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Zhang X, de Moura HL, Monga A, Zibetti MVW, Kijowski R, Regatte RR. Repeatability of Quantitative Knee Cartilage T 1, T 2, and T 1ρ Mapping With 3D-MRI Fingerprinting. J Magn Reson Imaging 2024; 60:688-699. [PMID: 37885320 PMCID: PMC11045656 DOI: 10.1002/jmri.29068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Three-dimensional MR fingerprinting (3D-MRF) techniques have been recently described for simultaneous multiparametric mapping of knee cartilage. However, investigation of repeatability remains limited. PURPOSE To assess the intra-day and inter-day repeatabilities of knee cartilage T1, T2, and T1ρ maps using a 3D-MRF sequence for simultaneous measurement. STUDY TYPE Prospective. SUBJECTS Fourteen healthy subjects (35.4 ± 9.3 years, eight males), scanned on Day 1 and Day 7. FIELD STRENGTH/SEQUENCE 3 T/3D-MRF, T1, T2, and T1ρ maps. ASSESSMENT The acquisition of 3D-MRF cartilage (simultaneous acquisition of T1, T2, and T1ρ maps) were acquired using a dictionary pattern-matching approach. Conventional cartilage T1, T2, and T1ρ maps were acquired using variable flip angles and a modified 3D-Turbo-Flash sequence with different echo and spin-lock times, respectively, and were fitted using mono-exponential models. Each sequence was acquired on Day 1 and Day 7 with two scans on each day. STATISTICAL TESTS The mean and SD for cartilage T1, T2, and T1ρ were calculated in five manually segmented regions of interest (ROIs), including lateral femur, lateral tibia, medial femur, medial tibia, and patella cartilages. Intra-subject and inter-subject repeatabilities were assessed using coefficient of variation (CV) and intra-class correlation coefficient (ICC), respectively, on the same day and among different days. Regression and Bland-Altman analysis were performed to compare maps between the conventional and 3D-MRF sequences. RESULTS The CV in all ROIs was lower than 7.4%, 8.4%, and 7.5% and the ICC was higher than 0.56, 0.51, and 0.52 for cartilage T1, T2, and T1ρ, respectively. The MRF results had a good agreement with the conventional methods with a linear regression slope >0.61 and R2 > 0.59. CONCLUSION The 3D-MRF sequence had high intra-subject and inter-subject repeatabilities for simultaneously measuring knee cartilage T1, T2, and T1ρ with good agreement with conventional sequences. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Xiaoxia Zhang
- 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
| | - Anmol Monga
- 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
| | - Richard Kijowski
- 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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Ma D, Badve C, Sun JEP, Hu S, Wang X, Chen Y, Nayate A, Wien M, Martin D, Singer LT, Durieux JC, Flask C, Costello DW. Motion Robust MR Fingerprinting Scan to Image Neonates With Prenatal Opioid Exposure. J Magn Reson Imaging 2024; 59:1758-1768. [PMID: 37515516 PMCID: PMC10823040 DOI: 10.1002/jmri.28907] [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] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/31/2023] Open
Abstract
PURPOSE To explore whether MR fingerprinting (MRF) scans provide motion-robust and quantitative brain tissue measurements for non-sedated infants with prenatal opioid exposure (POE). STUDY TYPE Prospective. POPULATION 13 infants with POE (3 male; 12 newborns (age 7-65 days) and 1 infant aged 9-months). FIELD STRENGTH/SEQUENCE 3T, 3D T1-weighted MPRAGE, 3D T2-weighted TSE and MRF sequences. ASSESSMENT The image quality of MRF and MRI was assessed in a fully crossed, multiple-reader, multiple-case study. Sixteen image quality features in three types-image artifacts, structure and myelination visualization-were ranked by four neuroradiologists (8, 7, 5, and 8 years of experience respectively), using a 3-point scale. MRF T1 and T2 values in 8 white matter brain regions were compared between babies younger than 1 month and babies between 1 and 2 months. STATISTICAL TESTS Generalized estimating equations model to test the significance of differences of regional T1 and T2 values of babies under 1 month and those older. MRI and MRF image quality was assessed using Gwet's second order auto-correlation coefficient (AC2) with confidence levels. The Cochran-Mantel-Haenszel test was used to assess the difference in proportions between MRF and MRI for all features and stratified by the type of features. A P value <0.05 was considered statistically significant. RESULTS The MRF of two infants were excluded in T1 and T2 value analysis due to severe motion artifact but were included in the image quality assessment. In infants under 1 month of age (N = 6), the T1 and T2 values were significantly higher compared to those between 1 and 2 months of age (N = 4). MRF images showed significantly higher image quality ratings in all three feature types compared to MRI images. CONCLUSIONS MR Fingerprinting scans have potential to be a motion-robust and efficient method for nonsedated infants. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Dan Ma
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Chaitra Badve
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Jessie EP Sun
- Radiology, Case Western Reserve University, Cleveland, OH
| | - Siyuan Hu
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Xiaofeng Wang
- Quantitative Health Science, Cleveland Clinic, Cleveland, OH
| | - Yong Chen
- Radiology, Case Western Reserve University, Cleveland, OH
| | - Ameya Nayate
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Michael Wien
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Douglas Martin
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Lynn T Singer
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland
| | - Jared C. Durieux
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Chris Flask
- Radiology, Case Western Reserve University, Cleveland, OH
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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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Hu S, Chen Y, Zong X, Lin W, Griswold M, Ma D. Improving motion robustness of 3D MR fingerprinting with a fat navigator. Magn Reson Med 2023; 90:1802-1817. [PMID: 37345703 PMCID: PMC10524525 DOI: 10.1002/mrm.29761] [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: 01/05/2023] [Revised: 05/01/2023] [Accepted: 05/26/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE To develop a 3D MR fingerprinting (MRF) method in combination with fat navigators to improve its motion robustness for neuroimaging. METHODS A rapid fat navigator was developed using the stack-of-spirals acquisition and non-Cartesian spiral GRAPPA. The fat navigator module was implemented in the 3D MRF sequence with high scan efficiency. The developed method was first validated in phantoms and five healthy subjects with intentional head motion. The method was further applied to infants with neonatal opioid withdrawal symptoms. The 3D MRF scans with fat navigators acquired with and without acceleration along the partition-encoding direction were both examined in the study. RESULTS Both phantom and in vivo results demonstrated that the added fat navigator modules did not influence the quantification accuracy in MRF. In combination with non-Cartesian spiral GRAPPA, a rapid fat navigator sampling with whole-brain coverage was achieved in ˜0.5 s at 3T, reducing its sensitivity to potential motion. Based on the motion waveforms extracted from fat navigators, the motion robustness of the 3D MRF was largely improved. With the proposed method, the motion-corrupted MRF datasets yielded T1 and T2 maps with significantly reduced artifacts and high correlations with measurements from the reference motion-free MRF scans. CONCLUSION We developed a 3D MRF method coupled with rapid fat navigators to improve its motion robustness for quantitative neuroimaging. Our results demonstrate that (1) accurate tissue quantification was preserved with the fat navigator modules and (2) the motion robustness for quantitative tissue mapping was largely improved with the developed method.
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Affiliation(s)
- Siyuan Hu
- Department of Biomedical Engineering Cleveland, Ohio, USA
| | - Yong Chen
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xiaopeng Zong
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Weili Lin
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Mark Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Biomedical Engineering Cleveland, Ohio, USA
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Qian E, Poojar P, Fung M, Jin Z, Vaughan JT, Shrivastava D, Gultekin D, Fernandes T, Geethanath S. Magnetic resonance fingerprinting based thermometry (MRFT): application to ex vivoimaging near DBS leads. Phys Med Biol 2023; 68:17NT01. [PMID: 37489867 DOI: 10.1088/1361-6560/acea54] [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: 01/08/2023] [Accepted: 07/25/2023] [Indexed: 07/26/2023]
Abstract
The purpose of this study is to demonstrate the first work ofT1-based magnetic resonance thermometry using magnetic resonance fingerprinting (dubbed MRFT). We compared temperature estimation of MRFT with proton resonance frequency shift (PRFS) thermometry onex vivobovine muscle. We demonstrated MRFT's feasibility in predicting temperature onex vivobovine muscles with deep brain stimulation (DBS) lead.B0maps generated from MRFT were compared with gold standardB0maps near the DBS lead. MRFT and PRFS estimated temperatures were compared in the presence of motion. All experiments were performed on a 3 Tesla whole-body GE Premier system with a 21-channel receive head coil (GE Healthcare, Milwaukee, WI). Four fluoroptic probes were used to measure the temperature at the center of a cold muscle (probe 1), the room temperature water bottle (probe 2), and the center and periphery of the heated muscle (probes 3 and 4). We selected regions of interest (ROIs) around the location of the probes and used simple linear regression to generate the temperature sensitivity calibration equations that convertT1maps and Δsmaps to temperature maps. We then repeated the same setup and compared MRFT and PRFS thermometry temperature estimation with gold standard probe measurements. For the MRFT experiment on DBS lead, we taped the probe to the tip of the DBS lead and used a turbo spin echo sequence to induce heating near the lead. We selected ROIs around the tip of the lead to compare MRFT temperature estimation with probe measurements and compared with PRFS temperature estimation. Vendor-suppliedB0mapping sequence was acquired to compare with MRFT-generatedB0maps. We found strong linear relationships (R2> 0.958) betweenT1and temperature and Δsand temperatures in our temperature sensitivity calibration experiment. MRFT and PRFS thermometry both accurately predict temperature (RMSE < 1.55 °C) compared to probe measurements. MRFT estimated temperature near DBS lead has a similar trend as the probe temperature. BothB0maps show inhomogeneities around the lead. MRFT estimated temperature is less sensitive to motion.
