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Martinez JA, Yu VY, Tringale KR, Otazo R, Cohen O. Phase-sensitive deep reconstruction method for rapid multiparametric MR fingerprinting and quantitative susceptibility mapping in the brain. Magn Reson Imaging 2024; 109:147-157. [PMID: 38513790 PMCID: PMC11042874 DOI: 10.1016/j.mri.2024.03.023] [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: 12/01/2023] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 03/23/2024]
Abstract
INTRODUCTION This study explores the potential of Magnetic Resonance Fingerprinting (MRF) with a novel Phase-Sensitivity Deep Reconstruction Network (PS-DRONE) for simultaneous quantification of T1, T2, Proton Density, B1+, phase and quantitative susceptibility mapping (QSM). METHODS Data were acquired at 3 T in vitro and in vivo using an optimized EPI-based MRF sequence. Phantom experiments were conducted using a standardized phantom for T1 and T2 maps and a custom-made agar-based gadolinium phantom for B1 and QSM maps. In vivo experiments included five healthy volunteers and one patient diagnosed with brain metastasis. PSDRONE maps were compared to reference maps obtained through standard imaging sequences. RESULTS Total scan time was 2 min for 32 slices and a resolution of [1 mm, 1 mm, 4.5 mm]. The reconstruction of T1, T2, Proton Density, B1+ and phase maps were reconstructed within 1 s. In the phantoms, PS-DRONE analysis presented accurate and strongly correlated T1 and T2 maps (r = 0.99) compared to the reference maps. B1 maps from PS-DRONE showed slightly higher values, though still correlated (r = 0.6) with the reference. QSM values showed a small bias but were strongly correlated (r = 0.99) with reference data. In the in vivo analysis, PS-DRONE-derived T1 and T2 values for gray and white matter matched reference values in healthy volunteers. PS-DRONE B1 and QSM maps showed strong correlations with reference values. CONCLUSION The PS-DRONE network enables concurrent acquisition of T1, T2, PD, B1+, phase and QSM maps, within 2 min of acquisition time and 1 s of reconstruction time.
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Affiliation(s)
- Jessica A Martinez
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA.
| | - Victoria Y Yu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Kathryn R Tringale
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA
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Slioussarenko C, Baudin PY, Reyngoudt H, Marty B. Bi-component dictionary matching for MR fingerprinting for efficient quantification of fat fraction and water T 1 in skeletal muscle. Magn Reson Med 2024; 91:1179-1189. [PMID: 37867467 DOI: 10.1002/mrm.29901] [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: 07/28/2023] [Revised: 09/15/2023] [Accepted: 10/06/2023] [Indexed: 10/24/2023]
Abstract
PURPOSE To propose an efficient bi-component MR fingerprinting (MRF) fitting method using a Variable Projection (VARPRO) strategy, applied to the quantification of fat fraction (FF) and water T1 (T 1 H 2 0 $$ \mathrm{T}{1}_{{\mathrm{H}}_20} $$ ) in skeletal muscle tissues. METHODS The MRF signals were analyzed in a two-step process by comparing them to the elements of separate water and fat dictionaries (bi-component dictionary matching). First, each pair of water and fat dictionary elements was fitted to the acquired signal to determine an optimal FF that was used to merge the fingerprints in a combined water/fat dictionary. Second, standard dictionary matching was applied to the combined dictionary for determining the remaining parameters. A clustering method was implemented to further accelerate the fitting. Accuracy, precision, and matching time of this approach were evaluated on both numerical and in vivo datasets, and compared to the reference dictionary-matching approach that includes FF as a dictionary parameter. RESULTS In numerical phantoms, all MRF parameters showed high correlation with ground truth for the reference and the bi-component method (R2 > 0.98). In vivo, the estimated parameters from the proposed method were highly correlated with those from the reference approach (R2 > 0.997). The bi-component method achieved an acceleration factor of up to 360 compared to the reference dictionary matching. CONCLUSION The proposed bi-component fitting approach enables a significant acceleration of the reconstruction of MRF parameter maps for fat-water imaging, while maintaining comparable precision and accuracy to the reference on FF andT 1 H 2 0 $$ \mathrm{T}{1}_{{\mathrm{H}}_20} $$ estimation.
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Affiliation(s)
| | - Pierre-Yves Baudin
- Institute of Myology, Neuromuscular Investigation Center, NMR Laboratory, Paris, France
| | - Harmen Reyngoudt
- Institute of Myology, Neuromuscular Investigation Center, NMR Laboratory, Paris, France
| | - Benjamin Marty
- Institute of Myology, Neuromuscular Investigation Center, NMR Laboratory, Paris, France
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Monga A, Singh D, de Moura HL, Zhang X, Zibetti MVW, Regatte RR. Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review. Bioengineering (Basel) 2024; 11:236. [PMID: 38534511 DOI: 10.3390/bioengineering11030236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/28/2024] Open
Abstract
Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.
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Affiliation(s)
- Anmol Monga
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Dilbag Singh
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Hector L de Moura
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Xiaoxia Zhang
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Marcelo V W Zibetti
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Ravinder R Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
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Rossi GMC, Mackowiak ALC, Açikgöz BC, Pierzchała K, Kober T, Hilbert T, Bastiaansen JAM. SPARCQ: A new approach for fat fraction mapping using asymmetries in the phase-cycled balanced SSFP signal profile. Magn Reson Med 2023; 90:2348-2361. [PMID: 37496187 DOI: 10.1002/mrm.29813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/19/2023] [Accepted: 07/12/2023] [Indexed: 07/28/2023]
Abstract
PURPOSE To develop SPARCQ (Signal Profile Asymmetries for Rapid Compartment Quantification), a novel approach to quantify fat fraction (FF) using asymmetries in the phase-cycled balanced SSFP (bSSFP) profile. METHODS SPARCQ uses phase-cycling to obtain bSSFP frequency profiles, which display asymmetries in the presence of fat and water at certain TRs. For each voxel, the measured signal profile is decomposed into a weighted sum of simulated profiles via multi-compartment dictionary matching. Each dictionary entry represents a single-compartment bSSFP profile with a specific off-resonance frequency and relaxation time ratio. Using the results of dictionary matching, the fractions of the different off-resonance components are extracted for each voxel, generating quantitative maps of water and FF and banding-artifact-free images for the entire image volume. SPARCQ was validated using simulations, experiments in a water-fat phantom and in knees of healthy volunteers. Experimental results were compared with reference proton density FFs obtained with 1 H-MRS (phantoms) and with multiecho gradient-echo MRI (phantoms and volunteers). SPARCQ repeatability was evaluated in six scan-rescan experiments. RESULTS Simulations showed that FF quantification is accurate and robust for SNRs greater than 20. Phantom experiments demonstrated good agreement between SPARCQ and gold standard FFs. In volunteers, banding-artifact-free quantitative maps and water-fat-separated images obtained with SPARCQ and ME-GRE demonstrated the expected contrast between fatty and non-fatty tissues. The coefficient of repeatability of SPARCQ FF was 0.0512. CONCLUSION SPARCQ demonstrates potential for fat quantification using asymmetries in bSSFP profiles and may be a promising alternative to conventional FF quantification techniques.
