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Mao A, Flassbeck S, Assländer J. Bias-reduced neural networks for parameter estimation in quantitative MRI. Magn Reson Med 2024; 92:1638-1648. [PMID: 38703042 DOI: 10.1002/mrm.30135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 04/09/2024] [Accepted: 04/11/2024] [Indexed: 05/06/2024]
Abstract
PURPOSE To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound. THEORY AND METHODS We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications. RESULTS In simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cramér-Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as nonlinear least-squares fitting, while state-of-the-art NNs show larger deviations. CONCLUSION The proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy.
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Affiliation(s)
- Andrew Mao
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA
| | - Sebastian Flassbeck
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Jakob Assländer
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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Zhang X, de Moura HL, Monga A, Zibetti MVW, Kijowski R, Regatte RR. Repeatability of Quantitative Knee Cartilage T 1, T 2, and T 1ρ Mapping With 3D-MRI Fingerprinting. J Magn Reson Imaging 2024; 60:688-699. [PMID: 37885320 PMCID: PMC11045656 DOI: 10.1002/jmri.29068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 10/03/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Three-dimensional MR fingerprinting (3D-MRF) techniques have been recently described for simultaneous multiparametric mapping of knee cartilage. However, investigation of repeatability remains limited. PURPOSE To assess the intra-day and inter-day repeatabilities of knee cartilage T1, T2, and T1ρ maps using a 3D-MRF sequence for simultaneous measurement. STUDY TYPE Prospective. SUBJECTS Fourteen healthy subjects (35.4 ± 9.3 years, eight males), scanned on Day 1 and Day 7. FIELD STRENGTH/SEQUENCE 3 T/3D-MRF, T1, T2, and T1ρ maps. ASSESSMENT The acquisition of 3D-MRF cartilage (simultaneous acquisition of T1, T2, and T1ρ maps) were acquired using a dictionary pattern-matching approach. Conventional cartilage T1, T2, and T1ρ maps were acquired using variable flip angles and a modified 3D-Turbo-Flash sequence with different echo and spin-lock times, respectively, and were fitted using mono-exponential models. Each sequence was acquired on Day 1 and Day 7 with two scans on each day. STATISTICAL TESTS The mean and SD for cartilage T1, T2, and T1ρ were calculated in five manually segmented regions of interest (ROIs), including lateral femur, lateral tibia, medial femur, medial tibia, and patella cartilages. Intra-subject and inter-subject repeatabilities were assessed using coefficient of variation (CV) and intra-class correlation coefficient (ICC), respectively, on the same day and among different days. Regression and Bland-Altman analysis were performed to compare maps between the conventional and 3D-MRF sequences. RESULTS The CV in all ROIs was lower than 7.4%, 8.4%, and 7.5% and the ICC was higher than 0.56, 0.51, and 0.52 for cartilage T1, T2, and T1ρ, respectively. The MRF results had a good agreement with the conventional methods with a linear regression slope >0.61 and R2 > 0.59. CONCLUSION The 3D-MRF sequence had high intra-subject and inter-subject repeatabilities for simultaneously measuring knee cartilage T1, T2, and T1ρ with good agreement with conventional sequences. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Xiaoxia Zhang
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Hector L. de Moura
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Anmol Monga
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Marcelo V. W. Zibetti
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Richard Kijowski
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Ravinder R. Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
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Sørland KI, Trimble CG, Wu CY, Bathen TF, Elschot M, Cloos MA. Reducing femoral flow artefacts in radial magnetic resonance fingerprinting of the prostate using region-optimised virtual coils. NMR IN BIOMEDICINE 2024; 37:e5136. [PMID: 38514929 DOI: 10.1002/nbm.5136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 01/19/2024] [Accepted: 02/07/2024] [Indexed: 03/23/2024]
Abstract