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Affiliation(s)
- Enlin Qian
- Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States of America
- Department of Biomedical Engineering, Columbia University, New York, NY, United States of America
| | - Pavan Poojar
- Accessible MR Laboratory, Biomedical Engineering and Imaging Institute, Dept. of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mt. Sinai, New York, NY, United States of America
| | - Maggie Fung
- GE Healthcare, New York, NY, United States of America
| | - Zhezhen Jin
- Department of Biostatistics, Columbia University, New York, NY, United States of America
| | - John Thomas Vaughan
- Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States of America
- Department of Biomedical Engineering, Columbia University, New York, NY, United States of America
| | - Devashish Shrivastava
- Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States of America
| | - David Gultekin
- Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States of America
| | - Tiago Fernandes
- Accessible MR Laboratory, Biomedical Engineering and Imaging Institute, Dept. of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mt. Sinai, New York, NY, United States of America
- ISR - Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico-Universidade de Lisboa, Lisbon, Portugal
| | - Sairam Geethanath
- Columbia Magnetic Resonance Research Center, Columbia University, New York, NY, United States of America
- Accessible MR Laboratory, Biomedical Engineering and Imaging Institute, Dept. of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mt. Sinai, New York, NY, United States of America
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Hagiwara A, Fujita S, Kurokawa R, Andica C, Kamagata K, Aoki S. Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics. Invest Radiol 2023; 58:548-560. [PMID: 36822661 PMCID: PMC10332659 DOI: 10.1097/rli.0000000000000962] [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: 12/01/2022] [Revised: 01/10/2023] [Indexed: 02/25/2023]
Abstract
ABSTRACT With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.
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Affiliation(s)
- Akifumi Hagiwara
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shohei Fujita
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Christina Andica
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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Ullah I, Hassan AM, Saad RM, Omer H. GPU accelerated grouped magnetic resonance fingerprinting using clustering techniques. Magn Reson Imaging 2023; 97:13-23. [PMID: 36581213 DOI: 10.1016/j.mri.2022.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/19/2022] [Accepted: 12/23/2022] [Indexed: 12/28/2022]
Abstract
Magnetic Resonance Fingerprinting (MRF) is a new quantitative technique of Magnetic Resonance Imaging (MRI). Conventionally, MRF requires sequential correlation of the acquired MRF signals with all the signals of (a large sized) MRF dictionary. This is a computationally intensive matching process and is a major challenge in MRF image reconstruction. This paper introduces the use of clustering techniques (to reduce the effective size of MRF dictionary) by splitting MRF dictionary into multiple small sized MRF dictionary components called MRF signal groups. The proposed method has been further optimized for parallel processing to reduce the computation time of MRF pattern matching. A multi-core GPU based parallel framework has been developed that enables the MRF algorithm to process multiple MRF signals simultaneously. Experiments have been performed on human head and phantom datasets. The results show that the proposed method accelerates the conventional MRF (MATLAB based) reconstruction time up to 25× with single-core CPU implementation, 300× with multi- core CPU implementation and 1035× with the proposed multi-core GPU based framework by keeping the SNR of the resulting images in a clinically acceptable range. Furthermore, experimental results show that the memory requirements of MRF dictionary get significantly reduced (due to efficient memory utilization) in the proposed method.
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Affiliation(s)
- Irfan Ullah
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan.
| | - Abdul Moiz Hassan
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Rana Muhammad Saad
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Hammad Omer
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
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Sharafi A, Zibetti MVW, Chang G, Cloos M, Regatte RR. 3D magnetic resonance fingerprinting for rapid simultaneous T1, T2, and T1ρ volumetric mapping of human articular cartilage at 3 T. NMR IN BIOMEDICINE 2022; 35:e4800. [PMID: 35815660 PMCID: PMC9669203 DOI: 10.1002/nbm.4800] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 06/28/2022] [Accepted: 07/08/2022] [Indexed: 05/25/2023]
Abstract
Quantitative MRI can detect early biochemical changes in cartilage; however, the conventional techniques only measure one parameter (e.g., T1 , T2 , and T1ρ ) at a time while also being comparatively slow. We implemented a 3D magnetic resonance fingerprinting (3D-MRF) technique for simultaneous, volumetric mapping of T1 , T2 , and T1ρ in knee articular cartilage in under 9 min. It is evaluated on 11 healthy volunteers (mean age: 53 ± 9 years), five mild knee osteoarthritis (OA) patients (Kellgren-Lawrence (KL) score: 2, mean age: 60 ± 4 years), and the National Institute of Standards and Technology (NIST)/International Society for Magnetic Resonance in Medicine (ISMRM) system phantom. Proton density image, and T1 , T2, T1ρ relaxation times, and B1 + were estimated in the NIST/ISMRM system phantom as well as in the human knee medial and lateral femur, medial and lateral tibia, and patellar cartilage. The repeatability and reproducibility of the proposed technique were assessed in the phantom using analysis of the Bland-Altman plots. The intrasubject repeatability was assessed with the coefficient of variation (CV) and root mean square CV (rmsCV). The Mann-Whitney U test was used to assess the difference between healthy subjects and mild knee OA patients. The Bland-Altman plots in the NIST/ISMRM phantom demonstrated an average difference of 0.001% ± 015%, 1.2% ± 7.1%, and 0.47% ± 3% between two scans from the same 3-T scanner (repeatability), and 0.002% ± 015%, 0.62% ± 10.5%, and 0.97% ± 14% between the scans acquired on two different 3-T scanners (reproducibility) for T1 , T2 , and T1ρ , respectively. The in vivo knee study showed excellent repeatability with rmsCV less than 1%, 2%, and 1% for T1 , T2 , and T1ρ , respectively. T1ρ relaxation time in the mild knee OA patients was significantly higher (p < 0.05) than in healthy subjects. The proposed 3D-MRF sequence is fast, reproducible, robust to B1 + inhomogeneity, and can simultaneously measure the T1 , T2 , T1ρ , and B1 + volumetric maps of the knee joint in a single scan within a clinically feasible scan time.
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Affiliation(s)
- Azadeh Sharafi
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Marcelo V. W. Zibetti
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Gregory Chang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Martijn Cloos
- Center of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ravinder R. Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
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Nunez-Gonzalez L, Nagtegaal MA, Poot DHJ, de Bresser J, van Osch MJP, Hernandez-Tamames JA, Vos FM. Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting. Neuroimage 2022; 263:119638. [PMID: 36122685 DOI: 10.1016/j.neuroimage.2022.119638] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/11/2022] [Accepted: 08/31/2022] [Indexed: 11/30/2022] Open
Abstract
MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estimation for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmentations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL- and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray matter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter.
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Affiliation(s)
- L Nunez-Gonzalez
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.
| | - M A Nagtegaal
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - D H J Poot
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - J de Bresser
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - M J P van Osch
- C.J. Gorter Center for MRI, Radiology Department, Leiden University Medical Center, Leiden, the Netherlands
| | - J A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands; Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - F M Vos
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands; Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
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Sharafi A, Zibetti MVW, Chang G, Cloos MA, Regatte RR. Simultaneous bilateral T 1 , T 2 , and T 1ρ relaxation mapping of the hip joint with magnetic resonance fingerprinting. NMR IN BIOMEDICINE 2022; 35:e4651. [PMID: 34825750 PMCID: PMC9233946 DOI: 10.1002/nbm.4651] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 06/13/2023]
Abstract
Quantitative MRI can detect early biochemical changes in cartilage, but its bilateral use in clinical routines is challenging. The aim of this prospective study was to demonstrate the feasibility of magnetic resonance fingerprinting for bilateral simultaneous T1 , T2 , and T1ρ mapping of the hip joint. The study population consisted of six healthy volunteers with no known trauma or pain in the hip. Monoexponential T1 , T2 , and T1ρ relaxation components were assessed in femoral lateral, superolateral, and superomedial, and inferior, as well as acetabular, superolateral, and superomedial subregions in left and right hip cartilage. Aligned ranked nonparametric factorial analysis was used to assess the side's impact on the subregions. Kruskal-Wallis and Wilcoxon tests were used to compare subregions, and coefficient of variation to assess repeatability. Global averages of T1 (676.0 ± 45.4 and 687.6 ± 44.5 ms), T2 (22.5 ± 2.6 and 22.1 ± 2.5 ms), and T1ρ (38.2 ± 5.5 and 38.2 ± 5.5 ms) were measured in the left and right hip, and articular cartilage, respectively. The Kruskal-Wallis test showed a significant difference between different subregions' relaxation times regardless of the hip side (p < 0.001 for T1 , p = 0.012 for T2 , and p < 0.001 for T1ρ ). The Wilcoxon test showed that T1 of femoral layers was significantly (p < 0.003) higher than that for acetabular cartilage. The experiments showed excellent repeatability with CVrms of 1%, 2%, and 4% for T1 , T2 , and T1ρ, respectively. It was concluded that bilateral T1 , T2 , and T1ρ relaxation times, as well as B1+ maps, can be acquired simultaneously from hip joints using the proposed MRF sequence.