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Affiliation(s)
- Giulia M C Rossi
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Translational Imaging Center, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Adèle L C Mackowiak
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Translational Imaging Center, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Berk Can Açikgöz
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Translational Imaging Center, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Katarzyna Pierzchała
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tobias Kober
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jessica A M Bastiaansen
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Translational Imaging Center, Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
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Tourais J, Ploem T, van Zadelhoff TA, van de Steeg-Henzen C, Oei EHG, Weingartner S. Rapid Whole-Knee Quantification of Cartilage Using T 1, T 2*, and T RAFF2 Mapping With Magnetic Resonance Fingerprinting. IEEE Trans Biomed Eng 2023; 70:3197-3205. [PMID: 37227911 DOI: 10.1109/tbme.2023.3280115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
OBJECTIVE Quantitative Magnetic Resonance Imaging (MRI) holds great promise for the early detection of cartilage deterioration. Here, a Magnetic Resonance Fingerprinting (MRF) framework is proposed for comprehensive and rapid quantification of T1, T2*, and TRAFF2 with whole-knee coverage. METHODS A MRF framework was developed to achieve quantification of Relaxation Along a Fictitious Field in the 2nd rotating frame of reference ( TRAFF2) along with T1 and T2*. The proposed sequence acquires 65 measurements of 25 high-resolution slices, interleaved with 7 inversion pulses and 40 RAFF2 trains, for whole-knee quantification in a total acquisition time of 3:25 min. Comparison with reference T1, T2*, and TRAFF2 methods was performed in phantom and in seven healthy subjects at 3 T. Repeatability (test-retest) with and without repositioning was also assessed. RESULTS Phantom measurements resulted in good agreement between MRF and the reference with mean biases of -54, 2, and 5 ms for T1, T2*, and TRAFF2, respectively. Complete characterization of the whole-knee cartilage was achieved for all subjects, and, for the femoral and tibial compartments, a good agreement between MRF and reference measurements was obtained. Across all subjects, the proposed MRF method yielded acceptable repeatability without repositioning ( R2 ≥ 0.94) and with repositioning ( R2 ≥ 0.57) for T1, T2*, and TRAFF2. SIGNIFICANCE The short scan time combined with the whole-knee coverage makes the proposed MRF framework a promising candidate for the early assessment of cartilage degeneration with quantitative MRI, but further research may be warranted to improve repeatability after repositioning and assess clinical value in patients.
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Nagtegaal MA, Hermann I, Weingärtner S, Martinez-Heras E, Solana E, Llufriu S, Gass A, Poot DHJ, van Osch MJP, Vos FM, de Bresser J. White matter changes measured by multi-component MR Fingerprinting in multiple sclerosis. Neuroimage Clin 2023; 40:103528. [PMID: 37837891 PMCID: PMC10589890 DOI: 10.1016/j.nicl.2023.103528] [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: 03/06/2023] [Revised: 09/11/2023] [Accepted: 10/09/2023] [Indexed: 10/16/2023]
Abstract
T2-hyperintense lesions are the key imaging marker of multiple sclerosis (MS). Previous studies have shown that the white matter surrounding such lesions is often also affected by MS. Our aim was to develop a new method to visualize and quantify the extent of white matter tissue changes in MS based on relaxometry properties. We applied a fast, multi-parametric quantitative MRI approach and used a multi-component MR Fingerprinting (MC-MRF) analysis. We assessed the differences in the MRF component representing prolongedrelaxation time between patients with MS and controls and studied the relation between this component's volume and structural white matter damage identified on FLAIR MRI scans in patients with MS. A total of 48 MS patients at two different sites and 12 healthy controls were scanned with FLAIR and MRF-EPI MRI scans. MRF scans were analyzed with a joint-sparsity multi-component analysis to obtain magnetization fraction maps of different components, representing tissues such as myelin water, white matter, gray matter and cerebrospinal fluid. In the MS patients, an additional component was identified with increased transverse relaxation times compared to the white matter, likely representing changes in free water content. Patients with MS had a higher volume of the long- component in the white matter of the brain compared to healthy controls (B (95%-CI) = 0.004 (0.0006-0.008), p = 0.02). Furthermore, this MRF component had a moderate correlation (correlation coefficient R 0.47) with visible structural white matter changes on the FLAIR scans. Also, the component was found to be more extensive compared to structural white matter changes in 73% of MS patients. In conclusion, our MRF acquisition and analysis captured white matter tissue changes in MS patients compared to controls. In patients these tissue changes were more extensive compared to visually detectable white matter changes on FLAIR scans. Our method provides a novel way to quantify the extent of white matter changes in MS patients, which is underestimated using only conventional clinical MRI scans.
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Affiliation(s)
- Martijn A Nagtegaal
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands; C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Ingo Hermann
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - Eloy Martinez-Heras
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM). Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Elisabeth Solana
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM). Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Sara Llufriu
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM). Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Achim Gass
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Dirk H J Poot
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Matthias J P van Osch
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frans M Vos
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Jeroen de Bresser
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
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Ostenson J, Robison RK, Brittain EL, Damon BM. Feasibility of joint mapping of triglyceride saturation and water longitudinal relaxation in a single breath hold applied to high fat-fraction adipose depots in the periclavicular anatomy. Magn Reson Imaging 2023; 99:58-66. [PMID: 36764629 PMCID: PMC10088071 DOI: 10.1016/j.mri.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/27/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023]
Abstract
INTRODUCTION Simultaneous mapping of triglyceride (TAG) saturation and tissue water relaxation may improve the characterization of the structure and function of anatomies with significant adipose tissue. While several groups have demonstrated in vivo TAG saturation imaging using MRI, joint mapping of relaxation and TAG saturation is understudied. Such mappings may avoid bias from physiological motion, if they can be done within a single breath-hold, and also account for static and applied magnetic field heterogeneity. METHODS We propose a transient-state/MR fingerprinting single breath-hold sequence at 3 T, a low-rank reconstruction, and a parameter estimation pipeline that jointly estimates the number of double bonds (NDB), number of methylene interrupted double bonds (NMIDB), and tissue water T1, while accounting for non-ideal radiofrequency transmit scaling and off-resonance effects. We test the proposed method in simulations, in phantom against MR spectroscopy (MRS), and in vivo regions in and around high fat fraction (FF) periclavicular adipose tissue. Partial volume and multi-peak transverse relaxation effects are explored. RESULTS The simulation results demonstrate accurate NDB, NMIDB, and water T1 estimates across a range of NDB, NMIDB, and T1 values. In phantoms, the proposed method's estimates of NDB and NMIDB correlate with those from MR spectroscopy (Pearson correlation ≥0.98), while the water T1 estimates are concordant with a standard phantom. The NDB and NMIDB are sensitive to partial volumes of water, showing increasing bias at FF < 40%. This bias is found to be due to noise and transverse relaxation effects. The in vivo periclavicular adipose tissue has high FF (>90%). The adipose tissue NDB and NMIDB, and muscle T1 estimates are comparable to those reported in the literature. CONCLUSION Robust estimation of NDB, NMIDB at high FF and water T1 across a broad range of FFs are feasible using the proposed methods. Further reduction of noise and model bias are needed to employ the proposed technique in low FF anatomies and pathologies.
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Affiliation(s)
- Jason Ostenson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America.
| | - Ryan K Robison
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Philips, Gainesville, FL, United States of America
| | - Evan L Brittain
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Bruce M Damon
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America; Carle Clinical Imaging Research Program, Urbana, IL, United States of America; Stephens Family Clinical Research Institute, Carle Health, Urbana, IL, United States of America; Department of Bioengineering and Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
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Nassar J, Trabelsi A, Amer R, Le Fur Y, Attarian S, Radunsky D, Blumenfeld-Katzir T, Greenspan H, Bendahan D, Ben-Eliezer N. Estimation of subvoxel fat infiltration in neurodegenerative muscle disorders using quantitative multi-T 2 analysis. NMR IN BIOMEDICINE 2023:e4947. [PMID: 37021657 DOI: 10.1002/nbm.4947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 02/13/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
MRI's T2 relaxation time is a valuable biomarker for neuromuscular disorders and muscle dystrophies. One of the hallmarks of these pathologies is the infiltration of adipose tissue and a loss of muscle volume. This leads to a mixture of two signal components, from fat and from water, to appear in each imaged voxel, each having a specific T2 relaxation time. In this proof-of-concept work, we present a technique that can separate the signals from water and from fat within each voxel, measure their separate T2 values, and calculate their relative fractions. The echo modulation curve (EMC) algorithm is a dictionary-based technique that offers accurate and reproducible mapping of T2 relaxation times. We present an extension of the EMC algorithm for estimating subvoxel fat and water fractions, alongside the T2 and proton-density values of each component. To facilitate data processing, calf and thigh anatomy were automatically segmented using a fully convolutional neural network and FSLeyes software. The preprocessing included creating two signal dictionaries, for water and for fat, using Bloch simulations of the prospective protocol. Postprocessing included voxelwise fitting for two components, by matching the experimental decay curve to a linear combination of the two simulated dictionaries. Subvoxel fat and water fractions and relaxation times were generated and used to calculate a new quantitative biomarker, termed viable muscle index, and reflecting disease severity. This biomarker indicates the fraction of remaining muscle out of the entire muscle region. The results were compared with those using the conventional Dixon technique, showing high agreement (R = 0.98, p < 0.001). It was concluded that the new extension of the EMC algorithm can be used to quantify abnormal fat infiltration as well as identify early inflammatory processes corresponding to elevation in the T2 value of the water (muscle) component. This new ability may improve the diagnostic accuracy of neuromuscular diseases, help stratification of patients according to disease severity, and offer an efficient tool for tracking disease progression.