High acceleration factors in radial magnetic resonance fingerprinting (MRF) of the prostate lead to strong streak-like artefacts from flow in the femoral blood vessels, possibly concealing important anatomical information. Region-optimised virtual (ROVir) coils is a beamforming-based framework to create virtual coils that maximise signal in a region of interest while minimising signal in a region of interference. In this study, the potential of removing femoral flow streak artefacts in prostate MRF using ROVir coils is demonstrated in silico and in vivo. The ROVir framework was applied to radial MRF k-space data in an automated pipeline designed to maximise prostate signal while minimising signal from the femoral vessels. The method was tested in 15 asymptomatic volunteers at 3 T. The presence of streaks was visually assessed and measurements of whole prostate T1, T2 and signal-to-noise ratio (SNR) with and without streak correction were examined. In addition, a purpose-built simulation framework in which blood flow through the femoral vessels can be turned on and off was used to quantitatively evaluate ROVir's ability to suppress streaks in radial prostate MRF. In vivo it was shown that removing selected ROVir coils visibly reduces streak-like artefacts from the femoral blood flow, without increasing the reconstruction time. On average, 80% of the prostate SNR was retained. A similar reduction of streaks was also observed in silico, while the quantitative accuracy of T1 and T2 mapping was retained. In conclusion, ROVir coils efficiently suppress streaking artefacts from blood flow in radial MRF of the prostate, thereby improving the visual clarity of the images, without significant sacrifices to acquisition time, reconstruction time and accuracy of quantitative values. This is expected to help enable T1 and T2 mapping of prostate cancer in clinically viable times, aiding differentiation between prostate cancer from noncancer and healthy prostate tissue.
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Affiliation(s)
- Kaia I Sørland
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Christopher G Trimble
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hostpital, Trondheim, Norway
| | - Chia-Yin Wu
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland, Australia
- ARC Training Centre for Innovation on Biomedical Imaging Technology (CIBIT), The University of Queensland, St Lucia, Queensland, Australia
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, Queensland, Australia
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hostpital, Trondheim, Norway
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hostpital, Trondheim, Norway
| | - Martijn A Cloos
- Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland, Australia
- ARC Training Centre for Innovation on Biomedical Imaging Technology (CIBIT), The University of Queensland, St Lucia, Queensland, Australia
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Li P, Hu Y. Deep magnetic resonance fingerprinting based on Local and Global Vision Transformer. Med Image Anal 2024; 95:103198. [PMID: 38759259 DOI: 10.1016/j.media.2024.103198] [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: 06/30/2023] [Revised: 04/28/2024] [Accepted: 05/02/2024] [Indexed: 05/19/2024]
Abstract
To mitigate systematic errors in magnetic resonance fingerprinting (MRF), the precomputed dictionary is usually computed with minimal granularity across the entire range of tissue parameters. However, the dictionary grows exponentially with the number of parameters increase, posing significant challenges to the computational efficiency and matching accuracy of pattern-matching algorithms. Existing works, primarily based on convolutional neural networks (CNN), focus solely on local information to reconstruct multiple parameter maps, lacking in-depth investigations on the MRF mechanism. These methods may not exploit long-distance redundancies and the contextual information within voxel fingerprints introduced by the Bloch equation dynamics, leading to limited reconstruction speed and accuracy. To overcome these limitations, we propose a novel end-to-end neural network called the Local and Global Vision Transformer (LG-ViT) for MRF parameter reconstruction. Our proposed LG-ViT employs a multi-stage architecture that effectively reduces the computational overhead associated with the high-dimensional MRF data and the transformer model. Specifically, a local Transformer encoder is proposed to capture contextual information embedded within voxel fingerprints and local correlations introduced by the interconnected human tissues. Additionally, a global Transformer encoder is proposed to leverage long-distance dependencies arising from shared characteristics among different tissues across various spatial regions. By incorporating MRF physics-based data priors and effectively capturing local and global correlations, our proposed LG-ViT can achieve fast and accurate MRF parameter reconstruction. Experiments on both simulation and in vivo data demonstrate that the proposed method enables faster and more accurate MRF parameter reconstruction compared to state-of-the-art deep learning-based methods.
