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Jin J, Zhou Y, Chen L, Chen Z. Ultrafast T 2 and T 2* mapping using single-shot spatiotemporally encoded MRI with reduced field of view and spiral out-in-out-in trajectory. Med Phys 2024. [PMID: 38896823 DOI: 10.1002/mp.17268] [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: 01/09/2024] [Revised: 05/15/2024] [Accepted: 06/11/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND T2 and T2* mapping are crucial components of quantitative magnetic resonance imaging, offering valuable insights into tissue characteristics and pathology. Single-shot methods can achieve ultrafast T2 or T2* mapping by acquiring multiple readout echo trains. However, the extended echo trains pose challenges, such as compromised image quality and diminished quantification accuracy. PURPOSE In this study, we develop a single-shot method for ultrafast T2 and T2* mapping with reduced echo train length. METHODS The proposed method is based on ultrafast single-shot spatiotemporally encoded (SPEN) MRI combined with reduced field of view (FOV) and spiral out-in-out-in (OIOI) trajectory. Specifically, a biaxial SPEN excitation scheme was employed to excite the spin signal into the spatiotemporal encoding domain. The OIOI trajectory with high acquisition efficiency was employed to acquire signals within targeted reduced FOV. Through non-Cartesian super-resolved (SR) reconstruction, 12 aliasing-free images with different echo times were obtained within 150 ms. These images were subsequently fitted to generate T2 or T2* mapping simultaneously using a derived model. RESULTS Accurate and co-registered T2 and T2* maps were generated, closely resembling the reference maps. Numerical simulations demonstrated substantial consistency (R2 > 0.99) with the ground truth values. A mean difference of 0.6% and 1.7% was observed in T2 and T2*, respectively, in in vivo rat brain experiments compared to the reference. Moreover, the proposed method successfully obtained T2 and T2* mappings of rat kidney in free-breathing mode, demonstrating its superiority over multishot methods lacking respiratory navigation. CONCLUSIONS The results suggest that the proposed method can achieve ultrafast and accurate T2 and T2* mapping, potentially facilitating the application of T2 and T2* mapping in scenarios requiring high temporal resolution.
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Affiliation(s)
- Junxian Jin
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Yang Zhou
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Lin Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China
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Tao Y, Lv Z, Liu W, Qi H, Hu P. Recurrent neural network-based simultaneous cardiac T1, T2, and T1ρ mapping. NMR IN BIOMEDICINE 2024:e5133. [PMID: 38520183 DOI: 10.1002/nbm.5133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 03/25/2024]
Abstract
The purpose of the current study was to explore the feasibility of training a deep neural network to accelerate the process of generating T1, T2, and T1ρ maps for a recently proposed free-breathing cardiac multiparametric mapping technique, where a recurrent neural network (RNN) was utilized to exploit the temporal correlation among the multicontrast images. The RNN-based model was developed for rapid and accurate T1, T2, and T1ρ estimation. Bloch simulation was performed to simulate a dataset of more than 10 million signals and time correspondences with different noise levels for network training. The proposed RNN-based method was compared with a dictionary-matching method and a conventional mapping method to evaluate the model's effectiveness in phantom and in vivo studies at 3 T, respectively. In phantom studies, the RNN-based method and the dictionary-matching method achieved similar accuracy and precision in T1, T2, and T1ρ estimations. In in vivo studies, the estimated T1, T2, and T1ρ values obtained by the two methods achieved similar accuracy and precision for 10 healthy volunteers (T1: 1228.70 ± 53.80 vs. 1228.34 ± 52.91 ms, p > 0.1; T2: 40.70 ± 2.89 vs. 41.19 ± 2.91 ms, p > 0.1; T1ρ: 45.09 ± 4.47 vs. 45.23 ± 4.65 ms, p > 0.1). The RNN-based method can generate cardiac multiparameter quantitative maps simultaneously in just 2 s, achieving 60-fold acceleration compared with the dictionary-matching method. The RNN-accelerated method offers an almost instantaneous approach for reconstructing accurate T1, T2, and T1ρ maps, being much more efficient than the dictionary-matching method for the free-breathing multiparametric cardiac mapping technique, which may pave the way for inline mapping in clinical applications.