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Affiliation(s)
- Jannette Nassar
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | | | - Rula Amer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | | | - Shahram Attarian
- Reference Center for Neuromuscular Diseases and ALS, La Timone University Hospital, Aix-Marseille University, Marseille, France
- Inserm, GMGF, Aix Marseille University, Marseille, France
| | - Dvir Radunsky
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | | | - Hayit Greenspan
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | | | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center, New York, New York, USA
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Liu Y, Hamilton J, Jiang Y, Seiberlich N. Cardiac MRF using rosette trajectories for simultaneous myocardial T1, T2, and proton density fat fraction mapping. Front Cardiovasc Med 2022; 9:977603. [PMID: 36204572 PMCID: PMC9530568 DOI: 10.3389/fcvm.2022.977603] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/25/2022] [Indexed: 11/22/2022] Open
Abstract
The goal of this work is to extend prior work on cardiac MR Fingerprinting (cMRF) using rosette k-space trajectories to enable simultaneous T1, T2, and proton density fat fraction (PDFF) mapping in the heart. A rosette trajectory designed for water-fat separation at 1.5T was used in a 2D ECG-triggered 15-heartbeat cMRF sequence. Water and fat specific T1 and T2 maps were generated from the cMRF data. A PDFF map was also retrieved using Hierarchical IDEAL by segmenting the rosette cMRF data into multiple echoes. The accuracy of rosette cMRF in T1, T2, and PDFF quantification was validated in the ISMRM/NIST phantom and an in-house built fat fraction phantom, respectively. The proposed method was also applied for myocardial tissue mapping of healthy subjects and cardiac patients at 1.5T. T1, T2, and PDFF values measured using rosette cMRF in the ISMRM/NIST phantom and the fat fraction phantom agreed well with the reference values. In 16 healthy subjects, rosette cMRF yielded T1 values which were 80~90 ms higher than spiral cMRF and MOLLI. T2 values obtained using rosette cMRF were ~3 ms higher than spiral cMRF and ~5 ms lower than conventional T2-prep bSSFP method. Rosette cMRF was also able to detect abnormal T1 and T2 values in cardiomyopathy patients and may provide more accurate maps due to effective fat suppression. In conclusion, this study shows that rosette cMRF has the potential for efficient cardiac tissue characterization through simultaneous quantification of myocardial T1, T2, and PDFF.
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Affiliation(s)
- Yuchi Liu
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- *Correspondence: Yuchi Liu
| | - Jesse Hamilton
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Yun Jiang
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
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Lo WC, Panda A, Jiang Y, Ahad J, Gulani V, Seiberlich N. MR fingerprinting of the prostate. MAGMA (NEW YORK, N.Y.) 2022; 35:557-571. [PMID: 35419668 DOI: 10.1007/s10334-022-01012-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 03/21/2022] [Accepted: 03/24/2022] [Indexed: 06/03/2023]
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has been adopted as the key tool for detection, localization, characterization, and risk stratification of patients suspected to have prostate cancer. Despite advantages over systematic biopsy, the interpretation of prostate mpMRI has limitations including a steep learning curve, leading to considerable interobserver variation. There is growing interest in clinical translation of quantitative imaging techniques for more objective lesion assessment. However, traditional mapping techniques are slow, precluding their use in the clinic. Magnetic resonance fingerprinting (MRF) is an efficient approach for quantitative maps of multiple tissue properties simultaneously. The T1 and T2 values obtained with MRF have been validated with phantom studies as well as in normal volunteers and patients. Studies have shown that MRF-derived T1 and T2 along with ADC values are all significant independent predictors in the differentiation between normal prostate tissue and prostate cancer, and hold promise in differentiating low and intermediate/high-grade cancers. This review seeks to introduce the basics of the prostate MRF technique, discuss the potential applications of prostate MRF for the characterization of prostate cancer, and describes ongoing areas of research.
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Affiliation(s)
- Wei-Ching Lo
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Siemens Medical Solutions USA, Boston, Massachusetts, USA
| | - Ananya Panda
- Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, MN, 55905, USA
| | - Yun Jiang
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA
| | - James Ahad
- Case Western Reserve University, Cleveland, OH, USA
| | - Vikas Gulani
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA.
- Case Western Reserve University, Cleveland, OH, USA.
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11
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Reducing SAR in 7T brain fMRI by circumventing fat suppression while removing the lipid signal through a parallel acquisition approach. Sci Rep 2021; 11:15371. [PMID: 34321529 PMCID: PMC8319205 DOI: 10.1038/s41598-021-94692-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 07/09/2021] [Indexed: 12/14/2022] Open
Abstract
Ultra-high-field functional magnetic resonance imaging (fMRI) offers a way to new insights while increasing the spatial and temporal resolution. However, a crucial concern in 7T human MRI is the increase in power deposition, supervised through the specific absorption rate (SAR). The SAR limitation can restrict the brain coverage or the minimal repetition time of fMRI experiments. In the majority of today’s studies fMRI relies on the well-known gradient-echo echo-planar imaging (GRE-EPI) sequence, which offers ultrafast acquisition. Commonly, the GRE-EPI sequence comprises two pulses: fat suppression and excitation. This work provides the means for a significant reduction in the SAR by circumventing the fat-suppression pulse. Without this fat-suppression, however, lipid signal can result in artifacts due to the chemical shift between the lipid and water signals. Our approach exploits a reconstruction similar to the simultaneous-multi-slice method to separate the lipid and water images, thus avoiding undesired lipid artifacts in brain images. The lipid-water separation is based on the known spatial shift of the lipid signal, which can be detected by the multi-channel coils sensitivity profiles. Our study shows robust human imaging, offering greater flexibility to reduce the SAR, shorten the repetition time or increase the volume coverage with substantial benefit for brain functional studies.
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12
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Tippareddy C, Zhao W, Sunshine JL, Griswold M, Ma D, Badve C. Magnetic resonance fingerprinting: an overview. Eur J Nucl Med Mol Imaging 2021; 48:4189-4200. [PMID: 34037831 DOI: 10.1007/s00259-021-05384-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 04/25/2021] [Indexed: 12/17/2022]
Abstract
Magnetic resonance fingerprinting (MRF) is an evolving quantitative MRI framework consisting of unique data acquisition, processing, visualization, and interpretation steps. MRF is capable of simultaneously producing multiple high-resolution property maps including T1, T2, M0, ADC, and T2* measurements. While a relatively new technology, MRF has undergone rapid development for a variety of clinical applications from brain tumor characterization and epilepsy imaging to characterization of prostate cancer, cardiac imaging, among others. This paper will provide a brief overview of current state of MRF technology including highlights of technical and clinical advances. We will conclude with a brief discussion of the challenges that need to be overcome to establish MRF as a quantitative imaging biomarker.
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Affiliation(s)
- Charit Tippareddy
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Walter Zhao
- Case Western Reserve University School of Medicine, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Jeffrey L Sunshine
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Mark Griswold
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA.,Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, 11100 Euclid Ave., Cleveland, OH, 44106, USA
| | - Chaitra Badve
- Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, 11100 Euclid Ave., Cleveland, OH, 44106, USA.