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Affiliation(s)
- Azadeh Sharafi
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Marcelo V. W. Zibetti
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Gregory Chang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Martijn A. Cloos
- Center of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ravinder R. Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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11
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Fujita S, Hagiwara A, Takei N, Fukunaga I, Hagiwara Y, Ogawa T, Hatano T, Rettmann D, Banerjee S, Hwang KP, Amemiya S, Kamagata K, Hattori N, Abe O, Aoki S. Rigid real-time prospective motion-corrected three-dimensional multiparametric mapping of the human brain. Neuroimage 2022; 255:119176. [PMID: 35390461 DOI: 10.1016/j.neuroimage.2022.119176] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/03/2022] [Accepted: 04/01/2022] [Indexed: 10/18/2022] Open
Abstract
PURPOSE To develop a rigid real-time prospective motion-corrected multiparametric mapping technique and to test the performance of quantitative estimates. METHODS Motion tracking and correction were performed by integrating single-shot spiral navigators into a multiparametric imaging technique, three-dimensional quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). The spiral navigator was optimized, and quantitative measurements were validated using a standard system phantom. The effect of motion correction on whole-brain T1 and T2 mapping under different types of head motion during the scan was evaluated in 10 healthy volunteers. Finally, six patients with Parkinson's disease, which is known to be associated with a high prevalence of motion artifacts, were scanned to evaluate the effectiveness of our method in the real world. RESULTS The phantom study demonstrated that the proposed motion correction method did not introduce quantitative bias. Improved parametric map quality and repeatability were shown in volunteer experiments with both in-plane and through-plane motions, comparable to the no-motion ground truth. In real-life validation in patients, the approach showed improved parametric map quality compared to images obtained without motion correction. CONCLUSIONS Real-time prospective motion-corrected multiparametric relaxometry based on 3D-QALAS provided robust and repeatable whole-brain multiparametric mapping.
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Affiliation(s)
- Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan; Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan
| | - Naoyuki Takei
- MR Applications and Workflow, GE Healthcare, Tokyo, Japan
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan
| | - Yasuhiro Hagiwara
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Takashi Ogawa
- Department of Neurology, Juntendo University, Tokyo, Japan
| | - Taku Hatano
- Department of Neurology, Juntendo University, Tokyo, Japan
| | - Dan Rettmann
- MR Applications and Workflow, GE Healthcare, Rochester, MN, United States
| | | | - Ken-Pin Hwang
- Department of Radiology, MD Anderson Cancer Center, Houston, TX, United States
| | - Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan
| | | | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan
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12
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Springer E, Cardoso PL, Strasser B, Bogner W, Preusser M, Widhalm G, Nittka M, Koerzdoerfer G, Szomolanyi P, Hangel G, Hainfellner JA, Marik W, Trattnig S. MR Fingerprinting-A Radiogenomic Marker for Diffuse Gliomas. Cancers (Basel) 2022; 14:cancers14030723. [PMID: 35158990 PMCID: PMC8833555 DOI: 10.3390/cancers14030723] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/22/2022] [Accepted: 01/28/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Advanced MR imaging (MRI) of brain tumors is mainly based on qualitative contrast images. MR Fingerprinting (MRF) offers a novel approach. The purpose of this study was to use MRF-derived T1 and T2 relaxation maps to differentiate diffuse gliomas according to isocitrate dehydrogenase (IDH) mutation. (2) Methods: Twenty-four patients with histologically verified diffuse gliomas (14 IDH-mutant, four 1p/19q-codeleted, 10 IDH-wildtype) were enrolled. MRF T1 and T2 relaxation times were compared to apparent diffusion coefficient (ADC), relative cerebral blood volume (rCBV) within solid tumor, peritumoral edema, and normal-appearing white matter (NAWM), using contrast-enhanced MRI, diffusion-, perfusion-, and susceptibility-weighted imaging. For perfusion imaging, a T2* weighted perfusion sequence with leakage correction was used. Correlations of MRF T1 and T2 times with two established conventional sequences for T1 and T2 mapping were assessed (a fast double inversion recovery-based MR sequence ('MP2RAGE') for T1 quantification and a multi-contrast spin echo-based sequence for T2 quantification). (3) Results: MRF T1 and T2 relaxation times were significantly higher in the IDH-mutant than in IDH-wildtype gliomas within the solid part of the tumor (p = 0.024 for MRF T1, p = 0.041 for MRF T2). MRF T1 and T2 relaxation times were significantly higher in the IDH-wildtype than in IDH-mutant gliomas within peritumoral edema less than or equal to 1cm adjacent to the tumor (p = 0.038 for MRF T1 mean, p = 0.010 for MRF T2 mean). In the solid part of the tumor, there was a high correlation between MRF and conventionally measured T1 and T2 values (r = 0.913, p < 0.001 for T1, r = 0.775, p < 0.001 for T2), as well as between MRF and ADC values (r = 0.813, p < 0.001 for T2, r = 0.697, p < 0.001 for T1). The correlation was weak between the MRF and rCBV values (r = -0.374, p = 0.005 for T2, r = -0.181, p = 0.181 for T1). (4) Conclusions: MRF enables fast, single-sequence based, multi-parametric, quantitative tissue characterization of diffuse gliomas and may have the potential to differentiate IDH-mutant from IDH-wildtype gliomas.
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Affiliation(s)
- Elisabeth Springer
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Institute of Radiology, Hietzing Hospital, 1130 Vienna, Austria
| | - Pedro Lima Cardoso
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
| | - Bernhard Strasser
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
| | - Wolfgang Bogner
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Correspondence: ; Tel.: +431-40-400-64710
| | - Matthias Preusser
- Division of Oncology, Department of Internal Medicine I, Medical University of Vienna, 1090 Vienna, Austria;
| | - Georg Widhalm
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria;
| | - Mathias Nittka
- Siemens Healthineers, 91052 Erlangen, Germany; (M.N.); (G.K.)
| | | | - Pavol Szomolanyi
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Department of Imaging Methods, Institute of Measurement Science, Slovak Academy of Sciences, 84104 Bratislava, Slovakia
| | - Gilbert Hangel
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Department of Neurosurgery, Medical University of Vienna, 1090 Vienna, Austria;
| | - Johannes A. Hainfellner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria;
| | - Wolfgang Marik
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria;
| | - Siegfried Trattnig
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (E.S.); (P.L.C.); (B.S.); (P.S.); (G.H.); (S.T.)
- Christian Doppler Laboratory for Clinical Molecular MR Imaging, Medical University of Vienna, 1090 Vienna, Austria
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13
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Lima da Cruz GJ, Velasco C, Lavin B, Jaubert O, Botnar RM, Prieto C. Myocardial T1, T2, T2*, and fat fraction quantification via low-rank motion-corrected cardiac MR fingerprinting. Magn Reson Med 2022; 87:2757-2774. [PMID: 35081260 PMCID: PMC9306903 DOI: 10.1002/mrm.29171] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 12/06/2021] [Accepted: 01/05/2022] [Indexed: 12/11/2022]
Abstract
Purpose Develop a novel 2D cardiac MR fingerprinting (MRF) approach to enable simultaneous T1, T2, T2*, and fat fraction (FF) myocardial tissue characterization in a single breath‐hold scan. Methods Simultaneous, co‐registered, multi‐parametric mapping of T1, T2, and FF has been recently achieved with cardiac MRF. Here, we further incorporate T2* quantification within this approach, enabling simultaneous T1, T2, T2*, and FF myocardial tissue characterization in a single breath‐hold scan. T2* quantification is achieved with an eight‐echo readout that requires a long cardiac acquisition window. A novel low‐rank motion‐corrected (LRMC) reconstruction is exploited to correct for cardiac motion within the long acquisition window. The proposed T1/T2/T2*/FF cardiac MRF was evaluated in phantom and in 10 healthy subjects in comparison to conventional mapping techniques. Results The proposed approach achieved high quality parametric mapping of T1, T2, T2*, and FF with corresponding normalized RMS error (RMSE) T1 = 5.9%, T2 = 9.6% (T2 values <100 ms), T2* = 3.3% (T2* values <100 ms), and FF = 0.8% observed in phantom scans. In vivo, the proposed approach produced higher left‐ventricular myocardial T1 values than MOLLI (1148 vs 1056 ms), lower T2 values than T2‐GraSE (42.8 vs 50.6 ms), lower T2* values than eight‐echo gradient echo (GRE) (35.0 vs 39.4 ms), and higher FF values than six‐echo GRE (0.8 vs 0.3 %) reference techniques. The proposed approach achieved considerable reduction in motion artifacts compared to cardiac MRF without motion correction, improved spatial uniformity, and statistically higher apparent precision relative to conventional mapping for all parameters. Conclusion The proposed cardiac MRF approach enables simultaneous, co‐registered mapping of T1, T2, T2*, and FF in a single breath‐hold for comprehensive myocardial tissue characterization, achieving higher apparent precision than conventional methods.