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Affiliation(s)
- Yiming Tao
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Zhenfeng Lv
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Wenjian Liu
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Haikun Qi
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
- Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China
| | - Peng Hu
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
- Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China
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Christodoulou AG, Cruz G, Arami A, Weingärtner S, Artico J, Peters D, Seiberlich N. The future of cardiovascular magnetic resonance: All-in-one vs. real-time (Part 1). J Cardiovasc Magn Reson 2024; 26:100997. [PMID: 38237900 DOI: 10.1016/j.jocmr.2024.100997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 01/10/2024] [Indexed: 02/26/2024] Open
Abstract
Cardiovascular magnetic resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically "all-in-one" CMR, where several contrasts and/or motion states are acquired simultaneously, and "real-time" CMR, in which the examination is accelerated to avoid the need for breathholding and/or cardiac gating. The goal of this two-part manuscript is to describe these two different types of emerging rapid CMR. To this end, the vision of each is described, along with techniques which have been devised and tested along the pathway of clinical implementation. The pros and cons of the different methods are presented, and the remaining open needs of each are detailed. Part 1 will tackle the "all-in-one" approaches, and Part 2 the "real-time" approaches along with an overall summary of these emerging methods.
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Affiliation(s)
- Anthony G Christodoulou
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Gastao Cruz
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Ayda Arami
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands; Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | | | - Dana Peters
- Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Nicole Seiberlich
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
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Rashid I, Al-Kindi S, Rajagopalan V, Walker J, Rajagopalan S, Seiberlich N, Hamilton JI. Synthetic multi-contrast late gadolinium enhancement imaging using post-contrast magnetic resonance fingerprinting. NMR IN BIOMEDICINE 2024; 37:e5043. [PMID: 37740596 PMCID: PMC10841227 DOI: 10.1002/nbm.5043] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 08/16/2023] [Accepted: 09/02/2023] [Indexed: 09/24/2023]
Abstract
Late gadolinium enhancement (LGE) MRI is the non-invasive reference standard for identifying myocardial scar and fibrosis but has limitations, including difficulty delineating subendocardial scar and operator dependence on image quality. The purpose of this work is to assess the feasibility of generating multi-contrast synthetic LGE images from post-contrast T1 and T2 maps acquired using magnetic resonance fingerprinting (MRF). Fifteen consecutive patients with a history of prior ischemic cardiomyopathy (12 men; mean age 63 ± 13 years) were prospectively scanned at 1.5 T between Oct 2020 and May 2021 using conventional LGE and MRF after injection of gadolinium contrast. Three classes of synthetic LGE images were derived from MRF post-contrast T1 and T2 maps: bright-blood phase-sensitive inversion recovery (PSIR), black- and gray-blood T2 -prepared PSIR (T2 -PSIR), and a novel "tissue-optimized" image to enhance differentiation among scar, viable myocardium, and blood. Image quality was assessed on a 1-5 Likert scale by two cardiologists, and contrast was quantified as the mean absolute difference (MAD) in pixel intensities between two tissues, with different methods compared using Kruskal-Wallis with Bonferroni post hoc tests. Per-patient and per-segment scar detection rates were evaluated using conventional LGE images as reference. Image quality scores were highest for synthetic PSIR (4.0) and reference images (3.8), followed by synthetic tissue-optimized (3.3), gray-blood T2 -PSIR (3.0), and black-blood T2 -PSIR (2.6). Among synthetic images, PSIR yielded the highest myocardium/scar contrast (MAD = 0.42) but the lowest blood/scar contrast (MAD = 0.05), and vice versa for T2 -PSIR, while tissue-optimized images achieved a balance among all tissues (myocardium/scar MAD = 0.16, blood/scar MAD = 0.26, myocardium/blood MAD = 0.10). Based on reference mid-ventricular LGE scans, 13/15 patients had myocardial scar. The per-patient sensitivity/accuracy for synthetic images were the following: PSIR, 85/87%; black-blood T2 -PSIR, 62/53%; gray-blood T2 -PSIR, 100/93%; tissue optimized, 100/93%. Synthetic multi-contrast LGE images can be generated from post-contrast MRF data without additional scan time, with initial feasibility shown in ischemic cardiomyopathy patients.