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13
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Quantification of T1, T2 relaxation times from Magnetic Resonance Fingerprinting radially undersampled data using analytical transformations. Magn Reson Imaging 2021; 80:81-89. [PMID: 33932541 DOI: 10.1016/j.mri.2021.04.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/16/2021] [Accepted: 04/25/2021] [Indexed: 01/03/2023]
Abstract
Quantitative magnetic resonance imaging (MRI) estimates magnetic parameters related to tissue, such as T1, T2 relaxation times and proton density. MR fingerprinting (MRF) is a new concept that uses pseudo-random, incoherent measurements to create a unique fingerprint for each tissue type to quantify magnet parameters. This paper aims to enhance MRF performance by investigating (i) the most suitable acquisition trajectory, and (ii) analytical transformations, suitable for radial acquisitions. Highly undersampled MRF brain (k, t)-space data have been simulated and non-linearly reconstructed to exploit the low-rank property of dynamic imaging. Based on our findings, the radial trajectory is the most suitable for MRF compared to Cartesian and spiral acquisitions. Perhaps this is due to the fact that its aliasing artifacts are more noise-like, and that unlike spiral trajectories, it can use analytical transformations that do not require re-gridding. One such analytical algorithm is the spline reconstruction technique (SRT) that is based on a novel numerical implementation of an analytic representation of the inverse Radon transform. Here, for the first time, this algorithm is applied to MR radial data. Reconstructions using SRT were compared to the ones using filtered back-projection. SRT provided images of higher contrast, lower bias, which resulted in more accurate T1, T2 values.
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14
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Ahn JH, Yu JS, Park KS, Kang SH, Huh JH, Chang JS, Lee JH, Kim MY, Nickel MD, Kannengiesser S, Kim JY, Koh SB. Effect of hepatic steatosis on native T1 mapping of 3T magnetic resonance imaging in the assessment of T1 values for patients with non-alcoholic fatty liver disease. Magn Reson Imaging 2021; 80:1-8. [PMID: 33798658 DOI: 10.1016/j.mri.2021.03.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/18/2021] [Accepted: 03/18/2021] [Indexed: 02/09/2023]
Abstract
PURPOSE This study investigated whether T1 values in native T1 mapping of 3T magnetic resonance imaging (MRI) of the liver were affected by the fatty component. METHODS This prospective study involved 340 participants from a population-based cohort study between May 8, 2018 and August 8, 2019. Data obtained included: (1) hepatic stiffness according to magnetic resonance elastography (MRE); (2) T1 value according to T1 mapping; (3) fat fraction and iron concentration from multi-echo Dixon; and (4) clinical indices of hepatic steatosis including body mass index, waist circumference, history of diabetes, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transpeptidase, and triglycerides. The correlations between T1 value and fat fraction, and between T1 value and liver stiffness were assessed using Pearson's correlation coefficient. The independent two-sample t-test was used to evaluate the differences in T1 values according to the presence or absence of hepatic steatosis, and the one-way analysis of variance was used to evaluate the difference in T1 value by grading of hepatic steatosis according to MRI-based proton density fat fraction (PDFF). In addition, univariate and multivariate linear regression analyses were performed to determine whether other variables influenced the T1 value. RESULTS T1 value showed a positive correlation with the fat fraction obtained from PDFF (r = 0.615, P < 0.001) and with the liver stiffness obtained from MRE (r = 0.370, P < 0.001). Regardless of the evaluation method, the T1 value was significantly increased in subjects with hepatic steatosis (P < 0.001). When comparing hepatic steatosis grades based on MRI-PDFF, the mean T1 values were significantly different in all grades, and the T1 value tended to increase as the grade increased (P < 0.001, P for trend <0.001). On multiple linear regression analysis, the T1 value was influenced by MRI-PDFF, calculated liver iron concentration, liver stiffness, and serum aspartate aminotransferase level. CONCLUSION The T1 value obtained by current T1 mapping of 3T MRI was affected by the liver fat component and several other factors such as liver stiffness, iron concentration, and inflammation.
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Affiliation(s)
- Jhii-Hyun Ahn
- Department of Radiology, Wonju Severance Christian Hospital, Yonsei University College of Medicine, Republic of Korea
| | - Jeong-Sik Yu
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Kyu-Sang Park
- Mitohormesis Research Center, Department of Physiology, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Seong Hee Kang
- Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Ji Hye Huh
- Department of Internal Medicine, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea
| | - Jae Seung Chang
- Mitohormesis Research Center, Department of Physiology, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Jong-Han Lee
- Department of Laboratory Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Moon Young Kim
- Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | | | | | - Jang-Young Kim
- Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Sang-Baek Koh
- Department of Preventive Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
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15
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Buonincontri G, Kurzawski JW, Kaggie JD, Matys T, Gallagher FA, Cencini M, Donatelli G, Cecchi P, Cosottini M, Martini N, Frijia F, Montanaro D, Gómez PA, Schulte RF, Retico A, Tosetti M. Three dimensional MRF obtains highly repeatable and reproducible multi-parametric estimations in the healthy human brain at 1.5T and 3T. Neuroimage 2021; 226:117573. [PMID: 33221451 DOI: 10.1016/j.neuroimage.2020.117573] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 12/19/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is highly promising as a quantitative MRI technique due to its accuracy, robustness, and efficiency. Previous studies have found high repeatability and reproducibility of 2D MRF acquisitions in the brain. Here, we have extended our investigations to 3D MRF acquisitions covering the whole brain using spiral projection k-space trajectories. Our travelling head study acquired test/retest data from the brains of 12 healthy volunteers and 8 MRI systems (3 systems at 3 T and 5 at 1.5 T, all from a single vendor), using a study design not requiring all subjects to be scanned at all sites. The pulse sequence and reconstruction algorithm were the same for all acquisitions. After registration of the MRF-derived PD T1 and T2 maps to an anatomical atlas, coefficients of variation (CVs) were computed to assess test/retest repeatability and inter-site reproducibility in each voxel, while a General Linear Model (GLM) was used to determine the voxel-wise variability between all confounders, which included test/retest, subject, field strength and site. Our analysis demonstrated a high repeatability (CVs 0.7-1.3% for T1, 2.0-7.8% for T2, 1.4-2.5% for normalized PD) and reproducibility (CVs of 2.0-5.8% for T1, 7.4-10.2% for T2, 5.2-9.2% for normalized PD) in gray and white matter. Both repeatability and reproducibility improved when compared to similar experiments using 2D acquisitions. Three-dimensional MRF obtains highly repeatable and reproducible estimations of T1 and T2, supporting the translation of MRF-based fast quantitative imaging into clinical applications.
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Affiliation(s)
| | - Jan W Kurzawski
- IRCCS Stella Maris, Pisa, Italy; National Institute for Nuclear Physics (INFN), Pisa, Italy
| | - Joshua D Kaggie
- Department of Radiology, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Tomasz Matys
- Department of Radiology, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Ferdia A Gallagher
- Department of Radiology, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Matteo Cencini
- IRCCS Stella Maris, Pisa, Italy; Imago7 Foundation, Pisa, Italy
| | - Graziella Donatelli
- Imago7 Foundation, Pisa, Italy; U.O. Neuroradiologia, Azienda Ospedaliera Universitaria Pisana (AOUP), Pisa, Italy
| | - Paolo Cecchi
- U.O. Neuroradiologia, Azienda Ospedaliera Universitaria Pisana (AOUP), Pisa, Italy
| | - Mirco Cosottini
- Imago7 Foundation, Pisa, Italy; U.O. Neuroradiologia, Azienda Ospedaliera Universitaria Pisana (AOUP), Pisa, Italy; Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Nicola Martini
- U.O.C. Bioingegneria e Ing. Clinica, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Francesca Frijia
- U.O.C. Bioingegneria e Ing. Clinica, Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Domenico Montanaro
- U.O.C. Risonanza Magnetica Specialistica e Neuroradiologia, Fondazione CNR/Regione Toscana G. Monasterio, Pisa-Massa, Italy
| | - Pedro A Gómez
- Imago7 Foundation, Pisa, Italy; Technical University of Munich, Munich, Germany
| | | | | | - Michela Tosetti
- IRCCS Stella Maris, Pisa, Italy; Imago7 Foundation, Pisa, Italy.