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Affiliation(s)
- Gastao José Lima da Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Begoña Lavin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Biochemistry and Molecular Biology, School of Chemistry, Complutense University, Madrid, Spain
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Rene Michael Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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14
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Ma S, Wang N, Xie Y, Fan Z, Li D, Christodoulou AG. Motion-robust quantitative multiparametric brain MRI with motion-resolved MR multitasking. Magn Reson Med 2022; 87:102-119. [PMID: 34398991 PMCID: PMC8616852 DOI: 10.1002/mrm.28959] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/30/2021] [Accepted: 07/20/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To address head motion in brain MRI with a novel motion-resolved imaging framework, with application to motion-robust quantitative multiparametric mapping. METHODS MR multitasking conceptualizes the underlying multiparametric image in the presence of motion as a multidimensional low-rank tensor. By incorporating a motion-state dimension into the parameter dimensions and introducing unsupervised motion-state binning and outlier motion reweighting mechanisms, the brain motion can be readily resolved for motion-robust quantitative imaging. A numerical motion phantom was used to simulate different discrete and periodic motion patterns under various translational and rotational scenarios, as well as investigate the sensitivity to exceptionally small and large displacements. In vivo brain MRI was performed to also evaluate different real discrete and periodic motion patterns. The effectiveness of motion-resolved imaging for simultaneous T1 /T2 /T1ρ mapping in the brain was investigated. RESULTS For all 14 simulation scenarios of small, intermediate, and large motion displacements, the motion-resolved approach produced T1 /T2 /T1ρ maps with less absolute difference errors against the ground truth, lower RMSE, and higher structural similarity index measure of T1 /T2 /T1ρ measurements compared to motion removal, and no motion handling. For in vivo experiments, the motion-resolved approach produced T1 /T2 /T1ρ maps with the best image quality free from motion artifacts under random discrete motion, tremor, periodic shaking, and nodding patterns compared to motion removal and no motion handling. The proposed method also yielded T1 /T2 /T1ρ measurement distributions closest to the motion-free reference, with minimal measurement bias and variance. CONCLUSION Motion-resolved quantitative brain imaging is achieved with multitasking, which is generalizable to various head motion patterns without explicit need for registration-based motion correction.
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Affiliation(s)
- Sen Ma
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nan Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Zhaoyang Fan
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA,Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA,Corresponding author: Anthony G. Christodoulou, 8700 Beverly Blvd, PACT 400, Los Angeles, CA 90048, , phone: 3104236754
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15
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Yu N, Kim JY, Han D, Kim SY, Lee HM, Kim DH, Kim HG. Three-Dimensional Magnetic Resonance Fingerprinting in Neonates: Quantifying Regional Difference and Maturation in the Brain. Invest Radiol 2022; 57:44-51. [PMID: 34101674 DOI: 10.1097/rli.0000000000000800] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES Magnetic resonance fingerprinting (MRF) allows the simultaneous measurement of multiple tissue properties in a single acquisition. Three-dimensional (3D) MRF with high spatial resolution can be used for neonatal brain imaging. The aim of this study is to apply 3D MRF to neonates and show regional differences and maturation in the brain. MATERIALS AND METHODS In this prospective study, 3D MRF using hybrid radial-interleaved acquisition was performed on phantoms and neonates from December 2019 to October 2020. For the reconstruction of 3D MRF, singular value decomposition was applied to reduce reconstruction time, and the iterative reconstruction technique was applied to improve image quality. The accuracies of T1 and T2 values derived from 3D MRF were evaluated in a phantom experiment. Regional T1 and T2 values were obtained from neonates' brain T1 and T2 maps derived from 3D MRF. Regional T1 and T2 values were compared, and their changes according to corrected gestational age were evaluated. RESULTS The acquisition time for 3D MRF with a spatial resolution of 0.7 × 0.7 × 2 mm3 was less than 5 minutes. The phantom study showed high correlation between T1 and T2 values derived from 3D MRF and those from conventional spin echo sequences (T1, R2 = 0.998, P < 0.001; T2, R2 = 0.998, P < 0.001). Three-dimensional MRF was performed in 25 neonates (15 boys, 10 girls; median corrected gestational age, 263 days; interquartile range, 10 days). In neonates, T1 and T2 values differed in the frontal (median [interquartile range], 2785 [2684-2888] milliseconds and 189.8 [176.7-222.9] milliseconds), parietal (2849 [2741-2950] milliseconds and 191.6 [167.5-232.9] milliseconds), and occipital white matter (2621 [2513-2722] milliseconds and 162.9 [143.5-186.1] milliseconds), showing lower values in occipital white matter (P < 0.001). Regional T1 values showed a negative relationship with corrected gestational age (coefficient, -0.775 to -0.480; P < 0.05). CONCLUSIONS Fast and high spatial resolution 3D MRF was applied to neonates. T1 and T2 maps derived from 3D MRF enabled the quantification of regional differences and maturation in the neonatal brain.
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Affiliation(s)
- Nali Yu
- From the Departments of Pediatrics
| | - Jee Young Kim
- Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea
| | | | | | | | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea
| | - Hyun Gi Kim
- Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea
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16
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Cruz G, Qi H, Jaubert O, Kuestner T, Schneider T, Botnar RM, Prieto C. Generalized low-rank nonrigid motion-corrected reconstruction for MR fingerprinting. Magn Reson Med 2021; 87:746-763. [PMID: 34601737 DOI: 10.1002/mrm.29027] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 08/12/2021] [Accepted: 09/09/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE Develop a novel low-rank motion-corrected (LRMC) reconstruction for nonrigid motion-corrected MR fingerprinting (MRF). METHODS Generalized motion-corrected (MC) reconstructions have been developed for steady-state imaging. Here we extend this framework to enable nonrigid MC for transient imaging applications with varying contrast, such as MRF. This is achieved by integrating low-rank dictionary-based compression into the generalized MC model to reconstruct MC singular images, reducing motion artifacts in the resulting parametric maps. The proposed LRMC reconstruction was applied for cardiac motion correction in 2D myocardial MRF (T1 and T2 ) with extended cardiac acquisition window (~450 ms) and for respiratory MC in free-breathing 3D myocardial and 3D liver MRF. Experiments were performed in phantom and 22 healthy subjects. The proposed approach was compared with reference spin echo (phantom) and with 2D electrocardiogram-triggered/breath-hold MOLLI and T2 gradient-and-spin echo conventional maps (in vivo 2D and 3D myocardial MRF). RESULTS Phantom results were in general agreement with reference spin-echo measurements, presenting relative errors of approximately 5.4% and 5.5% for T1 and short T2 (<100 ms), respectively. The proposed LRMC MRF reduced residual blurring artifacts with respect to no MC for cardiac or respiratory motion in all cases (2D and 3D myocardial, 3D abdominal). In 2D myocardial MRF, left-ventricle T1 values were 1150 ± 41 ms for LRMC MRF and 1010 ± 56 ms for MOLLI; T2 values were 43.8 ± 2.3 ms for LRMC MRF and 49.5 ± 4.5 ms for T2 gradient and spin echo. Corresponding measurements for 3D myocardial MRF were 1085 ± 30 ms and 1062 ± 29 ms for T1 , and 43.5 ± 1.9 ms and 51.7 ± 1.7 ms for T2 . For 3D liver, LRMC MRF measured liver T1 at 565 ± 44 ms and liver T2 at 35.4 ± 2.4 ms. CONCLUSION The proposed LRMC reconstruction enabled generalized (nonrigid) MC for 2D and 3D MRF, both for cardiac and respiratory motion. The proposed approach reduced motion artifacts in the MRF maps with respect to no motion compensation and achieved good agreement with reference measurements.
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Affiliation(s)
- Gastao Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Thomas Kuestner
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Rene Michael Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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17
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Ding H, Velasco C, Ye H, Lindner T, Grech-Sollars M, O’Callaghan J, Hiley C, Chouhan MD, Niendorf T, Koh DM, Prieto C, Adeleke S. Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer. Cancers (Basel) 2021; 13:4742. [PMID: 34638229 PMCID: PMC8507535 DOI: 10.3390/cancers13194742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/08/2021] [Accepted: 09/16/2021] [Indexed: 11/25/2022] Open
Abstract
Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment.
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Affiliation(s)
- Hao Ding
- Imperial College School of Medicine, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK;
| | - Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London SE1 7EH, UK; (C.V.); (C.P.)
| | - Huihui Ye
- State Key Laboratory of Modern Optical instrumentation, Zhejiang University, Hangzhou 310027, China;
| | - Thomas Lindner
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg Eppendorf, 20246 Hamburg, Germany;
| | - Matthew Grech-Sollars
- Department of Medical Physics, Royal Surrey NHS Foundation Trust, Surrey GU2 7XX, UK;
- Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, UK
| | - James O’Callaghan
- UCL Centre for Medical Imaging, Division of Medicine, University College London, London W1W 7TS, UK; (J.O.); (M.D.C.)
| | - Crispin Hiley
- Cancer Research UK, Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6DD, UK;
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Manil D. Chouhan
- UCL Centre for Medical Imaging, Division of Medicine, University College London, London W1W 7TS, UK; (J.O.); (M.D.C.)