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Affiliation(s)
- Peng Li
- The School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China
| | - Yue Hu
- The School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China.
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Yablonski M, Zhou Z, Cao X, Schauman S, Liao C, Setsompop K, Yeatman JD. Fast and reliable quantitative measures of white matter development with magnetic resonance fingerprinting. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.26.600735. [PMID: 38979185 PMCID: PMC11230456 DOI: 10.1101/2024.06.26.600735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Developmental cognitive neuroscience aims to shed light on evolving relationships between brain structure and cognitive development. To this end, quantitative methods that reliably measure individual differences in brain tissue properties are fundamental. Standard qualitative MRI sequences are influenced by scan parameters and hardware-related biases, and also lack physical units, making the analysis of individual differences problematic. In contrast, quantitative MRI can measure physical properties of the tissue but with the cost of long scan durations and sensitivity to motion. This poses a critical limitation for studying young children. Here, we examine the reliability and validity of an efficient quantitative multiparameter mapping method - Magnetic Resonance Fingerprinting (MRF) - in children scanned longitudinally. We focus on T1 values in white matter, since quantitative T1 values are known to primarily reflect myelin content, a key factor in brain development. Forty-nine children aged 8-13y (mean 10.3y ±1.4) completed two scanning sessions 2-4 months apart. In each session, two 2-minute 3D-MRF scans at 1mm isotropic resolution were collected to evaluate the effect of scan duration on image quality and scan-rescan reliability. A separate calibration scan was used to measure B0 inhomogeneity and correct for bias. We examined the impact of scan time and B0 inhomogeneity correction on scan-rescan reliability of values in white matter, by comparing single 2-min and combined two 2-min scans, with and without B0-correction. Whole-brain voxel-based reliability analysis showed that combining two 2-min MRF scans improved reliability (pearson's r=0.87) compared with a single 2-min scan (r=0.84), while B0-correction had no effect on reliability in white matter (r=0.86 and 0.83 4-min vs 2-min). Using diffusion tractography, we delineated MRF-derived T1 profiles along major white matter fiber tracts and found similar or higher reliability for T1 from MRF compared to diffusion parameters (based on a 10-minute dMRI scan). Lastly, we found that T1 values in multiple white matter tracts were significantly correlated with age. In sum, MRF-derived T1 values were highly reliable in a longitudinal sample of children and replicated known age effects. Reliability in white matter was improved by longer scan duration but was not affected by B0-correction, making it a quick and straightforward scan to collect. We propose that MRF provides a promising avenue for acquiring quantitative brain metrics in children and patient populations where scan time and motion are of particular concern.
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Dupuis A, Chen Y, Hansen M, Chow K, Sun JE, Badve C, Ma D, Griswold MA, Boyacioglu R. Quantifying 3D MR fingerprinting (3D-MRF) reproducibility across subjects, sessions, and scanners automatically using MNI atlases. Magn Reson Med 2024; 91:2074-2088. [PMID: 38192239 PMCID: PMC10950529 DOI: 10.1002/mrm.29983] [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: 07/25/2023] [Revised: 12/01/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
PURPOSE Quantitative MRI techniques such as MR fingerprinting (MRF) promise more objective and comparable measurements of tissue properties at the point-of-care than weighted imaging. However, few direct cross-modal comparisons of MRF's repeatability and reproducibility versus weighted acquisitions have been performed. This work proposes a novel fully automated pipeline for quantitatively comparing cross-modal imaging performance in vivo via atlas-based sampling. METHODS We acquire whole-brain 3D-MRF, turbo spin echo, and MPRAGE sequences three times each on two scanners across 10 subjects, for a total of 60 multimodal datasets. The proposed automated registration and analysis pipeline uses linear and nonlinear registration to align all qualitative and quantitative DICOM stacks to Montreal Neurological Institute (MNI) 152 space, then samples each dataset's native space through transformation inversion to compare performance within atlas regions across subjects, scanners, and repetitions. RESULTS Voxel values within MRF-derived maps were found to be more repeatable (σT1 = 1.90, σT2 = 3.20) across sessions than vendor-reconstructed MPRAGE (σT1w = 6.04) or turbo spin echo (σT2w = 5.66) images. Additionally, MRF was found to be more reproducible across scanners (σT1 = 2.21, σT2 = 3.89) than either qualitative modality (σT1w = 7.84, σT2w = 7.76). Notably, differences between repeatability and reproducibility of in vivo MRF were insignificant, unlike the weighted images. CONCLUSION MRF data from many sessions and scanners can potentially be treated as a single dataset for harmonized analysis or longitudinal comparisons without the additional regularization steps needed for qualitative modalities.