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16
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Fujita S, Buonincontri G, Cencini M, Fukunaga I, Takei N, Schulte RF, Hagiwara A, Uchida W, Hori M, Kamagata K, Abe O, Aoki S. Repeatability and reproducibility of human brain morphometry using three-dimensional magnetic resonance fingerprinting. Hum Brain Mapp 2020; 42:275-285. [PMID: 33089962 PMCID: PMC7775993 DOI: 10.1002/hbm.25232] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/13/2020] [Accepted: 09/29/2020] [Indexed: 12/12/2022] Open
Abstract
Three-dimensional (3D) Magnetic resonance fingerprinting (MRF) permits whole-brain volumetric quantification of T1 and T2 relaxation values, potentially replacing conventional T1-weighted structural imaging for common brain imaging analysis. The aim of this study was to evaluate the repeatability and reproducibility of 3D MRF in evaluating brain cortical thickness and subcortical volumetric analysis in healthy volunteers using conventional 3D T1-weighted images as a reference standard. Scan-rescan tests of both 3D MRF and conventional 3D fast spoiled gradient recalled echo (FSPGR) were performed. For each sequence, the regional cortical thickness and volume of the subcortical structures were measured using standard automatic brain segmentation software. Repeatability and reproducibility were assessed using the within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), and mean percent difference and ICC, respectively. The wCV and ICC of cortical thickness were similar across all regions with both 3D MRF and FSPGR. The percent relative difference in cortical thickness between 3D MRF and FSPGR across all regions was 8.0 ± 3.2%. The wCV and ICC of the volume of subcortical structures across all structures were similar between 3D MRF and FSPGR. The percent relative difference in the volume of subcortical structures between 3D MRF and FSPGR across all structures was 7.1 ± 3.6%. 3D MRF measurements of human brain cortical thickness and subcortical volumes are highly repeatable, and consistent with measurements taken on conventional 3D T1-weighted images. A slight, consistent bias was evident between the two, and thus careful attention is required when combining data from MRF and conventional acquisitions.
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Affiliation(s)
- Shohei Fujita
- Department of Radiology, Juntendo University, Tokyo, Japan.,Department of Radiology, The University of Tokyo, Tokyo, Japan
| | | | - Matteo Cencini
- Imago7 Foundation, Pisa, Italy.,IRCCS Stella Maris, Pisa, Italy
| | - Issei Fukunaga
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Naoyuki Takei
- MR Applications and Workflow, GE Healthcare, Tokyo, Japan
| | | | | | - Wataru Uchida
- Department of Radiology, Juntendo University, Tokyo, Japan.,Department of Radiological Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, Tokyo, Japan
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17
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Cervelli R, Cencini M, Buonincontri G, Campana F, Cacciato Insilla A, Aringhieri G, De Simone P, Boggi U, Campani D, Tosetti M, Crocetti L. 7-T MRI of explanted liver and ex-vivo pancreatic specimens: prospective study protocol of radiological-pathological correlation feasibility (the EXLIPSE project). Eur Radiol Exp 2020; 4:58. [PMID: 33057851 PMCID: PMC7560686 DOI: 10.1186/s41747-020-00185-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 09/03/2020] [Indexed: 12/11/2022] Open
Abstract
The study focuses on radiological-pathological correlation between imaging of ex vivo samples obtained by a 7-T scanner and histological examination. The specimens will be derived from native explanted cirrhotic livers, liver grafts excluded from donation because of severe steatosis, and primary pancreatic tumours. Magnetic resonance imaging (MRI) examinations will be performed within 24 h from liver or pancreatic lesion surgical removal. The MRI protocol will include morphological sequences, quantitative T1, T2, and fat-, water-fraction maps with Cartesian k-space acquisition, and multiparametric methods based on a transient-state “MRI fingerprinting”. Finally, the specimen will be fixed by formalin. Qualitative imaging analysis will be performed by two independent blinded radiologists to assess image consistency score. Quantitative analysis will be performed by drawing regions of interest on different tissue zones to measure T1 and T2 relaxation times as well as fat- and water-fraction. The same tissue areas will be analysed by the pathologists. This study will provide the possibility to improve our knowledge about qualitative and quantitative abdominal imaging assessment at 7 T, by correlating imaging characteristics and the corresponding histological composition of ex vivo specimens, in order to identify imaging biomarkers. Trial registration: ClinicalTrials.gov: 13646. Registered 9 July 2019—retrospectively registered
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Affiliation(s)
- Rosa Cervelli
- Division of Diagnostic and Interventional Radiology, University of Pisa, Via Paradisa, 2 - Cisanello Hospital, 56100, Pisa, Italy
| | | | | | - Francesco Campana
- Division of Diagnostic and Interventional Radiology, University of Pisa, Via Paradisa, 2 - Cisanello Hospital, 56100, Pisa, Italy
| | | | - Giacomo Aringhieri
- Division of Diagnostic and Interventional Radiology, University of Pisa, Via Paradisa, 2 - Cisanello Hospital, 56100, Pisa, Italy
| | - Paolo De Simone
- Division of Hepatic Surgery and Liver Transplant, University of Pisa, Pisa, Italy
| | - Ugo Boggi
- Division of General and Transplant and Surgery, University of Pisa, Pisa, Italy
| | | | | | - Laura Crocetti
- Division of Diagnostic and Interventional Radiology, University of Pisa, Via Paradisa, 2 - Cisanello Hospital, 56100, Pisa, Italy.
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18
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Marty B, Lopez Kolkovsky AL, Araujo ECA, Reyngoudt H. Quantitative Skeletal Muscle Imaging Using 3D MR Fingerprinting With Water and Fat Separation. J Magn Reson Imaging 2020; 53:1529-1538. [PMID: 32996670 DOI: 10.1002/jmri.27381] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 09/14/2020] [Accepted: 09/15/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Quantitative muscle MRI is a robust tool to monitor intramuscular fatty replacement and disease activity in patients with neuromuscular disorders (NMDs). PURPOSE To implement a 3D sequence for quantifying simultaneously fat fraction (FF) and water T1 (T1,H2O ) in the skeletal muscle, evaluate regular undersampling in the partition-encoding direction, and compare it to a recently proposed 2D MR fingerprinting sequence with water and fat separation (MRF T1 -FF). STUDY TYPE Prospective. PHANTOM/SUBJECTS Seventeen-vial phantom at different FF and T1,H2O , 11 healthy volunteers, and 6 subjects with different NMDs. FIELD STRENGTH/SEQUENCE 3T/3D MRF T1 -FF, 2D MRF T1 -FF, STEAM MRS ASSESSMENT: FF and T1,H2O measured with the 2D and 3D sequences were compared in the phantom and in vivo at different undersampling factors (US). Data were acquired in healthy subjects before and after plantar dorsiflexions and at rest in patients. STATISTICAL TESTS Linear correlations, Bland-Altman analysis, two-way repeated measures analysis of variance (ANOVA), Student's t-test. RESULTS Up to a US factor of 3, the undersampled acquisitions were in good agreement with the fully sampled sequence (R2 ≥ 0.98, T1,H2O bias ≤10 msec, FF bias ≤4 × 10-4 ) both in phantom and in vivo. The 2D and 3D MRF T1 -FF sequences provided comparable T1,H2O and FF values (R2 ≥ 0.95, absolute T1,H2O bias ≤35 msec, and absolute FF bias ≤0.003). The plantar dorsiflexion induced a significant increase of T1,H2O in the tibialis anterior and extensor digitorum (relative increase of +10.8 ± 1.7% and + 7.7 ± 1.4%, respectively, P < 0.05), that was accompanied by a significant reduction of FF in the tibialis anterior (relative decrease of -16.3 ± 4.0%, P < 0.05). Some subjects with NMDs presented increased and heterogeneous T1,H2O and FF values throughout the leg. DATA CONCLUSION Quantitative 3D T1,H2O and FF maps covering the entire leg were obtained within acquisition times compatible with clinical research (4 minutes 20 seconds) and a 1 × 1 × 5 mm3 spatial resolution. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Benjamin Marty
- Neuromuscular Investigation Center, NMR Laboratory, Institute of Myology, Paris, France.,CEA, DRF, IBFJ, MIRCen, NMR Laboratory, Paris, France
| | - Alfredo L Lopez Kolkovsky
- Neuromuscular Investigation Center, NMR Laboratory, Institute of Myology, Paris, France.,CEA, DRF, IBFJ, MIRCen, NMR Laboratory, Paris, France
| | - Ericky C A Araujo
- Neuromuscular Investigation Center, NMR Laboratory, Institute of Myology, Paris, France.,CEA, DRF, IBFJ, MIRCen, NMR Laboratory, Paris, France
| | - Harmen Reyngoudt
- Neuromuscular Investigation Center, NMR Laboratory, Institute of Myology, Paris, France.,CEA, DRF, IBFJ, MIRCen, NMR Laboratory, Paris, France
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19
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Liu Y, Hamilton J, Eck B, Griswold M, Seiberlich N. Myocardial T 1 and T 2 quantification and water-fat separation using cardiac MR fingerprinting with rosette trajectories at 3T and 1.5T. Magn Reson Med 2020; 85:103-119. [PMID: 32720408 PMCID: PMC10212526 DOI: 10.1002/mrm.28404] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 05/14/2020] [Accepted: 06/08/2020] [Indexed: 12/30/2022]
Abstract
PURPOSE This work aims to develop an approach for simultaneous water-fat separation and myocardial T1 and T2 quantification based on the cardiac MR fingerprinting (cMRF) framework with rosette trajectories at 3T and 1.5T. METHODS Two 15-heartbeat cMRF sequences with different rosette trajectories designed for water-fat separation at 3T and 1.5T were implemented. Water T1 and T2 maps, water image, and fat image were generated with B0 inhomogeneity correction using a B0 map derived from the cMRF data themselves. The proposed water-fat separation rosette cMRF approach was validated in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI system phantom and water/oil phantoms. It was also applied for myocardial tissue mapping of healthy subjects at both 3T and 1.5T. RESULTS Water T1 and T2 values measured using rosette cMRF in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom agreed well with the reference values. In the water/oil phantom, oil was well suppressed in the water images and vice versa. Rosette cMRF yielded comparable T1 but 2~3 ms higher T2 values in the myocardium of healthy subjects than the original spiral cMRF method. Epicardial fat deposition was also clearly shown in the fat images. CONCLUSION Rosette cMRF provides fat images along with myocardial T1 and T2 maps with significant fat suppression. This technique may improve visualization of the anatomical structure of the heart by separating water and fat and could provide value in diagnosing cardiac diseases associated with fibrofatty infiltration or epicardial fat accumulation. It also paves the way toward comprehensive myocardial tissue characterization in a single scan.