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Affiliation(s)
- Imran Rashid
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Sadeer Al-Kindi
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Varun Rajagopalan
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Jonathan Walker
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Sanjay Rajagopalan
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jesse I. Hamilton
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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Rashid I, Cruz G, Seiberlich N, Hamilton JI. Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications. J Magn Reson Imaging 2023. [PMID: 38153855 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, Michigan, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Gastao Cruz
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, Ohio, USA
| | - Nicole Seiberlich
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, Ohio, USA
| | - Jesse I Hamilton
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, Ohio, USA
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Lyu Z, Hua S, Xu J, Shen Y, Guo R, Hu P, Qi H. Free-breathing simultaneous native myocardial T1, T2 and T1ρ mapping with Cartesian acquisition and dictionary matching. J Cardiovasc Magn Reson 2023; 25:63. [PMID: 37946191 PMCID: PMC10636995 DOI: 10.1186/s12968-023-00973-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 10/20/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND T1, T2 and T1ρ are well-recognized parameters for quantitative cardiac MRI. Simultaneous estimation of these parameters allows for comprehensive myocardial tissue characterization, such as myocardial fibrosis and edema. However, conventional techniques either quantify the parameters individually with separate breath-hold acquisitions, which may result in unregistered parameter maps, or estimate multiple parameters in a prolonged breath-hold acquisition, which may be intolerable to patients. We propose a free-breathing multi-parametric mapping (FB-MultiMap) technique that provides co-registered myocardial T1, T2 and T1ρ maps in a single efficient acquisition. METHODS The proposed FB-MultiMap performs electrocardiogram-triggered single-shot Cartesian acquisition over 16 consecutive cardiac cycles, where inversion, T2 and T1ρ preparations are introduced for varying contrasts. A diaphragmatic navigator was used for prospective through-plane motion correction and the in-plane motion was corrected retrospectively with a group-wise image registration method. Quantitative mapping was conducted through dictionary matching of the motion corrected images, where the subject-specific dictionary was created using Bloch simulations for a range of T1, T2 and T1ρ values, as well as B1 factors to account for B1 inhomogeneities. The FB-MultiMap was optimized and validated in numerical simulations, phantom experiments, and in vivo imaging of 15 healthy subjects and six patients with suspected cardiac diseases. RESULTS The phantom T1, T2 and T1ρ values estimated with FB-MultiMap agreed well with reference measurements with no dependency on heart rate. In healthy subjects, FB-MultiMap T1 was higher than MOLLI T1 mapping (1218 ± 50 ms vs. 1166 ± 38 ms, p < 0.001). The myocardial T2 and T1ρ estimated with FB-MultiMap were lower compared to the mapping with T2- or T1ρ-prepared 2D balanced steady-state free precession (T2: 41.2 ± 2.8 ms vs. 42.5 ± 3.1 ms, p = 0.06; T1ρ: 45.3 ± 4.4 ms vs. 50.2 ± 4.0, p < 0.001). The pathological changes in myocardial parameters measured with FB-MultiMap were consistent with conventional techniques in all patients. CONCLUSION The proposed free-breathing multi-parametric mapping technique provides co-registered myocardial T1, T2 and T1ρ maps in 16 heartbeats, achieving similar mapping quality to conventional breath-hold mapping methods.
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Affiliation(s)
- Zhenfeng Lyu
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China
- Shanghai Clinical Research and Trial Center, Shanghai, China
| | - Sha Hua
- Department of Cardiovascular Medicine, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Xu
- UIH America, Inc., Houston, TX, USA
| | - Yiwen Shen
- Department of Cardiovascular Medicine, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rui Guo
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Peng Hu
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China.
- Shanghai Clinical Research and Trial Center, Shanghai, China.
| | - Haikun Qi
- School of Biomedical Engineering, ShanghaiTech University, 4th Floor, BME Building, 393 Middle Huaxia Road, Pudong District, Shanghai, 201210, China.
- Shanghai Clinical Research and Trial Center, Shanghai, China.