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Affiliation(s)
- Andrew Dupuis
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yong Chen
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University Hospitals, Cleveland, Ohio, USA
| | | | - Kelvin Chow
- Siemens Medical Solutions USA, Inc, Chicago, Illinois, USA
| | - Jessie E.P. Sun
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Rasim Boyacioglu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
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Brackenier Y, Wang N, Liao C, Cao X, Schauman S, Yurt M, Cordero-Grande L, Malik SJ, Kerr A, Hajnal JV, Setsompop K. Rapid and accurate navigators for motion and B 0 tracking using QUEEN: Quantitatively enhanced parameter estimation from navigators. Magn Reson Med 2024; 91:2028-2043. [PMID: 38173304 DOI: 10.1002/mrm.29976] [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: 06/17/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE To develop a framework that jointly estimates rigid motion and polarizing magnetic field (B0 ) perturbations (δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ ) for brain MRI using a single navigator of a few milliseconds in duration, and to additionally allow for navigator acquisition at arbitrary timings within any type of sequence to obtain high-temporal resolution estimates. THEORY AND METHODS Methods exist that match navigator data to a low-resolution single-contrast image (scout) to estimate either motion orδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . In this work, called QUEEN (QUantitatively Enhanced parameter Estimation from Navigators), we propose combined motion andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ estimation from a fast, tailored trajectory with arbitrary-contrast navigator data. To this end, the concept of a quantitative scout (Q-Scout) acquisition is proposed from which contrast-matched scout data is predicted for each navigator. Finally, navigator trajectories, contrast-matched scout, andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ are integrated into a motion-informed parallel-imaging framework. RESULTS Simulations and in vivo experiments show the need to modelδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ to obtain accurate motion parameters estimated in the presence of strongδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . Simulations confirm that tailored navigator trajectories are needed to robustly estimate both motion andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . Furthermore, experiments show that a contrast-matched scout is needed for parameter estimation from multicontrast navigator data. A retrospective, in vivo reconstruction experiment shows improved image quality when using the proposed Q-Scout and QUEEN estimation. CONCLUSIONS We developed a framework to jointly estimate rigid motion parameters andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ from navigators. Combing a contrast-matched scout with the proposed trajectory allows for navigator deployment in almost any sequence and/or timing, which allows for higher temporal-resolution motion andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ estimates.