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Affiliation(s)
- Yuchi Liu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Jesse Hamilton
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Brendan Eck
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic Lerner Research Institute, Cleveland, OH, USA
| | - Mark Griswold
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Department of Radiology, University of Michigan, Ann Arbor, MI, USA.,Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
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20
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Jaubert O, Arrieta C, Cruz G, Bustin A, Schneider T, Georgiopoulos G, Masci P, Sing‐Long C, Botnar RM, Prieto C. Multi‐parametric liver tissue characterization using MR fingerprinting: Simultaneous T
1
, T
2
, T
2
*, and fat fraction mapping. Magn Reson Med 2020; 84:2625-2635. [DOI: 10.1002/mrm.28311] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 03/23/2020] [Accepted: 04/16/2020] [Indexed: 12/22/2022]
Affiliation(s)
- Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
| | - Cristobal Arrieta
- Biomedical Imaging Center and Millennium Nucleus for Cardiovascular Magnetic Resonance Pontificia Universidad Católica de Chile Santiago Chile
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
| | - Aurélien Bustin
- School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
| | | | - Georgios Georgiopoulos
- School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
| | - Pier‐Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom
| | - Carlos Sing‐Long
- Biomedical Imaging Center and Millennium Nucleus for Cardiovascular Magnetic Resonance Pontificia Universidad Católica de Chile Santiago Chile
- Instituto de Ingeniería Matemática y Computacional and Millennium Nucleus for the Discovery of Structures in Complex Data Pontificia Universidad Católica de Chile Santiago Chile
| | - Rene M. 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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21
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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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22
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McGivney DF, Boyacioğlu R, Jiang Y, Poorman ME, Seiberlich N, Gulani V, Keenan KE, Griswold MA, Ma D. Magnetic resonance fingerprinting review part 2: Technique and directions. J Magn Reson Imaging 2020; 51:993-1007. [PMID: 31347226 PMCID: PMC6980890 DOI: 10.1002/jmri.26877] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/05/2019] [Accepted: 07/05/2019] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR-sensitive tissue properties with a single acquisition. There have been numerous advances in MRF in the years since its inception. In this work we highlight some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:993-1007.
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Affiliation(s)
- Debra F. McGivney
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Rasim Boyacioğlu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yun Jiang
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Megan E. Poorman
- Department of Physics, University of Colorado Boulder, Boulder, Colorado, USA
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
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23
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Koolstra K, Webb AG, Veeger TTJ, Kan HE, Koken P, Börnert P. Water-fat separation in spiral magnetic resonance fingerprinting for high temporal resolution tissue relaxation time quantification in muscle. Magn Reson Med 2020; 84:646-662. [PMID: 31898834 PMCID: PMC7217066 DOI: 10.1002/mrm.28143] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 11/27/2019] [Accepted: 12/02/2019] [Indexed: 12/16/2022]
Abstract
Purpose To minimize the known biases introduced by fat in rapid T1 and T2 quantification in muscle using a single‐run magnetic resonance fingerprinting (MRF) water–fat separation sequence. Methods The single‐run MRF acquisition uses an alternating in‐phase/out‐of‐phase TE pattern to achieve water–fat separation based on a 2‐point DIXON method. Conjugate phase reconstruction and fat deblurring were applied to correct for B0 inhomogeneities and chemical shift blurring. Water and fat signals were matched to the on‐resonance MRF dictionary. The method was first tested in a multicompartment phantom. To test whether the approach is capable of measuring small in vivo dynamic changes in relaxation times, experiments were run in 9 healthy volunteers; parameter values were compared with and without water–fat separation during muscle recovery after plantar flexion exercise. Results Phantom results show the robustness of the water–fat resolving MRF approach to undersampling. Parameter maps in volunteers show a significant (P < .01) increase in T1 (105 ± 94 ms) and decrease in T2 (14 ± 6 ms) when using water–fat‐separated MRF, suggesting improved parameter quantification by reducing the well‐known biases introduced by fat. Exercise results showed smooth T1 and T2 recovery curves. Conclusion Water–fat separation using conjugate phase reconstruction is possible within a single‐run MRF scan. This technique can be used to rapidly map relaxation times in studies requiring dynamic scanning, in which the presence of fat is problematic.
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Affiliation(s)
- Kirsten Koolstra
- C.J. Gorter Center for High Field MRI, Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Andrew G Webb
- C.J. Gorter Center for High Field MRI, Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Thom T J Veeger
- C.J. Gorter Center for High Field MRI, Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Hermien E Kan
- C.J. Gorter Center for High Field MRI, Radiology, Leiden University Medical Center, Leiden, Netherlands
| | | | - Peter Börnert
- C.J. Gorter Center for High Field MRI, Radiology, Leiden University Medical Center, Leiden, Netherlands.,Philips Research, Hamburg, Germany
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24
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Hamilton JI, Seiberlich N. Machine Learning for Rapid Magnetic Resonance Fingerprinting Tissue Property Quantification. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:69-85. [PMID: 33132408 PMCID: PMC7595247 DOI: 10.1109/jproc.2019.2936998] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Magnetic Resonance Fingerprinting (MRF) is an MRI-based method that can provide quantitative maps of multiple tissue properties simultaneously from a single rapid acquisition. Tissue property maps are generated by matching the complex signal evolutions collected at the scanner to a dictionary of signals derived using Bloch equation simulations. However, in some circumstances, the process of dictionary generation and signal matching can be time-consuming, reducing the utility of this technique. Recently, several groups have proposed using machine learning to accelerate the extraction of quantitative maps from MRF data. This article will provide an overview of current research that combines MRF and machine learning, as well as present original research demonstrating how machine learning can speed up dictionary generation for cardiac MRF.