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Gaur S, Panda A, Fajardo JE, Hamilton J, Jiang Y, Gulani V. Magnetic Resonance Fingerprinting: A Review of Clinical Applications. Invest Radiol 2023; 58:561-577. [PMID: 37026802 PMCID: PMC10330487 DOI: 10.1097/rli.0000000000000975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
ABSTRACT Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance imaging that allows for efficient simultaneous measurements of multiple tissue properties, which are then used to create accurate and reproducible quantitative maps of these properties. As the technique has gained popularity, the extent of preclinical and clinical applications has vastly increased. The goal of this review is to provide an overview of currently investigated preclinical and clinical applications of MRF, as well as future directions. Topics covered include MRF in neuroimaging, neurovascular, prostate, liver, kidney, breast, abdominal quantitative imaging, cardiac, and musculoskeletal applications.
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Affiliation(s)
- Sonia Gaur
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Ananya Panda
- All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | | | - Jesse Hamilton
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Yun Jiang
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Vikas Gulani
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
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Liu Y, Hamilton J, Jiang Y, Seiberlich N. Assessment of MRF for simultaneous T 1 and T 2 quantification and water-fat separation in the liver at 0.55 T. MAGMA (NEW YORK, N.Y.) 2023; 36:513-523. [PMID: 36574163 PMCID: PMC10293475 DOI: 10.1007/s10334-022-01057-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 11/10/2022] [Accepted: 12/13/2022] [Indexed: 12/28/2022]
Abstract
OBJECTIVE The goal of this work was to assess the feasibility of performing MRF in the liver on a 0.55 T scanner and to examine the feasibility of water-fat separation using rosette MRF at 0.55 T. MATERIALS AND METHODS Spiral and rosette MRF sequences were implemented on a commercial 0.55 T scanner. The accuracy of both sequences in T1 and T2 quantification was validated in the ISMRM/NIST system phantom. The efficacy of rosette MRF in water-fat separation was evaluated in simulations and water/oil phantoms. Both spiral and rosette MRF were performed in the liver of healthy subjects. RESULTS In the ISMRM/NIST phantom, both spiral and rosette MRF achieved good agreement with reference values in T1 and T2 measurements. In addition, rosette MRF enables water-fat separation and can generate water- and fat- specific T1 maps, T2 maps, and proton density images from the same dataset for a spatial resolution of 1.56 × 1.56 × 5mm3 within the acquisition time of 15 s. CONCLUSION It is feasible to measure T1 and T2 simultaneously in the liver using MRF on a 0.55 T system with lower performance gradients compared to state-of-the-art 1.5 T and 3 T systems within an acquisition time of 15 s. In addition, rosette MRF enables water-fat separation along with T1 and T2 quantification with no time penalty.
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Affiliation(s)
- Yuchi Liu
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA.
| | - Jesse Hamilton
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Yun Jiang
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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Wang X, Rosenzweig S, Roeloffs V, Blumenthal M, Scholand N, Tan Z, Holme HCM, Unterberg-Buchwald C, Hinkel R, Uecker M. Free-breathing myocardial T 1 mapping using inversion-recovery radial FLASH and motion-resolved model-based reconstruction. Magn Reson Med 2023; 89:1368-1384. [PMID: 36404631 PMCID: PMC9892313 DOI: 10.1002/mrm.29521] [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: 11/17/2021] [Revised: 09/22/2022] [Accepted: 10/20/2022] [Indexed: 11/22/2022]
Abstract
PURPOSE To develop a free-breathing myocardialT 1 $$ {\mathrm{T}}_1 $$ mapping technique using inversion-recovery (IR) radial fast low-angle shot (FLASH) and calibrationless motion-resolved model-based reconstruction. METHODS Free-running (free-breathing, retrospective cardiac gating) IR radial FLASH is used for data acquisition at 3T. First, to reduce the waiting time between inversions, an analytical formula is derived that takes the incompleteT 1 $$ {\mathrm{T}}_1 $$ recovery into account for an accurateT 1 $$ {\mathrm{T}}_1 $$ calculation. Second, the respiratory motion signal is estimated from the k-space center of the contrast varying acquisition using an adapted singular spectrum analysis (SSA-FARY) technique. Third, a motion-resolved model-based reconstruction is used to estimate both parameter and coil sensitivity maps directly from the sorted k-space data. Thus, spatiotemporal total variation, in addition to the spatial sparsity constraints, can be directly applied to the parameter maps. Validations are performed on an experimental phantom, 11 human subjects, and a young landrace pig with myocardial infarction. RESULTS In comparison to an IR spin-echo reference, phantom results confirm goodT 1 $$ {\mathrm{T}}_1 $$ accuracy, when reducing the waiting time from 5 s to 1 s using the new correction. The motion-resolved model-based reconstruction further improvesT 1 $$ {\mathrm{T}}_1 $$ precision compared to the spatial regularization-only reconstruction. Aside from showing that a reliable respiratory motion signal can be estimated using modified SSA-FARY, in vivo studies demonstrate that dynamic myocardialT 1 $$ {\mathrm{T}}_1 $$ maps can be obtained within 2 min with good precision and repeatability. CONCLUSION Motion-resolved myocardialT 1 $$ {\mathrm{T}}_1 $$ mapping during free-breathing with good accuracy, precision and repeatability can be achieved by combining inversion-recovery radial FLASH, self-gating and a calibrationless motion-resolved model-based reconstruction.