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck, Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany;
| | - Dow-Mu Koh
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SM2 5NG, UK;
- Department of Radiology, Royal Marsden Hospital, London SW3 6JJ, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London SE1 7EH, UK; (C.V.); (C.P.)
| | - Sola Adeleke
- High Dimensional Neurology Group, Queen’s Square Institute of Neurology, University College London, London WC1N 3BG, UK
- Department of Oncology, Guy’s & St Thomas’ Hospital, London SE1 9RT, UK
- School of Cancer & Pharmaceutical Sciences, King’s College London, London WC2R 2LS, UK
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18
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Stout JN, Liao C, Gagoski B, Turk EA, Feldman HA, Bibbo C, Barth WH, Shainker SA, Wald LL, Grant PE, Adalsteinsson E. Quantitative T 1 and T 2 mapping by magnetic resonance fingerprinting (MRF) of the placenta before and after maternal hyperoxia. Placenta 2021; 114:124-132. [PMID: 34537569 DOI: 10.1016/j.placenta.2021.08.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 06/16/2021] [Accepted: 08/05/2021] [Indexed: 11/13/2022]
Abstract
INTRODUCTION MR relaxometry has been used to assess placental exchange function, but methods to date are not sufficiently fast to be robust to placental motion. Magnetic resonance fingerprinting (MRF) permits rapid, voxel-wise, intrinsically co-registered T1 and T2 mapping. After characterizing measurement error, we scanned pregnant women during air and oxygen breathing to demonstrate MRF's ability to detect placental oxygenation changes. METHODS The accuracy of FISP-based, sliding-window reconstructed MRF was tested on phantoms. MRF scans in 9-s breath holds were acquired at 3T in 31 pregnant women during air and oxygen breathing. A mixed effects model was used to test for changes in placenta relaxation times between physiological states, to assess the dependency on gestational age (GA), and the impact of placental motion. RESULTS MRF estimates of known phantom relaxation times resulted in mean absolute errors for T1 of 92 ms (4.8%), but T2 was less accurate at 16 ms (13.6%). During normoxia, placental T1 = 1825 ± 141 ms (avg ± standard deviation) and T2 = 60 ± 16 ms (gestational age range 24.3-36.7, median 32.6 weeks). In the statistical model, placental T2 rose and T1 remained contant after hyperoxia, and no GA dependency was observed for T1 or T2. DISCUSSION Well-characterized, motion-robust MRF was used to acquire T1 and T2 maps of the placenta. Changes with hyperoxia are consistent with a net increase in oxygen saturation. Toward the goal of whole-placenta quantitative oxygenation imaging over time, we aim to implement 3D MRF with integrated motion correction to improve T2 accuracy.
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Affiliation(s)
- Jeffrey N Stout
- Fetal and Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, 02115, USA.
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Borjan Gagoski
- Fetal and Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Esra Abaci Turk
- Fetal and Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Henry A Feldman
- Centers for Clinical and Translational Research, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Carolina Bibbo
- Brigham and Women's Hospital, Division of Maternal-Fetal Medicine, Boston, MA, 02115, USA
| | - William H Barth
- Maternal-Fetal Medicine, Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Scott A Shainker
- Maternal-Fetal Medicine, Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, 02129, USA
| | - P Ellen Grant
- Fetal and Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Elfar Adalsteinsson
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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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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20
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T 1 and T 2 quantification using magnetic resonance fingerprinting in mild traumatic brain injury. Eur Radiol 2021; 32:1308-1319. [PMID: 34410458 DOI: 10.1007/s00330-021-08235-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/15/2021] [Accepted: 07/27/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES To assess whether MR fingerprinting (MRF)-based relaxation properties exhibit cross-sectional and prospective correlations with patient outcome and compare the results with those from DTI. METHODS Clinical imaging, MRF, and DTI were acquired in patients (24 ± 10 days after injury (timepoint 1) and 90 ± 17 days after injury (timepoint 2)) and once in controls. Patient outcome was assessed with global functioning, symptom profile, and neuropsychological testing. ADC and fractional anisotropy (FA) from DTI and T1 and T2 from MRF were compared in 12 gray and white matter regions with Mann-Whitney tests. Bivariate associations between MR measures and outcome were assessed using the Spearman correlation and logistic regression. RESULTS Data from 22 patients (38 ± 12 years; 17 women) and 18 controls (32 ± 8 years; 12 women) were analyzed. Fourteen patients (37 ± 12 years; 11 women) returned for timepoint 2, while two patients provided only timepoint 2 clinical outcome data. At timepoint 1, there were no differences between patients and controls in T1, T2, and ADC, while FA was lower in mTBI frontal white matter. T1 at timepoint 1 and the change in T1 exhibited more (n = 18) moderate to strong correlations (|r|= 0.6-0.85) with clinical outcome at timepoint 2 than T2 (n = 3), FA (n = 7), and ADC (n = 2). High T1 at timepoint 1, and serially increasing T1, accounted for five of the six MR measures with the highest utility for identification of non-recovered patients at timepoint 2 (AUC > 0.80). CONCLUSION T1 derived from MRF was found to have higher utility than T2, FA, and ADC for predicting 3-month outcome after mTBI. KEY POINTS • In a region-of-interest approach, FA, ADC, and T1 and T2 all showed limited utility in differentiating patients from controls at an average of 24 and 90 days post-mild traumatic brain injury. • T1 at 24 days, and the serial change in T1, revealed more and stronger predictive correlations with clinical outcome at 90 days than did T2, ADC, or FA. • T1 showed better prospective identification of non-recovered patients at 90 days than ADC, T2, and FA.
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21
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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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22
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Hsieh JJL, Svalbe I. Magnetic resonance fingerprinting: from evolution to clinical applications. J Med Radiat Sci 2020; 67:333-344. [PMID: 32596957 PMCID: PMC7754037 DOI: 10.1002/jmrs.413] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 05/19/2020] [Accepted: 05/23/2020] [Indexed: 02/06/2023] Open
Abstract
In 2013, Magnetic Resonance Fingerprinting (MRF) emerged as a method for fast, quantitative Magnetic Resonance Imaging. This paper reviews the current status of MRF up to early 2020 and aims to highlight the advantages MRF can offer medical imaging professionals. By acquiring scan data as pseudorandom samples, MRF elicits a unique signal evolution, or 'fingerprint', from each tissue type. It matches 'randomised' free induction decay acquisitions against pre-computed simulated tissue responses to generate a set of quantitative images of T1 , T2 and proton density (PD) with co-registered voxels, rather than as traditional relative T1 - and T2 -weighted images. MRF numeric pixel values retain accuracy and reproducibility between 2% and 8%. MRF acquisition is robust to strong undersampling of k-space. Scan sequences have been optimised to suppress sub-sampling artefacts, while artificial intelligence and machine learning techniques have been employed to increase matching speed and precision. MRF promises improved patient comfort with reduced scan times and fewer image artefacts. Quantitative MRF data could be used to define population-wide numeric biomarkers that classify normal versus diseased tissue. Certification of clinical centres for MRF scan repeatability would permit numeric comparison of sequential images for any individual patient and the pooling of multiple patient images across large, cross-site imaging studies. MRF has to date shown promising results in early clinical trials, demonstrating reliable differentiation between malignant and benign prostate conditions, and normal and sclerotic hippocampal tissue. MRF is now undergoing small-scale trials at several sites across the world; moving it closer to routine clinical application.
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Affiliation(s)
- Jean J. L. Hsieh
- Department of Diagnostic RadiologyTan Tock Seng HospitalSingaporeSingapore
- Department of Medical Imaging and Radiation SciencesMonash UniversityClaytonVictoriaAustralia
| | - Imants Svalbe
- School of Physics and AstronomyMonash UniversityClaytonVictoriaAustralia
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23
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Sharafi A, Zibetti MVW, Chang G, Cloos M, Regatte RR. MR fingerprinting for rapid simultaneous T 1 , T 2 , and T 1ρ relaxation mapping of the human articular cartilage at 3T. Magn Reson Med 2020; 84:2636-2644. [PMID: 32385949 PMCID: PMC7396294 DOI: 10.1002/mrm.28308] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 04/12/2020] [Accepted: 04/13/2020] [Indexed: 12/25/2022]
Abstract
PURPOSE To implement a novel technique for simultaneous, quantitative multiparametric mapping of the knee articular cartilage. METHODS A novel MR fingerprinting pulse sequence is proposed and implemented for simultaneous measurements of proton density, T1 , T2, and T1ρ relaxation times at 3T. The repeatability and reproducibility of the proposed technique were assessed in model phantoms. Institutional review board-approved MR fingerprinting imaging sequence was performed on healthy volunteers and patients with mild knee osteoarthritis. The Wilcoxon test was used to compare healthy controls and patients. The intra- and intersubject repeatability were assessed with coefficient of variation and the RMS coefficient of variation, respectively RESULTS: The Bland-Altman plots demonstrated an average difference of 4.67 ms, -0.09 ms, and 0.05 ms between 2 scans in the same scanner; and 9.68 ms, 0.29 ms, and -0.72 ms between the scans acquired on 2 different scanners for T1 , T2 , and T1ρ , respectively. The in vivo knee study showed excellent repeatability with RMS coefficient of variation less than 3%, 6%, and 5% for T1 , T2 , and T1ρ , respectively. The Wilcoxon test showed a significant difference between control and mild osteoarthritis patients for T1 (P = .04), T2 (P = .01), and T1ρ (P = .02) relaxation time in medial tibial cartilage, as well as for T2 relaxation time (P = .02) in medial femoral cartilage. CONCLUSION The proposed MRF sequence is fast and can simultaneously measure the T1 , T2 , T1ρ , and B 1 + maps in a single scan. It is able to discriminate between mild osteoarthritis patients and healthy volunteers.