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Affiliation(s)
- Jesse I Hamilton
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106 USA, and the Department of Radiology, University of Michigan, Ann Arbor, MI 48109
| | - Nicole Seiberlich
- Department of Biomedical Engineering and the Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106 USA, the Department of Radiology and Cardiology, University Hospitals, Cleveland, OH 44106 USA, and the Department of Radiology, University of Michigan, Ann Arbor, MI 48109
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25
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Hu HH, Branca RT, Hernando D, Karampinos DC, Machann J, McKenzie CA, Wu HH, Yokoo T, Velan SS. Magnetic resonance imaging of obesity and metabolic disorders: Summary from the 2019 ISMRM Workshop. Magn Reson Med 2019; 83:1565-1576. [PMID: 31782551 DOI: 10.1002/mrm.28103] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 11/04/2019] [Accepted: 11/11/2019] [Indexed: 02/06/2023]
Abstract
More than 100 attendees from Australia, Austria, Belgium, Canada, China, Germany, Hong Kong, Indonesia, Japan, Malaysia, the Netherlands, the Philippines, Republic of Korea, Singapore, Sweden, Switzerland, the United Kingdom, and the United States convened in Singapore for the 2019 ISMRM-sponsored workshop on MRI of Obesity and Metabolic Disorders. The scientific program brought together a multidisciplinary group of researchers, trainees, and clinicians and included sessions in diabetes and insulin resistance; an update on recent advances in water-fat MRI acquisition and reconstruction methods; with applications in skeletal muscle, bone marrow, and adipose tissue quantification; a summary of recent findings in brown adipose tissue; new developments in imaging fat in the fetus, placenta, and neonates; the utility of liver elastography in obesity studies; and the emerging role of radiomics in population-based "big data" studies. The workshop featured keynote presentations on nutrition, epidemiology, genetics, and exercise physiology. Forty-four proffered scientific abstracts were also presented, covering the topics of brown adipose tissue, quantitative liver analysis from multiparametric data, disease prevalence and population health, technical and methodological developments in data acquisition and reconstruction, newfound applications of machine learning and neural networks, standardization of proton density fat fraction measurements, and X-nuclei applications. The purpose of this article is to summarize the scientific highlights from the workshop and identify future directions of work.
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Affiliation(s)
- Houchun H Hu
- Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio
| | - Rosa Tamara Branca
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Diego Hernando
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin.,Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Jürgen Machann
- Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany.,German Center for Diabetes Research, Tübingen, Germany.,Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Charles A McKenzie
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
| | - Holden H Wu
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California
| | - Takeshi Yokoo
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - S Sendhil Velan
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore.,Singapore BioImaging Consortium, Agency for Science Technology and Research, Singapore
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26
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Jaubert O, Cruz G, Bustin A, Schneider T, Lavin B, Koken P, Hajhosseiny R, Doneva M, Rueckert D, Botnar RM, Prieto C. Water-fat Dixon cardiac magnetic resonance fingerprinting. Magn Reson Med 2019; 83:2107-2123. [PMID: 31736146 PMCID: PMC7064906 DOI: 10.1002/mrm.28070] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 10/15/2019] [Accepted: 10/17/2019] [Indexed: 12/12/2022]
Abstract
Purpose Cardiac magnetic resonance fingerprinting (cMRF) has been recently introduced to simultaneously provide T1, T2, and M0 maps. Here, we develop a 3‐point Dixon‐cMRF approach to enable simultaneous water specific T1, T2, and M0 mapping of the heart and fat fraction (FF) estimation in a single breath‐hold scan. Methods Dixon‐cMRF is achieved by combining cMRF with several innovations that were previously introduced for other applications, including a 3‐echo GRE acquisition with golden angle radial readout and a high‐dimensional low‐rank tensor constrained reconstruction to recover the highly undersampled time series images for each echo. Water–fat separation of the Dixon‐cMRF time series is performed to allow for water‐ and fat‐specific T1, T2, and M0 estimation, whereas FF estimation is extracted from the M0 maps. Dixon‐cMRF was evaluated in a standardized T1–T2 phantom, in a water–fat phantom, and in healthy subjects in comparison to current clinical standards: MOLLI, SASHA, T2‐GRASE, and 6‐point Dixon proton density FF (PDFF) mapping. Results Dixon‐cMRF water T1 and T2 maps showed good agreement with reference T1 and T2 mapping techniques (R2 > 0.99 and maximum normalized RMSE ~5%) in a standardized phantom. Good agreement was also observed between Dixon‐cMRF FF and reference PDFF (R2 > 0.99) and between Dixon‐cMRF water T1 and T2 and water selective T1 and T2 maps (R2 > 0.99) in a water–fat phantom. In vivo Dixon‐cMRF water T1 values were in good agreement with MOLLI and water T2 values were slightly underestimated when compared to T2‐GRASE. Average myocardium septal T1 values were 1129 ± 38 ms, 1026 ± 28 ms, and 1045 ± 32 ms for SASHA, MOLLI, and the proposed water Dixon‐cMRF. Average T2 values were 51.7 ± 2.2 ms and 42.8 ± 2.6 ms for T2‐GRASE and water Dixon‐cMRF, respectively. Dixon‐cMRF FF maps showed good agreement with in vivo PDFF measurements (R2 > 0.98) and average FF in the septum was measured at 1.3%. Conclusion The proposed Dixon‐cMRF allows to simultaneously quantify myocardial water T1, water T2, and FF in a single breath‐hold scan, enabling multi‐parametric T1, T2, and fat characterization. Moreover, reduced T1 and T2 quantification bias caused by water–fat partial volume was demonstrated in phantom experiments.
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Affiliation(s)
- Olivier Jaubert
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Aurélien Bustin
- 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
| | | | - Reza Hajhosseiny
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Daniel Rueckert
- Department of Computing, Imperial College London, London, United Kingdom
| | - René M 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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27
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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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28
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Lee PK, Watkins LE, Anderson TI, Buonincontri G, Hargreaves BA. Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations. Magn Reson Med 2019; 82:1438-1451. [PMID: 31131500 PMCID: PMC8057531 DOI: 10.1002/mrm.27832] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 05/06/2019] [Accepted: 05/08/2019] [Indexed: 12/21/2022]
Abstract
PURPOSE To investigate a computationally efficient method for optimizing the Cramér-Rao Lower Bound (CRLB) of quantitative sequences without using approximations or an analytical expression of the signal. METHODS Automatic differentiation was applied to Bloch simulations and used to optimize several quantitative sequences without the need for approximations or an analytical expression. The results were validated with in vivo measurements and comparisons to prior art. Multi-echo spin echo and DESPO T 1 were used as benchmarks to verify the CRLB implementation. The CRLB of the Magnetic Resonance Fingerprinting (MRF) sequence, which has a complicated analytical formulation, was also optimized using automatic differentiation. RESULTS The sequence parameters obtained for multi-echo spin echo and DESPO T 1 matched results obtained using conventional methods. In vivo, MRF scans demonstrate that the CRLB optimization obtained with automatic differentiation can improve performance in presence of white noise. For MRF, the CRLB optimization converges in 1.1 CPU hours for N TR = 400 and has O ( N TR ) asymptotic runtime scaling for the calculation of the CRLB objective and gradient. CONCLUSIONS Automatic differentiation can be used to optimize the CRLB of quantitative sequences without using approximations or analytical expressions. For MRF, the runtime is computationally efficient and can be used to investigate confounding factors as well as MRF sequences with a greater number of repetitions.