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Affiliation(s)
- Xiaoqing Wang
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | - Sebastian Rosenzweig
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | - Volkert Roeloffs
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
| | - Moritz Blumenthal
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
| | - Nick Scholand
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
| | - Zhengguo Tan
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | | | - Christina Unterberg-Buchwald
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
| | - Rabea Hinkel
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
- Laboratory Animal Science Unit, Leibniz Institute for Primate Research, Deutsches Primatenzentrum GmbH, Göttingen, Germany
- Institute for Animal Hygiene, Animal Welfare and Farm Animal Behavior, University of Veterinary Medicine, Hannover, Germany
| | - Martin Uecker
- Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria
- Institute for Diagnostic and Interventional Radiology of the University Medical Center Göttingen, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Germany
- Cluster of “Excellence Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, Germany
- BioTechMed-Graz, Graz, Austria
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10
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Eck BL, Yim M, Hamilton JI, da Cruz GJL, Li X, Flamm SD, Tang WHW, Prieto C, Seiberlich N, Kwon DH. Cardiac Magnetic Resonance Fingerprinting: Potential Clinical Applications. Curr Cardiol Rep 2023; 25:119-131. [PMID: 36805913 PMCID: PMC10134477 DOI: 10.1007/s11886-022-01836-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/10/2022] [Indexed: 02/21/2023]
Abstract
PURPOSE OF REVIEW Cardiac magnetic resonance fingerprinting (cMRF) has developed as a technique for rapid, multi-parametric tissue property mapping that has potential to both improve cardiac MRI exam efficiency and expand the information captured. In this review, we describe the cMRF technique, summarize technical developments and in vivo reports, and highlight potential clinical applications. RECENT FINDINGS Technical developments in cMRF continue to progress rapidly, including motion compensated reconstruction, additional tissue property quantification, signal time course analysis, and synthetic LGE image generation. Such technical developments can enable simplified CMR protocols by combining multiple evaluations into a single protocol and reducing the number of breath-held scans. cMRF continues to be reported for use in a range of pathologies; however barriers to clinical implementation remain. Technical developments are described in this review, followed by a focus on potential clinical applications that they may support. Clinical translation of cMRF could shorten protocols, improve CMR accessibility, and provide additional information as compared to conventional cardiac parametric mapping methods. Current needs for clinical implementation are discussed, as well as how those needs may be met in order to bring cMRF from its current research setting to become a viable tool for patient care.
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Affiliation(s)
- Brendan L Eck
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Michael Yim
- Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jesse I Hamilton
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Gastao José Lima da Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, England, UK
| | - Xiaojuan Li
- Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Scott D Flamm
- Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - W H Wilson Tang
- Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, England, UK
- School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Deborah H Kwon
- Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA.