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Affiliation(s)
- Azadeh Sharafi
- Bernard and Irene Schwartz Center for Biomedical ImagingDepartment of RadiologyNew York University School of MedicineNew YorkNYUSA
| | - Marcelo V. W. Zibetti
- Bernard and Irene Schwartz Center for Biomedical ImagingDepartment of RadiologyNew York University School of MedicineNew YorkNYUSA
| | - Gregory Chang
- Bernard and Irene Schwartz Center for Biomedical ImagingDepartment of RadiologyNew York University School of MedicineNew YorkNYUSA
| | - Martijn Cloos
- Bernard and Irene Schwartz Center for Biomedical ImagingDepartment of RadiologyNew York University School of MedicineNew YorkNYUSA
| | - Ravinder R. Regatte
- Bernard and Irene Schwartz Center for Biomedical ImagingDepartment of RadiologyNew York University School of MedicineNew YorkNYUSA
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24
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Serrao EM, Kessler DA, Carmo B, Beer L, Brindle KM, Buonincontri G, Gallagher FA, Gilbert FJ, Godfrey E, Graves MJ, McLean MA, Sala E, Schulte RF, Kaggie JD. Magnetic resonance fingerprinting of the pancreas at 1.5 T and 3.0 T. Sci Rep 2020; 10:17563. [PMID: 33067515 PMCID: PMC7567885 DOI: 10.1038/s41598-020-74462-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 09/29/2020] [Indexed: 12/18/2022] Open
Abstract
Magnetic resonance imaging of the pancreas is increasingly used as an important diagnostic modality for characterisation of pancreatic lesions. Pancreatic MRI protocols are mostly qualitative due to time constraints and motion sensitivity. MR Fingerprinting is an innovative acquisition technique that provides qualitative data and quantitative parameter maps from a single free-breathing acquisition with the potential to reduce exam times. This work investigates the feasibility of MRF parameter mapping for pancreatic imaging in the presence of free-breathing exam. Sixteen healthy participants were prospectively imaged using MRF framework. Regions-of-interest were drawn in multiple solid organs including the pancreas and T1 and T2 values determined. MRF T1 and T2 mapping was performed successfully in all participants (acquisition time:2.4-3.6 min). Mean pancreatic T1 values were 37-43% lower than those of the muscle, spleen, and kidney at both 1.5 and 3.0 T. For these organs, the mean pancreatic T2 values were nearly 40% at 1.5 T and < 12% at 3.0 T. The feasibility of MRF at 1.5 T and 3 T was demonstrated in the pancreas. By enabling fast and free-breathing quantitation, MRF has the potential to add value during the clinical characterisation and grading of pathological conditions, such as pancreatitis or cancer.
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Affiliation(s)
- Eva M Serrao
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Cancer Research UK, Cambridge, UK
| | - Dimitri A Kessler
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Bruno Carmo
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Lucian Beer
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | | | | | - Ferdia A Gallagher
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Cancer Research UK, Cambridge, UK
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Cancer Research UK, Cambridge, UK
| | - Edmund Godfrey
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Martin J Graves
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Mary A McLean
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Cancer Research UK, Cambridge, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Cancer Research UK, Cambridge, UK
| | | | - Joshua D Kaggie
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK.
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
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25
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Edelman R, Leloudas N, Pang J, Bailes J, Merrell R, Koktzoglou I. Twofold improved tumor-to-brain contrast using a novel T1 relaxation-enhanced steady-state (T 1RESS) MRI technique. SCIENCE ADVANCES 2020; 6:6/44/eabd1635. [PMID: 33115747 PMCID: PMC7608787 DOI: 10.1126/sciadv.abd1635] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 09/15/2020] [Indexed: 06/11/2023]
Abstract
A technique that provides more accurate cancer detection would be of great value. Toward this end, we developed T1 relaxation-enhanced steady-state (T1RESS), a novel magnetic resonance imaging (MRI) pulse sequence that enables the flexible modulation of T1 weighting and provides the unique feature that intravascular signals can be toggled on and off in contrast-enhanced scans. T1RESS makes it possible to effectively use an MRI technique with improved signal-to-noise ratio efficiency for cancer imaging. In a proof-of-concept study, "dark blood" unbalanced T1RESS provided a twofold improvement in tumor-to-brain contrast compared with standard techniques, whereas balanced T1RESS greatly enhanced vascular detail. In conclusion, T1RESS represents a new MRI technique with substantial potential value for cancer imaging, along with a broad range of other clinical applications.
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Affiliation(s)
- R Edelman
- Radiology, NorthShore University HealthSystem, 2650 Ridge Ave., Evanston, IL 60201, USA.
- Northwestern Medicine, 251 E. Huron St., Chicago, IL 60611, USA
| | - N Leloudas
- Radiology, NorthShore University HealthSystem, 2650 Ridge Ave., Evanston, IL 60201, USA
| | - J Pang
- Siemens Medical Solutions USA Inc., 737 N. Michigan Ave., Chicago, IL 60611, USA
| | - J Bailes
- University of Chicago Pritzker School of Medicine, 924 E. 57th St., Chicago, IL 60637, USA
- Neurosurgery, NorthShore University HealthSystem, 2650 Ridge Ave., Evanston, IL 60201, USA
| | - R Merrell
- University of Chicago Pritzker School of Medicine, 924 E. 57th St., Chicago, IL 60637, USA
- Neurology, NorthShore University HealthSystem, 2650 Ridge Ave., Evanston, IL 60201, USA
| | - I Koktzoglou
- Radiology, NorthShore University HealthSystem, 2650 Ridge Ave., Evanston, IL 60201, USA
- University of Chicago Pritzker School of Medicine, 924 E. 57th St., Chicago, IL 60637, USA
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26
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Cruz G, Jaubert O, Qi H, Bustin A, Milotta G, Schneider T, Koken P, Doneva M, Botnar RM, Prieto C. 3D free-breathing cardiac magnetic resonance fingerprinting. NMR IN BIOMEDICINE 2020; 33:e4370. [PMID: 32696590 DOI: 10.1002/nbm.4370] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 06/04/2020] [Accepted: 06/23/2020] [Indexed: 05/15/2023]
Abstract
PURPOSE To develop a novel respiratory motion compensated three-dimensional (3D) cardiac magnetic resonance fingerprinting (cMRF) approach for whole-heart myocardial T1 and T2 mapping from a free-breathing scan. METHODS Two-dimensional (2D) cMRF has been recently proposed for simultaneous, co-registered T1 and T2 mapping from a breath-hold scan; however, coverage is limited. Here we propose a novel respiratory motion compensated 3D cMRF approach for whole-heart myocardial T1 and T2 tissue characterization from a free-breathing scan. Variable inversion recovery and T2 preparation modules are used for parametric encoding, respiratory bellows driven localized autofocus is proposed for beat-to-beat translation motion correction and a subspace regularized reconstruction is employed to accelerate the scan. The proposed 3D cMRF approach was evaluated in a standardized T1 /T2 phantom in comparison with reference spin echo values and in 10 healthy subjects in comparison with standard 2D MOLLI, SASHA and T2 -GraSE mapping techniques at 1.5 T. RESULTS 3D cMRF T1 and T2 measurements were generally in good agreement with reference spin echo values in the phantom experiments, with relative errors of 2.9% and 3.8% for T1 and T2 (T2 < 100 ms), respectively. in vivo left ventricle (LV) myocardial T1 values were 1054 ± 19 ms for MOLLI, 1146 ± 20 ms for SASHA and 1093 ± 24 ms for the proposed 3D cMRF; corresponding T2 values were 51.8 ± 1.6 ms for T2-GraSE and 44.6 ± 2.0 ms for 3D cMRF. LV coefficients of variation were 7.6 ± 1.6% for MOLLI, 12.1 ± 2.7% for SASHA and 5.8 ± 0.8% for 3D cMRF T1 , and 10.5 ± 1.4% for T2-GraSE and 11.7 ± 1.6% for 3D cMRF T2 . CONCLUSION The proposed 3D cMRF can provide whole-heart, simultaneous and co-registered T1 and T2 maps with accuracy and precision comparable to those of clinical standards in a single free-breathing scan of about 7 min.
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Affiliation(s)
- Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Haikun Qi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Aurélien Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Giorgia Milotta
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | | | | | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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27
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Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence. Invest Radiol 2020; 55:601-616. [PMID: 32209816 PMCID: PMC7413678 DOI: 10.1097/rli.0000000000000666] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.
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Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| | | | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
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Kurzawski JW, Cencini M, Peretti L, Gómez PA, Schulte RF, Donatelli G, Cosottini M, Cecchi P, Costagli M, Retico A, Tosetti M, Buonincontri G. Retrospective rigid motion correction of three-dimensional magnetic resonance fingerprinting of the human brain. Magn Reson Med 2020; 84:2606-2615. [PMID: 32368835 DOI: 10.1002/mrm.28301] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 04/06/2020] [Accepted: 04/07/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE To obtain three-dimensional (3D), quantitative and motion-robust imaging with magnetic resonance fingerprinting (MRF). METHODS Our acquisition is based on a 3D spiral projection k-space scheme. We compared different orderings of trajectory interleaves in terms of rigid motion-correction robustness. In all tested orderings, we considered the whole dataset as a sum of 56 segments of 7-s duration, acquired sequentially with the same flip angle schedule. We performed a separate image reconstruction for each segment, producing whole-brain navigators that were aligned to the first segment using normalized correlation. The estimated rigid motion was used to correct the k-space data, and the aligned data were matched with the dictionary to obtain motion-corrected maps. RESULTS A significant improvement on the motion-affected maps after motion correction is evident with the suppression of motion artifacts. Correlation with the motionless baseline improved by 20% on average for both T1 and T2 estimations after motion correction. In addition, the average motion-induced quantification bias of 70 ms for T1 and 18 ms for T2 values was reduced to 12 ms and 6 ms, respectively, improving the reliability of quantitative estimations. CONCLUSION We established a method that allows correcting 3D rigid motion on a 7-s timescale during the reconstruction of MRF data using self-navigators, improving the image quality and the quantification robustness.