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Affiliation(s)
- Philip K. Lee
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Lauren E. Watkins
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | | | - Guido Buonincontri
- IRCCS Fondazione Stella Maris, Pisa, PI, 56128, Italy
- Fondazione Imago7, Pisa, PI, 56128, Italy
| | - Brian A. Hargreaves
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
- Bioengineering, Stanford University, Stanford, CA, 94305, USA
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29
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Marty B, Carlier PG. MR fingerprinting for water T1 and fat fraction quantification in fat infiltrated skeletal muscles. Magn Reson Med 2019; 83:621-634. [PMID: 31502715 DOI: 10.1002/mrm.27960] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 07/11/2019] [Accepted: 07/31/2019] [Indexed: 12/28/2022]
Abstract
PURPOSE To develop a fast MR fingerprinting (MRF) sequence for simultaneous estimation of water T1 (T1H2O ) and fat fraction (FF) in fat infiltrated skeletal muscles. METHODS The MRF sequence for T1H2O and FF quantification (MRF T1-FF) comprises a 1400 radial spokes echo train, following nonselective inversion, with varying echo and repetition time, as well as prescribed flip angle. Undersampled frames were reconstructed at different acquisition time-points by nonuniform Fourier transform, and a bi-component model based on Bloch simulations applied to adjust the signal evolution and extract T1H2O and FF. The sequence was validated on a multi-vial phantom, in three healthy volunteers and five patients with neuromuscular diseases. We evaluated the agreement between MRF T1-FF parameters and reference values and confounding effects due to B0 and B1 inhomogeneities. RESULTS In phantom, T1H2O and FF were highly correlated with references values measured with multi-inversion time inversion recovery-stimulated echo acquisition mode and Dixon, respectively (R2 > 0.99). In vivo, T1H2O and FF determined by the MRF T1-FF sequence were also correlated with reference values (R2 = 0.98 and 0.97, respectively). The precision on T1H2O was better than 5% for muscles where FF was less than 0.4. Both T1H2O and FF values were not confounded by B0 nor B1 inhomogeneities. CONCLUSION The MRF T1-FF sequence derived T1H2O and FF values in voxels containing a mixture of water and fat protons. This method can be used to comprehend and characterize the effects of tissue water compartmentation and distribution on muscle T1 values in patients affected by chronic fat infiltration.
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Affiliation(s)
- Benjamin Marty
- NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France.,NMR Laboratory, CEA, DRF, IBFJ, MIRCen, Paris, France
| | - Pierre G Carlier
- NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France.,NMR Laboratory, CEA, DRF, IBFJ, MIRCen, Paris, France
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30
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Cencini M, Tosetti M, Buonincontri G. An Aristotelian View on MR-Based Attenuation Correction (ARISTOMRAC): Combining the Four Elements. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019. [DOI: 10.1109/trpms.2019.2903593] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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31
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Marty B, Carlier PG. Physiological and pathological skeletal muscle T1 changes quantified using a fast inversion-recovery radial NMR imaging sequence. Sci Rep 2019; 9:6852. [PMID: 31048765 PMCID: PMC6497638 DOI: 10.1038/s41598-019-43398-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 04/24/2019] [Indexed: 12/18/2022] Open
Abstract
We investigated the response of skeletal muscle global T1 under different physiological and pathological conditions using an inversion-recovery radial T1 mapping sequence. Thirty five healthy volunteers, seven patients with Becker muscular dystrophy (BMD) and seven patients with sporadic inclusion body myositis (IBM) were investigated in order to evaluate the effects of gender, age, muscle group, exercise and pathological processes on global T1 values. In addition, the intramuscular fat content was measured using 3-point Dixon and the global T2 and water T2 (T2H2O) were determined with a multi-spin-echo sequence. In the muscles of healthy volunteers, there was no impact of age on global T1. However, we measured a significant effect of sex and muscle group. After exercise, a significant 7.7% increase of global T1 was measured in the recruited muscles, and global T1 variations were highly correlated to T2H2O variations (R = 0.91). In pathologies, global T1 values were reduced in fat infiltrated muscles. When fat fraction was taken into account, global T1 values were higher in IBM patients compared to BMD. Global T1 variations are a sensitive indicator of tissue changes in skeletal muscle related to several physiological and pathological events.
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Affiliation(s)
- Benjamin Marty
- Institute of Myology, Neuromuscular Investigation Center, NMR Laboratory, Paris, France. .,CEA, DRF, IBFJ, MIRCen, NMR Laboratory, Paris, France.
| | - Pierre G Carlier
- Institute of Myology, Neuromuscular Investigation Center, NMR Laboratory, Paris, France.,CEA, DRF, IBFJ, MIRCen, NMR Laboratory, Paris, France
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32
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MR fingerprinting with simultaneous T 1, T 2, and fat signal fraction estimation with integrated B 0 correction reduces bias in water T 1 and T 2 estimates. Magn Reson Imaging 2019; 60:7-19. [PMID: 30910696 DOI: 10.1016/j.mri.2019.03.017] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 03/15/2019] [Accepted: 03/19/2019] [Indexed: 12/26/2022]
Abstract
PURPOSE MR fingerprinting (MRF) sequences permit efficient T1 and T2 estimation in cranial and extracranial regions, but these areas may include substantial fat signals that bias T1 and T2 estimates. MRI fat signal fraction estimation is also a topic of active research in itself, but may be complicated by B0 heterogeneity and blurring during spiral k-space acquisitions, which are commonly used for MRF. An MRF method is proposed that separates fat and water signals, estimates water T1 and T2, and accounts for B0 effects with spiral blurring correction, in a single sequence. THEORY AND METHODS A k-space-based fat-water separation method is further extended to unbalanced steady-state free precession MRF with swept echo time. Repeated application of this k-space fat-water separation to demodulated forms of the measured data allows a B0 map and correction to be approximated. The method is compared with MRF without fat separation across a broad range of fat signal fractions (FSFs), water T1s and T2s, and under heterogeneous static fields in simulations, phantoms, and in vivo. RESULTS The proposed method's FSF estimates had a concordance correlation coefficient of 0.990 with conventional measurements, and reduced biases in the T1 and T2 estimates due to fat signal relative to other MRF sequences by several hundred ms. The B0 correction improved the FSF, T1, and T2 estimation compared to those estimates without correction. CONCLUSION The proposed method improves MRF water T1 and T2 estimation in the presence of fat and provides accurate FSF estimation with inline B0 correction.
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33
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Cencini M, Biagi L, Kaggie JD, Schulte RF, Tosetti M, Buonincontri G. Magnetic resonance fingerprinting with dictionary-based fat and water separation (DBFW MRF): A multi-component approach. Magn Reson Med 2018; 81:3032-3045. [PMID: 30578569 PMCID: PMC6590362 DOI: 10.1002/mrm.27628] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 10/04/2018] [Accepted: 11/14/2018] [Indexed: 12/20/2022]
Abstract
Purpose To obtain a fast and robust fat‐water separation with simultaneous estimation of water T1, fat T1, and fat fraction maps. Methods We modified an MR fingerprinting (MRF) framework to use a single dictionary combination of a water and fat dictionary. A variable TE acquisition pattern with maximum TE = 4.8 ms was used to increase the fat–water separability. Radiofrequency (RF) spoiling was used to reduce the size of the dictionary by reducing T2 sensitivity. The technique was compared both in vitro and in vivo to an MRF method that incorporated 3‐point Dixon (DIXON MRF), as well as Cartesian IDEAL with different acquisition parameters. Results The proposed dictionary‐based fat–water separation technique (DBFW MRF) successfully provided fat fraction, water, and fat T1, B0, and B1+ maps both in vitro and in vivo. The fat fraction and water T1 values obtained with DBFW MRF show excellent agreement with DIXON MRF as well as with the reference values obtained using a Cartesian IDEAL with a long TR (concordance correlation coefficient: 0.97/0.99 for fat fraction–water T1). Whereas fat fraction values with Cartesian IDEAL were degraded in the presence of T1 saturation, MRF methods successfully estimated and accounted for T1 in the fat fraction estimates. Conclusion The DBFW MRF technique can successfully provide T1 and fat fraction quantification in under 20 s per slice, intrinsically correcting T1 biases typical of fast Dixon techniques. These features could improve the diagnostic quality and use of images in presence of fat.
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Affiliation(s)
- Matteo Cencini
- Department of Physics, University of Pisa, Pisa, Italy.,IMAGO7 Foundation, Pisa, Italy
| | - Laura Biagi
- IMAGO7 Foundation, Pisa, Italy.,IRCCS Stella Maris Scientific Institute, Pisa, Italy
| | - Joshua D Kaggie
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | | | - Michela Tosetti
- IMAGO7 Foundation, Pisa, Italy.,IRCCS Stella Maris Scientific Institute, Pisa, Italy
| | - Guido Buonincontri
- IMAGO7 Foundation, Pisa, Italy.,Istituto Nazionale di Fisica Nucleare (INFN), Pisa, Italy
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