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11
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Eyre K, Lindsay K, Razzaq S, Chetrit M, Friedrich M. Simultaneous multi-parametric acquisition and reconstruction techniques in cardiac magnetic resonance imaging: Basic concepts and status of clinical development. Front Cardiovasc Med 2022; 9:953823. [PMID: 36277755 PMCID: PMC9582154 DOI: 10.3389/fcvm.2022.953823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/22/2022] [Indexed: 11/13/2022] Open
Abstract
Simultaneous multi-parametric acquisition and reconstruction techniques (SMART) are gaining attention for their potential to overcome some of cardiovascular magnetic resonance imaging's (CMR) clinical limitations. The major advantages of SMART lie within their ability to simultaneously capture multiple "features" such as cardiac motion, respiratory motion, T1/T2 relaxation. This review aims to summarize the overarching theory of SMART, describing key concepts that many of these techniques share to produce co-registered, high quality CMR images in less time and with less requirements for specialized personnel. Further, this review provides an overview of the recent developments in the field of SMART by describing how they work, the parameters they can acquire, their status of clinical testing and validation, and by providing examples for how their use can improve the current state of clinical CMR workflows. Many of the SMART are in early phases of development and testing, thus larger scale, controlled trials are needed to evaluate their use in clinical setting and with different cardiac pathologies.
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Affiliation(s)
- Katerina Eyre
- McGill University Health Centre, Montreal, QC, Canada,Department of Experimental Medicine, McGill University, Montreal, QC, Canada,*Correspondence: Katerina Eyre,
| | - Katherine Lindsay
- McGill University Health Centre, Montreal, QC, Canada,Department of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Saad Razzaq
- Department of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Michael Chetrit
- McGill University Health Centre, Montreal, QC, Canada,Department of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Matthias Friedrich
- McGill University Health Centre, Montreal, QC, Canada,Department of Experimental Medicine, McGill University, Montreal, QC, Canada
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12
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Liu Y, Hamilton J, Jiang Y, Seiberlich N. Cardiac MRF using rosette trajectories for simultaneous myocardial T1, T2, and proton density fat fraction mapping. Front Cardiovasc Med 2022; 9:977603. [PMID: 36204572 PMCID: PMC9530568 DOI: 10.3389/fcvm.2022.977603] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/25/2022] [Indexed: 11/22/2022] Open
Abstract
The goal of this work is to extend prior work on cardiac MR Fingerprinting (cMRF) using rosette k-space trajectories to enable simultaneous T1, T2, and proton density fat fraction (PDFF) mapping in the heart. A rosette trajectory designed for water-fat separation at 1.5T was used in a 2D ECG-triggered 15-heartbeat cMRF sequence. Water and fat specific T1 and T2 maps were generated from the cMRF data. A PDFF map was also retrieved using Hierarchical IDEAL by segmenting the rosette cMRF data into multiple echoes. The accuracy of rosette cMRF in T1, T2, and PDFF quantification was validated in the ISMRM/NIST phantom and an in-house built fat fraction phantom, respectively. The proposed method was also applied for myocardial tissue mapping of healthy subjects and cardiac patients at 1.5T. T1, T2, and PDFF values measured using rosette cMRF in the ISMRM/NIST phantom and the fat fraction phantom agreed well with the reference values. In 16 healthy subjects, rosette cMRF yielded T1 values which were 80~90 ms higher than spiral cMRF and MOLLI. T2 values obtained using rosette cMRF were ~3 ms higher than spiral cMRF and ~5 ms lower than conventional T2-prep bSSFP method. Rosette cMRF was also able to detect abnormal T1 and T2 values in cardiomyopathy patients and may provide more accurate maps due to effective fat suppression. In conclusion, this study shows that rosette cMRF has the potential for efficient cardiac tissue characterization through simultaneous quantification of myocardial T1, T2, and PDFF.
