantitative MRI technique capable of acquiring multiple property maps simultaneously in a short timeframe. The MRF framework has been adapted to a wide variety of clinical applications, but faces challenges in technical development, and to date has only demonstrated repeatability and reproducibility in small studies. In this review, we discuss the current implementations of MRF and their use in a clinical setting. Based on this analysis, we highlight areas of need that must be addressed before MRF can be fully adopted into the clinic and make recommendations to the MRF community on standardization and validation strategies of MRF techniques. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:675-692.
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Affiliation(s)
- Megan E. Poorman
- Department of PhysicsUniversity of Colorado Boulder Boulder Colorado USA
- Physical Measurement LaboratoryNational Institute of Standards and Technology Boulder Colorado USA
| | - Michele N. Martin
- Physical Measurement LaboratoryNational Institute of Standards and Technology Boulder Colorado USA
| | - Dan Ma
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Debra F. McGivney
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Vikas Gulani
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Mark A. Griswold
- Department of RadiologyCase Western Reserve University Cleveland Ohio USA
| | - Kathryn E. Keenan
- Physical Measurement LaboratoryNational Institute of Standards and Technology Boulder Colorado USA
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Gómez PA, Molina-Romero M, Buonincontri G, Menzel MI, Menze BH. Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging. Sci Rep 2019; 9:8468. [PMID: 31186480 PMCID: PMC6560213 DOI: 10.1038/s41598-019-44832-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 05/22/2019] [Indexed: 02/01/2023] Open
Abstract
Magnetic resonance imaging (MRI) has evolved into an outstandingly versatile diagnostic modality, as it has the ability to non-invasively produce detailed information on a tissue's structure and function. Complementary data is normally obtained in separate measurements, either as contrast-weighted images, which are fast and simple to acquire, or as quantitative parametric maps, which offer an absolute quantification of underlying biophysical effects, such as relaxation times or flow. Here, we demonstrate how to acquire and reconstruct data in a transient-state with a dual purpose: 1 - to generate contrast-weighted images that can be adjusted to emphasise clinically relevant image biomarkers; exemplified with signal modulation according to flow to obtain angiography information, and 2 - to simultaneously infer multiple quantitative parameters with a single, highly accelerated acquisition. This is achieved by introducing three novel elements: a model that accounts for flowing blood, a method for sequence design using smooth flip angle excitation patterns that incorporates both parameter encoding and signal contrast, and the reconstruction of temporally resolved contrast-weighted images. From these images we simultaneously obtain angiography projections and multiple quantitative maps. By doing so, we increase the amount of clinically relevant data without adding measurement time, creating new dimensions for biomarker exploration and adding value to MR examinations for patients and clinicians alike.
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MR fingerprinting with simultaneous T 1, T 2, and fat signal fraction estimation with integrated B 0 correction reduces bias in water T 1 and T 2 estimates. Magn Reson Imaging 2019; 60:7-19. [PMID: 30910696 DOI: 10.1016/j.mri.2019.03.017] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 03/15/2019] [Accepted: 03/19/2019] [Indexed: 12/26/2022]
Abstract
PURPOSE MR fingerprinting (MRF) sequences permit efficient T1 and T2 estimation in cranial and extracranial regions, but these areas may include substantial fat signals that bias T1 and T2 estimates. MRI fat signal fraction estimation is also a topic of active research in itself, but may be complicated by B0 heterogeneity and blurring during spiral k-space acquisitions, which are commonly used for MRF. An MRF method is proposed that separates fat and water signals, estimates water T1 and T2, and accounts for B0 effects with spiral blurring correction, in a single sequence. THEORY AND METHODS A k-space-based fat-water separation method is further extended to unbalanced steady-state free precession MRF with swept echo time. Repeated application of this k-space fat-water separation to demodulated forms of the measured data allows a B0 map and correction to be approximated. The method is compared with MRF without fat separation across a broad range of fat signal fractions (FSFs), water T1s and T2s, and under heterogeneous static fields in simulations, phantoms, and in vivo. RESULTS The proposed method's FSF estimates had a concordance correlation coefficient of 0.990 with conventional measurements, and reduced biases in the T1 and T2 estimates due to fat signal relative to other MRF sequences by several hundred ms. The B0 correction improved the FSF, T1, and T2 estimation compared to those estimates without correction. CONCLUSION The proposed method improves MRF water T1 and T2 estimation in the presence of fat and provides accurate FSF estimation with inline B0 correction.