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Affiliation(s)
- Jan W Kurzawski
- Pisa Division, National Institute for Nuclear Physics (INFN), Pisa, Italy.,Imago7 Foundation, Pisa, Italy
| | - Matteo Cencini
- Imago7 Foundation, Pisa, Italy.,IRCCS Stella Maris, Pisa, Italy
| | - Luca Peretti
- Imago7 Foundation, Pisa, Italy.,Department of Physics, University of Pisa, Pisa, Italy
| | - Pedro A Gómez
- Munich School of Bioengineering, Technical University of Munich, Munich, Germany
| | | | - Graziella Donatelli
- Imago7 Foundation, Pisa, Italy.,Neuroradiology Unit, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Mirco Cosottini
- Imago7 Foundation, Pisa, Italy.,Department of Physics, University of Pisa, Pisa, Italy
| | - Paolo Cecchi
- Neuroradiology Unit, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Mauro Costagli
- Imago7 Foundation, Pisa, Italy.,IRCCS Stella Maris, Pisa, Italy
| | - Alessandra Retico
- Pisa Division, National Institute for Nuclear Physics (INFN), Pisa, Italy
| | - Michela Tosetti
- Imago7 Foundation, Pisa, Italy.,IRCCS Stella Maris, Pisa, Italy
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29
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Jaubert O, Cruz G, Bustin A, Schneider T, Koken P, Doneva M, Rueckert D, Botnar RM, Prieto C. Free-running cardiac magnetic resonance fingerprinting: Joint T1/T2 map and Cine imaging. Magn Reson Imaging 2020; 68:173-182. [PMID: 32061964 PMCID: PMC7677167 DOI: 10.1016/j.mri.2020.02.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 01/21/2020] [Accepted: 02/09/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE To develop and evaluate a novel non-ECG triggered 2D magnetic resonance fingerprinting (MRF) sequence allowing for simultaneous myocardial T1 and T2 mapping and cardiac Cine imaging. METHODS Cardiac MRF (cMRF) has been recently proposed to provide joint T1/T2 myocardial mapping by triggering the acquisition to mid-diastole and relying on a subject-dependent dictionary of MR signal evolutions to generate the maps. In this work, we propose a novel "free-running" (non-ECG triggered) cMRF framework for simultaneous myocardial T1 and T2 mapping and cardiac Cine imaging in a single scan. Free-running cMRF is based on a transient state bSSFP acquisition with tiny golden angle radial readouts, varying flip angle and multiple adiabatic inversion pulses. The acquired data is retrospectively gated into several cardiac phases, which are reconstructed with an approach that combines parallel imaging, low rank modelling and patch-based high-order tensor regularization. Free-running cMRF was evaluated in a standardized phantom and ten healthy subjects. Comparison with reference spin-echo, MOLLI, SASHA, T2-GRASE and Cine was performed. RESULTS T1 and T2 values obtained with the proposed approach were in good agreement with reference phantom values (ICC(A,1) > 0.99). Reported values for myocardium septum T1 were 1043 ± 48 ms, 1150 ± 100 ms and 1160 ± 79 ms for MOLLI, SASHA and free-running cMRF respectively and for T2 of 51.7 ± 4.1 ms and 44.6 ± 4.1 ms for T2-GRASE and free-running cMRF respectively. Good agreement was observed between free-running cMRF and conventional Cine 2D ejection fraction (bias = -0.83%). CONCLUSION The proposed free-running cardiac MRF approach allows for simultaneous assessment of myocardial T1 and T2 and Cine imaging in a single scan.
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Affiliation(s)
- O Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - G Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - A Bustin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - T Schneider
- Philips Healthcare, Guilford, United Kingdom
| | - P Koken
- Philips Research Europe, Hamburg, Germany
| | - M Doneva
- Philips Research Europe, Hamburg, Germany
| | - D Rueckert
- Department of Computing, Imperial College London, London, United Kingdom
| | - R M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - C Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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30
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Ma S, Nguyen CT, Han F, Wang N, Deng Z, Binesh N, Moser FG, Christodoulou AG, Li D. Three-dimensional simultaneous brain T 1 , T 2 , and ADC mapping with MR Multitasking. Magn Reson Med 2019; 84:72-88. [PMID: 31765496 DOI: 10.1002/mrm.28092] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 10/01/2019] [Accepted: 10/31/2019] [Indexed: 02/06/2023]
Abstract
PURPOSE To develop a simultaneous T1 , T2 , and ADC mapping method that provides co-registered, distortion-free images and enables multiparametric quantification of 3D brain coverage in a clinically feasible scan time with the MR Multitasking framework. METHODS The T1 /T2 /diffusion weighting was generated by a series of T2 preparations and diffusion preparations. The underlying multidimensional image containing 3 spatial dimensions, 1 T1 weighting dimension, 1 T2 -preparation duration dimension, 1 b-value dimension, and 1 diffusion direction dimension was modeled as a 5-way low-rank tensor. A separate real-time low-rank model incorporating time-resolved phase correction was also used to compensate for both inter-shot and intra-shot phase inconsistency induced by physiological motion. The proposed method was validated on both phantom and 16 healthy subjects. The quantification of T1 /T2 /ADC was evaluated for each case. Three post-surgery brain tumor patients were scanned for demonstration of clinical feasibility. RESULTS Multitasking T1 /T2 /ADC maps were perfectly co-registered and free from image distortion. Phantom studies showed substantial quantitative agreement ( R 2 = 0.999 ) with reference protocols for T1 /T2 /ADC. In vivo studies showed nonsignificant T1 (P = .248), T2 (P = .97), ADC (P = .328) differences among the frontal, parietal, and occipital regions. Although Multitasking showed significant differences of T1 (P = .03), T2 (P < .001), and ADC (P = .001) biases against the references, the mean bias estimates were small (ΔT1 % < 5%, ΔT2 % < 7%, ΔADC% < 5%), with all intraclass correlation coefficients greater than 0.82 indicating "excellent" agreement. Patient studies showed that Multitasking T1 /T2 /ADC maps were consistent with the clinical qualitative images. CONCLUSION The Multitasking approach simultaneously quantifies T1 /T2 /ADC with substantial agreement with the references and is promising for clinical applications.
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Affiliation(s)
- Sen Ma
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California.,Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Christopher T Nguyen
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
| | - Fei Han
- Siemens Healthcare, Los Angeles, California
| | - Nan Wang
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California.,Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Zixin Deng
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Nader Binesh
- S. Mark Taper Foundation Imaging Center, Cedars-Sinai Medical Center, Los Angeles, California
| | - Franklin G Moser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California
| | | | - Debiao Li
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, California.,Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
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31
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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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Cruz G, Jaubert O, Botnar RM, Prieto C. Cardiac Magnetic Resonance Fingerprinting: Technical Developments and Initial Clinical Validation. Curr Cardiol Rep 2019; 21:91. [PMID: 31352620 PMCID: PMC6661029 DOI: 10.1007/s11886-019-1181-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE OF REVIEW Magnetic resonance imaging (MRI) has enabled non-invasive myocardial tissue characterization in a wide range of cardiovascular diseases by quantifying several tissue specific parameters such as T1, T2, and T2* relaxation times. Simultaneous assessment of these parameters has recently gained interest to potentially improve diagnostic accuracy and enable further understanding of the underlying disease. However, these quantitative maps are usually acquired sequentially and are not necessarily co-registered, making multi-parametric analysis challenging. Magnetic resonance fingerprinting (MRF) has been recently introduced to unify and streamline parametric mapping into a single simultaneous, multi-parametric, fully co-registered, and efficient scan. Feasibility of cardiac MRF has been demonstrated and initial clinical validation studies are ongoing. Provide an overview of the cardiac MRF framework, recent technical developments and initial undergoing clinical validation. RECENT FINDINGS Cardiac MRF has enabled the acquisition of co-registered T1 and T2 maps in a single, efficient scan. Initial results demonstrate feasibility of cardiac MRF in healthy subjects and small patient cohorts. Current in vivo results show a small bias and comparable precision in T1 and T2 with respect to conventional clinical parametric mapping approaches. This bias may be explained by several confounding factors such as magnetization transfer and field inhomogeneities, which are currently not included in the cardiac MRF model. Initial clinical validation for cardiac MRF has demonstrated good reproducibility in healthy subjects and heart transplant patients, reduced artifacts in inflammatory cardiomyopathy patients and good differentiation between hypertrophic cardiomyopathy and healthy controls. Cardiac MRF has emerged as a novel technique for simultaneous, multi-parametric, and co-registered mapping of different tissue parameters. Initial efforts have focused on enabling T1, T2, and fat quantification; however this approach has the potential of enabling quantification of several other parameters (such as T2*, diffusion, perfusion, and flow) from a single scan. Initial results in healthy subjects and patients are promising, thus further clinical validation is now warranted.