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Affiliation(s)
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Xiaozhi Cao
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Sophie Schauman
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Mahmut Yurt
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Lucilio Cordero-Grande
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BNN, Madrid, Spain
| | - Shaihan J Malik
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Adam Kerr
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Cognitive and Neurobiological Imaging, Stanford University, Stanford, California, USA
| | - Joseph V Hajnal
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Huang P, Eck B, Liu R, Li H, Yang M, Kim J, Zhang X, Li X, Ying L. Robust Highly-accelerated MR Fingerprinting Using Transformer-based Deep Learning. PROCEEDINGS OF THE INTERNATIONAL SOCIETY FOR MAGNETIC RESONANCE IN MEDICINE ... SCIENTIFIC MEETING AND EXHIBITION. INTERNATIONAL SOCIETY FOR MAGNETIC RESONANCE IN MEDICINE. SCIENTIFIC MEETING AND EXHIBITION 2024; 32:3577. [PMID: 38798756 PMCID: PMC11127716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Affiliation(s)
- Peizhou Huang
- Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
| | - Brendan Eck
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Ruiying Liu
- Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
| | - Hongyu Li
- Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
| | - Mingrui Yang
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Jeehun Kim
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Xiaoliang Zhang
- Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
| | - Xiaojuan Li
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Leslie Ying
- Biomedical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
- Electrical Engineering, State University of New York at Buffalo, Buffalo, NY, United States
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Hamilton JI, Lima da Cruz G, Rashid I, Walker J, Rajagopalan S, Seiberlich N. Deep image prior cine MR fingerprinting with B 1 + spin history correction. Magn Reson Med 2024; 91:2010-2027. [PMID: 38098428 PMCID: PMC10950517 DOI: 10.1002/mrm.29979] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 11/29/2023] [Accepted: 11/29/2023] [Indexed: 03/07/2024]
Abstract
PURPOSE To develop a deep image prior (DIP) reconstruction for B1 + -corrected 2D cine MR fingerprinting (MRF). METHODS The proposed method combines low-rank (LR) modeling with a DIP to generate cardiac phase-resolved parameter maps without motion correction, employing self-supervised training to enforce consistency with undersampled spiral k-space data. Two implementations were tested: one approach (DIP) for cine T1 , T2 , and M0 mapping, and a second approach (DIP with effective B1 + estimation [DIP-B1]) that also generated an effective B1 + map to correct for errors due to RF transmit inhomogeneities, through-plane motion, and blood flow. Cine MRF data were acquired in 14 healthy subjects and four reconstructions were compared: LR, low-rank motion-corrected (LRMC), DIP, and DIP-B1. Results were compared to diastolic ECG-triggered MRF, MOLLI, and T2 -prep bSSFP. Additionally, bright-blood and dark-blood images calculated from cine MRF maps were used to quantify ventricular function and compared to reference cine measurements. RESULTS DIP and DIP-B1 outperformed other cine MRF reconstructions with improved noise suppression and delineation of high-resolution details. Within-segment variability in the myocardium (reported as the coefficient of variation for T1 /T2 ) was lowest for DIP-B1 (2.3/8.3%) followed by DIP (2.7/8.7%), LRMC (3.5/10.5%), and LR (15.3/39.6%). Spatial homogeneity improved with DIP-B1 having the lowest intersegment variability (2.6/4.1%). The mean bias in ejection fraction was -1.1% compared to reference cine scans. CONCLUSION A DIP reconstruction for 2D cine MRF enabled cardiac phase-resolved mapping of T1 , T2 , M0 , and the effective B1 + with improved noise suppression and precision compared to LR and LRMC.
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Affiliation(s)
- Jesse I. Hamilton
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | | | - Imran Rashid
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Harrington Heart and Vascular Institute, Cleveland, OH, USA
| | - Jonathan Walker
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Harrington Heart and Vascular Institute, Cleveland, OH, USA
| | - Sanjay Rajagopalan
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Harrington Heart and Vascular Institute, Cleveland, OH, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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Huaroc Moquillaza E, Weiss K, Stelter J, Steinhelfer L, Lee YJ, Amthor T, Koken P, Makowski MR, Braren R, Doneva M, Karampinos DC. Accelerated liver water T 1 mapping using single-shot continuous inversion-recovery spiral imaging. NMR IN BIOMEDICINE 2024; 37:e5097. [PMID: 38269568 DOI: 10.1002/nbm.5097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/21/2023] [Accepted: 12/06/2023] [Indexed: 01/26/2024]