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Affiliation(s)
- Yuchi Liu
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- *Correspondence: Yuchi Liu
| | - Jesse Hamilton
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Yun Jiang
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
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13
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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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14
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Ojha V, Khalique OK, Khurana R, Lorenzatti D, Leung SW, Lawton B, Slesnick TC, Cavalcante JC, Ducci CB, Patel AR, Prieto CC, Plein S, Raman SV, Salerno M, Parwani P. Highlights of the Virtual Society for Cardiovascular Magnetic Resonance 2022 Scientific Conference: CMR: improving cardiovascular care around the world. J Cardiovasc Magn Reson 2022; 24:38. [PMID: 35725565 PMCID: PMC9207863 DOI: 10.1186/s12968-022-00870-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/08/2022] [Indexed: 11/25/2022] Open
Abstract
The 25th Society for Cardiovascular Magnetic Resonance (SCMR) Annual Scientific Sessions saw 1524 registered participants from more than 50 countries attending the meeting virtually. Supporting the theme "CMR: Improving Cardiovascular Care Around the World", the meeting included 179 invited talks, 52 sessions including 3 plenary sessions, 2 keynote talks, and a total of 93 cases and 416 posters. The sessions were designed so as to showcase the multifaceted role of cardiovascular magnetic resonance (CMR) in identifying and prognosticating various myocardial pathologies. Additionally, various social networking sessions as well as fun activities were organized. The major areas of focus for the future are likely to be rapid efficient and high value CMR exams, automated and quantitative acquisition and post-processing using artificial intelligence and machine learning, multi-contrast imaging and advanced vascular imaging including 4D flow.
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Affiliation(s)
- Vineeta Ojha
- All India Institute of Medical Sciences, New Delhi, India
| | | | | | | | - Steve W Leung
- Gill Heart and Vascular Institute, University of Kentucky, Lexington, KY, USA
| | | | | | | | | | - Amit R Patel
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Claudia C Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sven Plein
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Subha V Raman
- Indiana University Cardiovascular Institute and Krannert Cardiovascular Research Center, Indianapolis, IN, USA
| | - Michael Salerno
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Purvi Parwani
- Division of Cardiology, Department of Medicine, Loma Linda University Health, Loma Linda University Medical Center, Loma Linda, CA, USA.
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15
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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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16
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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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17
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Feasibility of Magnetic Resonance Fingerprinting on Aging MRI Hardware. Tomography 2021; 8:10-21. [PMID: 35076600 PMCID: PMC8788417 DOI: 10.3390/tomography8010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/15/2021] [Accepted: 12/17/2021] [Indexed: 11/17/2022] Open
Abstract
The purpose of this work is to evaluate the feasibility of performing magnetic resonance fingerprinting (MRF) on older and lower-performance MRI hardware as a means to bring advanced imaging to the aging MRI install base. Phantom and in vivo experiments were performed on a 1.5T Siemens Aera (installed 2015) and 1.5T Siemens Symphony (installed 2002). A 2D spiral MRF sequence for simultaneous T1/T2/M0 mapping was implemented on both scanners with different gradient trajectories to accommodate system specifications. In phantom, for T1/T2 values in a physiologically relevant range (T1: 195-1539 ms; T2: 20-267 ms), scanners had strong correlation (R2 > 0.999) with average absolute percent difference of 8.1% and 10.1%, respectively. Comparison of the two trajectories on the newer scanner showed differences of 2.6% (T1) and 10.9% (T2), suggesting a partial explanation of the observed inter-scanner bias. Inter-scanner agreement was better when the same trajectory was used, with differences of 6.0% (T1) and 4.0% (T2). Intra-scanner coefficient of variation (CV) of T1 and T2 estimates in phantom were <2.0% and in vivo were ≤3.5%. In vivo inter-scanner white matter CV was 4.8% (T1) and 5.1% (T2). White matter measurements on the aging scanner after two months were consistent, with differences of 1.9% (T1) and 3.9% (T2). In conclusion, MRF is feasible on an aging MRI scanner and required only changes to the gradient trajectory.
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18
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Abstract
Magnetic resonance fingerprinting (MRF) is increasingly being used to evaluate brain development and differentiate normal and pathologic tissues in children. MRF can provide reliable and accurate intrinsic tissue properties, such as T1 and T2 relaxation times. MRF is a powerful tool in evaluating brain disease in pediatric population. MRF is a new quantitative MR imaging technique for rapid and simultaneous quantification of multiple tissue properties.