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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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Effect of spiral undersampling patterns on FISP MRF parameter maps. Magn Reson Imaging 2019; 62:174-180. [PMID: 30654162 DOI: 10.1016/j.mri.2019.01.011] [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: 10/17/2018] [Revised: 01/11/2019] [Accepted: 01/12/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE Artifacts arising from undersampling are not always treatable as incoherent noise for the pattern matching process in Magnetic Resonance Fingerprinting (MRF). To estimate the effect of undersampling artifacts on MRF quantitative results, spiral sampling trajectories and their temporal variation is examined. METHODS The effect of sampling trajectories and their variation during the MRF experiment was assessed by characterizing aliasing artifacts. Temporal rearrangements of sampling trajectories were tested and evaluated in simulations and scans of phantoms and in a volunteer brain. RESULTS Results show that some temporal variations of sampling patterns can lead to spatial biases in MRF parameter maps. Observed effects are consistent with derived performance indicators for different interleaving schemes, leading to substantially improved MRF sampling patterns. CONCLUSION With the help of the presented simulation framework, MRF implementations can be investigated and improved. This was demonstrated for a spiral FISP (Fast imaging with steady-state free precession) MRF implementation, where a significantly improved interleaving scheme was identified, and confirmed by experiment.
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Cencini M, Biagi L, Kaggie JD, Schulte RF, Tosetti M, Buonincontri G. Magnetic resonance fingerprinting with dictionary-based fat and water separation (DBFW MRF): A multi-component approach. Magn Reson Med 2018; 81:3032-3045. [PMID: 30578569 PMCID: PMC6590362 DOI: 10.1002/mrm.27628] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 10/04/2018] [Accepted: 11/14/2018] [Indexed: 12/20/2022]
Abstract
Purpose To obtain a fast and robust fat‐water separation with simultaneous estimation of water T1, fat T1, and fat fraction maps. Methods We modified an MR fingerprinting (MRF) framework to use a single dictionary combination of a water and fat dictionary. A variable TE acquisition pattern with maximum TE = 4.8 ms was used to increase the fat–water separability. Radiofrequency (RF) spoiling was used to reduce the size of the dictionary by reducing T2 sensitivity. The technique was compared both in vitro and in vivo to an MRF method that incorporated 3‐point Dixon (DIXON MRF), as well as Cartesian IDEAL with different acquisition parameters. Results The proposed dictionary‐based fat–water separation technique (DBFW MRF) successfully provided fat fraction, water, and fat T1, B0, and B1+ maps both in vitro and in vivo. The fat fraction and water T1 values obtained with DBFW MRF show excellent agreement with DIXON MRF as well as with the reference values obtained using a Cartesian IDEAL with a long TR (concordance correlation coefficient: 0.97/0.99 for fat fraction–water T1). Whereas fat fraction values with Cartesian IDEAL were degraded in the presence of T1 saturation, MRF methods successfully estimated and accounted for T1 in the fat fraction estimates. Conclusion The DBFW MRF technique can successfully provide T1 and fat fraction quantification in under 20 s per slice, intrinsically correcting T1 biases typical of fast Dixon techniques. These features could improve the diagnostic quality and use of images in presence of fat.
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Affiliation(s)
- Matteo Cencini
- Department of Physics, University of Pisa, Pisa, Italy.,IMAGO7 Foundation, Pisa, Italy
| | - Laura Biagi
- IMAGO7 Foundation, Pisa, Italy.,IRCCS Stella Maris Scientific Institute, Pisa, Italy
| | - Joshua D Kaggie
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | | | - Michela Tosetti
- IMAGO7 Foundation, Pisa, Italy.,IRCCS Stella Maris Scientific Institute, Pisa, Italy
| | - Guido Buonincontri
- IMAGO7 Foundation, Pisa, Italy.,Istituto Nazionale di Fisica Nucleare (INFN), Pisa, Italy
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31
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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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Rieger B, Akçakaya M, Pariente JC, Llufriu S, Martinez-Heras E, Weingärtner S, Schad LR. Time efficient whole-brain coverage with MR Fingerprinting using slice-interleaved echo-planar-imaging. Sci Rep 2018; 8:6667. [PMID: 29703978 PMCID: PMC5923901 DOI: 10.1038/s41598-018-24920-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 04/12/2018] [Indexed: 01/18/2023] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a promising method for fast simultaneous quantification of multiple tissue parameters. The objective of this study is to improve the coverage of MRF based on echo-planar imaging (MRF-EPI) by using a slice-interleaved acquisition scheme. For this, the MRF-EPI is modified to acquire several slices in a randomized interleaved manner, increasing the effective repetition time of the spoiled gradient echo readout acquisition in each slice. Per-slice matching of the signal-trace to a precomputed dictionary allows the generation of T1 and T2* maps with integrated B1+ correction. Subsequent compensation for the coil sensitivity profile and normalization to the cerebrospinal fluid additionally allows for quantitative proton density (PD) mapping. Numerical simulations are performed to optimize the number of interleaved slices. Quantification accuracy is validated in phantom scans and feasibility is demonstrated in-vivo. Numerical simulations suggest the acquisition of four slices as a trade-off between quantification precision and scan-time. Phantom results indicate good agreement with reference measurements (Difference T1: -2.4 ± 1.1%, T2*: -0.5 ± 2.5%, PD: -0.5 ± 7.2%). In-vivo whole-brain coverage of T1, T2* and PD with 32 slices was acquired within 3:36 minutes, resulting in parameter maps of high visual quality and comparable performance with single-slice MRF-EPI at 4-fold scan-time reduction.