Abstract
PURPOSE Liver T1 mapping techniques typically require long breath holds or long scan time in free-breathing, need correction for B 1 + inhomogeneities and process composite (water and fat) signals. The purpose of this work is to accelerate the multi-slice acquisition of liver water selective T1 (wT1) mapping in a single breath hold, improving the k-space sampling efficiency. METHODS The proposed continuous inversion-recovery (IR) Look-Locker methodology combines a single-shot gradient echo spiral readout, Dixon processing and a dictionary-based analysis for liver wT1 mapping at 3 T. The sequence parameters were adapted to obtain short scan times. The influence of fat, B 1 + inhomogeneities and TE on the estimation of T1 was first assessed using simulations. The proposed method was then validated in a phantom and in 10 volunteers, comparing it with MRS and the modified Look-Locker inversion-recovery (MOLLI) method. Finally, the clinical feasibility was investigated by comparing wT1 maps with clinical scans in nine patients. RESULTS The phantom results are in good agreement with MRS. The proposed method encodes the IR-curve for the liver wT1 estimation, is minimally sensitive to B 1 + inhomogeneities and acquires one slice in 1.2 s. The volunteer results confirmed the multi-slice capability of the proposed method, acquiring nine slices in a breath hold of 11 s. The present work shows robustness to B 1 + inhomogeneities (wT 1 , No B 1 + = 1.07 wT 1 , B 1 + - 45.63 , R 2 = 0.99 ) , good repeatability (wT 1 , 2 ° = 1 . 0 wT 1 , 1 ° - 2.14 , R 2 = 0.96 ) and is in better agreement with MRS (wT 1 = 0.92 wT 1 MRS + 103.28 , R 2 = 0.38 ) than is MOLLI (wT 1 MOLLI = 0.76 wT 1 MRS + 254.43 , R 2 = 0.44 ) . The wT1 maps in patients captured diverse lesions, thus showing their clinical feasibility. CONCLUSION A single-shot spiral acquisition can be combined with a continuous IR Look-Locker method to perform rapid repeatable multi-slice liver water T1 mapping at a rate of 1.2 s per slice without a B 1 + map. The proposed method is suitable for nine-slice liver clinical applications acquired in a single breath hold of 11 s.
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Affiliation(s)
- Elizabeth Huaroc Moquillaza
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | - Jonathan Stelter
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Lisa Steinhelfer
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | | | | | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rickmer Braren
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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11
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Qiu Z, Hu S, Zhao W, Sakaie K, Sun JE, Griswold MA, Jones DK, Ma D. Self-calibrated subspace reconstruction for multidimensional MR fingerprinting for simultaneous relaxation and diffusion quantification. Magn Reson Med 2024; 91:1978-1993. [PMID: 38102776 PMCID: PMC10950540 DOI: 10.1002/mrm.29969] [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: 07/30/2023] [Revised: 11/22/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023]
Abstract
PURPOSE To propose a new reconstruction method for multidimensional MR fingerprinting (mdMRF) to address shading artifacts caused by physiological motion-induced measurement errors without navigating or gating. METHODS The proposed method comprises two procedures: self-calibration and subspace reconstruction. The first procedure (self-calibration) applies temporally local matrix completion to reconstruct low-resolution images from a subset of under-sampled data extracted from the k-space center. The second procedure (subspace reconstruction) utilizes temporally global subspace reconstruction with pre-estimated temporal subspace from low-resolution images to reconstruct aliasing-free, high-resolution, and time-resolved images. After reconstruction, a customized outlier detection algorithm was employed to automatically detect and remove images corrupted by measurement errors. Feasibility, robustness, and scan efficiency were evaluated through in vivo human brain imaging experiments. RESULTS The proposed method successfully reconstructed aliasing-free, high-resolution, and time-resolved images, where the measurement errors were accurately represented. The corrupted images were automatically and robustly detected and removed. Artifact-free T1, T2, and ADC maps were generated simultaneously. The proposed reconstruction method demonstrated robustness across different scanners, parameter settings, and subjects. A high scan efficiency of less than 20 s per slice has been achieved. CONCLUSION The proposed reconstruction method can effectively alleviate shading artifacts caused by physiological motion-induced measurement errors. It enables simultaneous and artifact-free quantification of T1, T2, and ADC using mdMRF scans without prospective gating, with robustness and high scan efficiency.