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Affiliation(s)
- G. Cruz
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - O. Jaubert
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - R. M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
- Pontificia Universidad Católica de Chile Escuela de Ingeniería, Santiago, Chile
| | - C. Prieto
- School of Biomedical Engineering and Imaging Sciences, King’s College London, 3rd Floor, Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
- Pontificia Universidad Católica de Chile Escuela de Ingeniería, Santiago, Chile
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33
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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: 37] [Impact Index Per Article: 7.4] [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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34
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Kato Y, Ichikawa K, Okudaira K, Taoka T, Kawaguchi H, Murata K, Maruyama K, Koerzdoerfer G, Pfeuffer J, Nittka M, Naganawa S. Comprehensive Evaluation of B 1+-corrected FISP-based Magnetic Resonance Fingerprinting: Accuracy, Repeatability and Reproducibility of T 1 and T 2 Relaxation Times for ISMRM/NIST System Phantom and Volunteers. Magn Reson Med Sci 2019; 19:168-175. [PMID: 31217366 PMCID: PMC7553811 DOI: 10.2463/mrms.mp.2019-0016] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Purpose: This study aimed to evaluate comprehensively; accuracy, repeatability and reproducibility of T1 and T2 relaxation times measured by magnetic resonance fingerprinting using B1+-corrected fast imaging with steady-state precession (FISP–MRF). Methods: The International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) phantom was scanned for 100 days, and six healthy volunteers for 5 days using a FISP–MRF prototype sequence. Accuracy was evaluated on the phantom by comparing relaxation times measured by FISP–MRF with the reference values provided by the phantom manufacturer. Daily repeatability was characterized as the coefficient of variation (CV) of the measurements over 100 days for the phantom and over 5 days for volunteers. In addition, the cross-scanner reproducibility was evaluated in volunteers. Results: In the phantom study, T1 and T2 values from FISP–MRF showed a strong linear correlation with the reference values of the phantom (R2 = 0.9963 for T1; R2 = 0.9966 for T2). CVs were <1.0% for T1 values larger than 300 ms, and <3.0% for T2 values across a wide range. In the volunteer study, CVs for both T1 and T2 values were <5.0%, except for one subject. In addition, all T2 values estimated by FISP–MRF in vivo were lower than those measured with conventional mapping sequences reported in previous studies. The cross-scanner variation of T1 and T2 showed good agreement between two different scanners in the volunteers. Conclusion: B1+-corrected FISP-MRF showed an acceptable accuracy, repeatability and reproducibility in the phantom and volunteer studies.
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Affiliation(s)
- Yutaka Kato
- Department of Radiological Technology, Nagoya University Hospital
| | | | | | - Toshiaki Taoka
- Department of Radiology, Graduate School of Medicine, Nagoya University
| | | | | | | | | | | | | | - Shinji Naganawa
- Department of Radiology, Graduate School of Medicine, Nagoya University
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35
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Naganawa S, Nakane T, Kawai H, Taoka T, Kawaguchi H, Maruyama K, Murata K, Körzdörfer G, Pfeuffer J, Nittka M, Sone M. Detection of IV-gadolinium Leakage from the Cortical Veins into the CSF Using MR Fingerprinting. Magn Reson Med Sci 2019; 19:141-146. [PMID: 31217367 PMCID: PMC7232034 DOI: 10.2463/mrms.mp.2019-0048] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Purpose: It has been reported that leakage of intravenously administered gadolinium-based contrast agents (IV-GBCAs) into the cerebrospinal fluid (CSF) from the cortical veins even in healthy subjects can be detected using a highly sensitive pulse sequence such as heavily T2-weighted 3D fluid-attenuated inversion recovery and 3D-real inversion recovery (IR). The purpose of this study was to evaluate the feasibility of MR fingerprinting to detect GBCA leakage from the cortical veins after IV-GBCA. Materials: Fourteen patients with suspected endolymphatic hydrops (EH) who received a single dose of IV-GBCA (39–79 years old) were included. The real IR images as well as MR fingerprinting images were obtained at 4 h after IV-GBCA. T1 and T2 values were obtained using MR fingerprinting and analyzed in ROIs covering intense GBCA leakage, and non-leakage areas of the CSF as determined on real IR images. The scan time for real IR imaging was 10 min and that for MR fingerprinting was 41 s. Results: The mean T1 value of the ROI in the area of GBCA leakage was 2422 ± 261 ms and that in the non-leakage area was 3851 ± 235 ms (P < 0.01). There was no overlap between the T1 values in the area of GBCA leakage and those in the non-leakage area. The mean T2 value in the area of GBCA leakage was 319 ± 90 ms and that in the non-leakage area was 670 ± 166 ms (P < 0.01). There was some overlap between the T2 values in the area of GBCA leakage and those in the non-leakage area. Conclusion: Leaked GBCA from the cortical veins into the surrounding CSF can be detected using MR fingerprinting obtained in <1 min.
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Affiliation(s)
- Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine
| | - Toshiki Nakane
- Department of Radiology, Nagoya University Graduate School of Medicine
| | - Hisashi Kawai
- Department of Radiology, Nagoya University Graduate School of Medicine
| | - Toshiaki Taoka
- Department of Radiology, Nagoya University Graduate School of Medicine
| | | | | | | | - Gregor Körzdörfer
- Siemens Healthcare GmbH.,Friedrich-Alexander-Universität Erlangen-Nürnberg
| | | | | | - Michihiko Sone
- Department of Otorhinolaryngology, Nagoya University Graduate School of Medicine
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36
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Lima da Cruz G, Bustin A, Jaubert O, Schneider T, Botnar RM, Prieto C. Sparsity and locally low rank regularization for MR fingerprinting. Magn Reson Med 2019; 81:3530-3543. [PMID: 30720209 PMCID: PMC6492150 DOI: 10.1002/mrm.27665] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 12/03/2018] [Accepted: 12/29/2018] [Indexed: 12/22/2022]
Abstract
PURPOSE Develop a sparse and locally low rank (LLR) regularized reconstruction to accelerate MR fingerprinting (MRF). METHODS Recent works have introduced low rank reconstructions to MRF, based on temporal compression operators learned from the MRF dictionary. In other MR applications, LLR regularization has been introduced to exploit temporal redundancy in local regions of the image. Here, we propose to include spatial sparsity and LLR regularization terms in the MRF reconstruction. This approach, so called SLLR-MRF, further reduces aliasing in the time-point images and enables higher acceleration factors. The proposed approach was evaluated in simulations, T1 /T2 phantom acquisition, and in vivo brain acquisitions in 5 healthy subjects with different undersampling factors. Acceleration was also used in vivo to enable acquisitions with higher in-plane spatial resolution in comparable scan time. RESULTS Simulations, phantom, and in vivo results show that low rank MRF reconstructions with high acceleration factors (<875 time-point images, 1 radial spoke per time-point) have residual aliasing artifacts that propagate into the parametric maps. The artifacts are reduced with the proposed SLLR-MRF resulting in considerable improvements in precision, without changes in accuracy. In vivo results show improved parametric maps for the proposed SLLR-MRF, potentially enabling MRF acquisitions with 1 radial spoke per time-point in approximately 2.6 s (~600 time-point images) for 2 × 2 mm and 9.6 s (1750 time-point images) for 1 × 1 mm in-plane resolution. CONCLUSION The proposed SLLR-MRF reconstruction further improves parametric map quality compared with low rank MRF, enabling shorter scan times and/or increased spatial resolution.
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Affiliation(s)
- Gastão Lima da Cruz
- King’s College LondonSchool of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
| | - Aurélien Bustin
- King’s College LondonSchool of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
| | - Oliver Jaubert
- King’s College LondonSchool of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
| | | | - René M. Botnar
- King’s College LondonSchool of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
- Pontificia Universidad Católica de ChileEscuela de IngenieríaSantiagoChile
| | - Claudia Prieto
- King’s College LondonSchool of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
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37
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Cao X, Ye H, Liao C, Li Q, He H, Zhong J. Fast 3D brain MR fingerprinting based on multi-axis spiral projection trajectory. Magn Reson Med 2019; 82:289-301. [PMID: 30883867 DOI: 10.1002/mrm.27726] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 02/09/2019] [Accepted: 02/12/2019] [Indexed: 01/12/2023]
Abstract
PURPOSE To develop a fast, sub-millimeter 3D magnetic resonance fingerprinting (MRF) technique for whole-brain quantitative scans. METHODS An acquisition trajectory based on multi-axis spiral projection imaging (maSPI) was implemented for 3D MRF with steady-state precession and slab excitation. By appropriately assigning the in-plane and through-plane rotations of spiral interleaves in a novel acquisition scheme, an maSPI-based acquisition was implemented, and the total acquisition time was reduced by up to a factor of 8 compared to stack-of-spiral (SOS)-based acquisition. A sliding-window method was also used to further reduce the required number of time points for a faster acquisition. The experiments were conducted both on a phantom and in vivo. RESULTS The results from the phantom measurements with the proposed and gold standard methods were consistent with a good linear correlation and an R2 value approaching 0.99. The in vivo experiments achieved whole-brain parametric maps with isotropic resolutions of 1 mm and 0.8 mm in 5.0 and 6.0 min, respectively, with potential for further acceleration. An in vivo experiment with intentionally moving subjects demonstrated that the maSPI scheme largely outperforms the SOS scheme in terms of robustness to head motion. CONCLUSION 3D MRF with an maSPI acquisition scheme enables fast and robust scans for high-resolution parametric mapping.
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Affiliation(s)
- 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
| | - Huihui Ye
- 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.,State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - 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
| | - Qing Li
- 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
| | - Hongjian He
- 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.,Department of Imaging Sciences, University of Rochester, Rochester, New York
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