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Affiliation(s)
- Sheng-Che Hung
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 2006 Old Clinic, CB#7510, 101 Manning Dr, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Marsico Hall, suite 1200, Chapel Hill, NC 27599, USA
| | - Yong Chen
- Department of Radiology, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA
| | - Pew-Thian Yap
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 2006 Old Clinic, CB#7510, 101 Manning Dr, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Marsico Hall, suite 1200, Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 2006 Old Clinic, CB#7510, 101 Manning Dr, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Marsico Hall, suite 1200, Chapel Hill, NC 27599, USA.
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19
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Feng L, Liu F, Soultanidis G, Liu C, Benkert T, Block KT, Fayad ZA, Yang Y. Magnetization-prepared GRASP MRI for rapid 3D T1 mapping and fat/water-separated T1 mapping. Magn Reson Med 2021; 86:97-114. [PMID: 33580909 PMCID: PMC8197608 DOI: 10.1002/mrm.28679] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/18/2020] [Accepted: 12/22/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE This study aimed to (i) develop Magnetization-Prepared Golden-angle RAdial Sparse Parallel (MP-GRASP) MRI using a stack-of-stars trajectory for rapid free-breathing T1 mapping and (ii) extend MP-GRASP to multi-echo acquisition (MP-Dixon-GRASP) for fat/water-separated (water-specific) T1 mapping. METHODS An adiabatic non-selective 180° inversion-recovery pulse was added to a gradient-echo-based golden-angle stack-of-stars sequence for magnetization-prepared 3D single-echo or 3D multi-echo acquisition. In combination with subspace-based GRASP-Pro reconstruction, the sequence allows for standard T1 mapping (MP-GRASP) or fat/water-separated T1 mapping (MP-Dixon-GRASP), respectively. The accuracy of T1 mapping using MP-GRASP was evaluated in a phantom and volunteers (brain and liver) against clinically accepted reference methods. The repeatability of T1 estimation was also assessed in the phantom and volunteers. The performance of MP-Dixon-GRASP for water-specific T1 mapping was evaluated in a fat/water phantom and volunteers (brain and liver). RESULTS ROI-based mean T1 values are correlated between the references and MP-GRASP in the phantom (R2 = 1.0), brain (R2 = 0.96), and liver (R2 = 0.73). MP-GRASP achieved good repeatability of T1 estimation in the phantom (R2 = 1.0), brain (R2 = 0.99), and liver (R2 = 0.82). Water-specific T1 is different from in-phase and out-of-phase composite T1 (composite T1 when fat and water signal are mixed in phase or out of phase) both in the phantom and volunteers. CONCLUSION This work demonstrated the initial performance of MP-GRASP and MP-Dixon-GRASP MRI for rapid 3D T1 mapping and 3D fat/water-separated T1 mapping in the brain (without motion) and in the liver (during free breathing). With fat/water-separated T1 estimation, MP-Dixon-GRASP could be potentially useful for imaging patients with fatty-liver diseases.
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Affiliation(s)
- Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Georgios Soultanidis
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chenyu Liu
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Benkert
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
| | - Kai Tobias Block
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
- Center for Advanced Imaging Innovation and Research (CAIR), New York University School of Medicine, New York, NY, USA
| | - Zahi A. Fayad
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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20
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Tippareddy C, Zhao W, Sunshine JL, Griswold M, Ma D, Badve C. Magnetic resonance fingerprinting: an overview. Eur J Nucl Med Mol Imaging 2021; 48:4189-4200. [PMID: 34037831 DOI: 10.1007/s00259-021-05384-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 04/25/2021] [Indexed: 12/17/2022]
Abstract
Magnetic resonance fingerprinting (MRF) is an evolving quantitative MRI framework consisting of unique data acquisition, processing, visualization, and interpretation steps. MRF is capable of simultaneously producing multiple high-resolution property maps including T1, T2, M0, ADC, and T2* measurements. While a relatively new technology, MRF has undergone rapid development for a variety of clinical applications from brain tumor characterization and epilepsy imaging to characterization of prostate cancer, cardiac imaging, among others. This paper will provide a brief overview of current state of MRF technology including highlights of technical and clinical advances. We will conclude with a brief discussion of the challenges that need to be overcome to establish MRF as a quantitative imaging biomarker.
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Affiliation(s)
- Charit Tippareddy
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Walter Zhao
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Jeffrey L Sunshine
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Mark Griswold
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA.,Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA.
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21
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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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