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Affiliation(s)
- Benedikt Rieger
- Computer Assisted Clinical Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Mehmet Akçakaya
- Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - José C Pariente
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Sara Llufriu
- Center of Neuroimmunology. Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona and Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Eloy Martinez-Heras
- Center of Neuroimmunology. Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona and Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Sebastian Weingärtner
- Computer Assisted Clinical Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
- Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States.
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States.
| | - Lothar R Schad
- Computer Assisted Clinical Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany
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Ma D, Jiang Y, Chen Y, McGivney D, Mehta B, Gulani V, Griswold M. Fast 3D magnetic resonance fingerprinting for a whole-brain coverage. Magn Reson Med 2018; 79:2190-2197. [PMID: 28833436 PMCID: PMC5868964 DOI: 10.1002/mrm.26886] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 07/19/2017] [Accepted: 08/03/2017] [Indexed: 12/24/2022]
Abstract
PURPOSE The purpose of this study was to accelerate the acquisition and reconstruction time of 3D magnetic resonance fingerprinting scans. METHODS A 3D magnetic resonance fingerprinting scan was accelerated by using a single-shot spiral trajectory with an undersampling factor of 48 in the x-y plane, and an interleaved sampling pattern with an undersampling factor of 3 through plane. Further acceleration came from reducing the waiting time between neighboring partitions. The reconstruction time was accelerated by applying singular value decomposition compression in k-space. Finally, a 3D premeasured B1 map was used to correct for the B1 inhomogeneity. RESULTS The T1 and T2 values of the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI phantom showed a good agreement with the standard values, with an average concordance correlation coefficient of 0.99, and coefficient of variation of 7% in the repeatability scans. The results from in vivo scans also showed high image quality in both transverse and coronal views. CONCLUSIONS This study applied a fast acquisition scheme for a fully quantitative 3D magnetic resonance fingerprinting scan with a total acceleration factor of 144 as compared with the Nyquist rate, such that 3D T1 , T2 , and proton density maps can be acquired with whole-brain coverage at clinical resolution in less than 5 min. Magn Reson Med 79:2190-2197, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, OH
| | - Yun Jiang
- Department of Radiology, Case Western Reserve University, Cleveland, OH
| | - Yong Chen
- Department of Radiology, Case Western Reserve University, Cleveland, OH
| | - Debra McGivney
- Department of Radiology, Case Western Reserve University, Cleveland, OH
| | - Bhairav Mehta
- Department of Radiology, Case Western Reserve University, Cleveland, OH
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, OH
| | - Mark Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, OH
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Liao C, Bilgic B, Manhard MK, Zhao B, Cao X, Zhong J, Wald LL, Setsompop K. 3D MR fingerprinting with accelerated stack-of-spirals and hybrid sliding-window and GRAPPA reconstruction. Neuroimage 2017; 162:13-22. [PMID: 28842384 PMCID: PMC6031129 DOI: 10.1016/j.neuroimage.2017.08.030] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 07/02/2017] [Accepted: 08/09/2017] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Whole-brain high-resolution quantitative imaging is extremely encoding intensive, and its rapid and robust acquisition remains a challenge. Here we present a 3D MR fingerprinting (MRF) acquisition with a hybrid sliding-window (SW) and GRAPPA reconstruction strategy to obtain high-resolution T1, T2 and proton density (PD) maps with whole brain coverage in a clinically feasible timeframe. METHODS 3D MRF data were acquired using a highly under-sampled stack-of-spirals trajectory with a steady-state precession (FISP) sequence. For data reconstruction, kx-ky under-sampling was mitigated using SW combination along the temporal axis. Non-uniform fast Fourier transform (NUFFT) was then applied to create Cartesian k-space data that are fully-sampled in the in-plane direction, and Cartesian GRAPPA was performed to resolve kz under-sampling to create an alias-free SW dataset. T1, T2 and PD maps were then obtained using dictionary matching. RESULTS Phantom study demonstrated that the proposed 3D-MRF acquisition/reconstruction method is able to produce quantitative maps that are consistent with conventional quantification techniques. Retrospectively under-sampled in vivo acquisition revealed that SW + GRAPPA substantially improves quantification accuracy over the current state-of-the-art accelerated 3D MRF. Prospectively under-sampled in vivo study showed that whole brain T1, T2 and PD maps with 1 mm3 resolution could be obtained in 7.5 min. CONCLUSIONS 3D MRF stack-of-spirals acquisition with hybrid SW + GRAPPA reconstruction may provide a feasible approach for rapid, high-resolution quantitative whole-brain imaging.
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Affiliation(s)
- Congyu Liao
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Mary Kate Manhard
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Bo Zhao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Xiaozhi Cao
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
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35
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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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