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Affiliation(s)
- Zhilang Qiu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
| | - Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
| | - Walter Zhao
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
| | - Ken Sakaie
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, United States
| | - Jessie E.P. Sun
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, United States
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, United States
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States
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12
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Mao A, Flassbeck S, Assländer J. Bias-Reduced Neural Networks for Parameter Estimation in Quantitative MRI. ARXIV 2024:arXiv:2312.11468v3. [PMID: 38463512 PMCID: PMC10925387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Purpose To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound. Theory and Methods We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications. Results In simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cramér-Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as non-linear least-squares fitting, while state-of-the-art NNs show larger deviations. Conclusion The proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy.
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Affiliation(s)
- Andrew Mao
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, New York
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York
| | - Sebastian Flassbeck
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, New York
| | - Jakob Assländer
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, New York
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13
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Menon RG, Monga A, Kijowski R, Regatte RR. Characterization of Age-Related and Sex-Related Differences of Relaxation Parameters in the Intervertebral Disc Using MR-Fingerprinting. J Magn Reson Imaging 2024; 59:1312-1324. [PMID: 37610269 PMCID: PMC10935608 DOI: 10.1002/jmri.28925] [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: 04/14/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND Multiparameter characterization using MR fingerprinting (MRF) can quantify multiple relaxation parameters of intervertebral disc (IVD) simultaneously. These parameters may vary by age and sex. PURPOSE To investigate age- and sex-related differences in the relaxation parameters of the IVD of the lumbar spine using a multiparameter MRF technique. STUDY TYPE Prospective. SUBJECTS 17 healthy subjects (8 male; mean age = 34 ± 10 years, range 20-60 years). FIELD STRENGTH/SEQUENCE 3D-MRF sequence for simultaneous acquisition of proton density, T1 , T2 , and T1ρ maps at 3.0T. ASSESSMENT Global mean T1 , T2 , and T1ρ of all lumbar IVDs and mean T1 , T2 , and T1ρ of each individual IVD (L1-L5) were measured. Gray level co-occurrence matrix was used to quantify textural features (median, contrast, correlation, energy, and homogeneity) from T1 , T2 , and T1ρ maps. STATISTICAL TESTS Spearman rank correlations (R) evaluated the association between age and T1 , T2 , and T1ρ of IVD. Mann-Whitney U-tests evaluated differences between males and females in T1 , T2 , and T1ρ of IVD. Statistical significance was defined as P-value <0.05. RESULTS There was a significant negative correlation between age and global mean values of all IVDs for T1 (R = -0.637), T2 (R = -0.509), and T1ρ (R = -0.726). For individual IVDs, there was a significant negative correlation between age and mean T1 at all IVD segments (R range = -0.530 to -0.708), between age and mean T2 at L2-L3, L3-L4, and L4-L5 (R range = -0.493 to 0.640), and between age and mean T1ρ at all segments except L1-L2 (R range = -0.632 to -0.763). There were no significant differences between sexes in global mean T1 , T2, and T1ρ (P-value = 0.23-0.76) The texture features with the highest significant correlations with age for all IVDs were global T1ρ mean (R = -0.726), T1 energy (R = -0.681), and T1 contrast (R = 0.709). CONCLUSION This study showed that the 3D-MRF technique has potential to characterize age-related differences in T1 , T2, or T1ρ of IVD in healthy subjects. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Rajiv G. Menon
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine
| | - Anmol Monga
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine
| | - Richard Kijowski
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine
| | - Ravinder R. Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine
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14
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Assländer J, Gultekin C, Mao A, Zhang X, Duchemin Q, Liu K, Charlson RW, Shepherd TM, Fernandez-Granda C, Flassbeck S. Rapid quantitative magnetization transfer imaging: Utilizing the hybrid state and the generalized Bloch model. Magn Reson Med 2024; 91:1478-1497. [PMID: 38073093 DOI: 10.1002/mrm.29951] [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: 06/15/2023] [Revised: 10/30/2023] [Accepted: 11/14/2023] [Indexed: 02/03/2024]
Abstract
PURPOSE To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model parameters. THEORY AND METHODS We combine two recently proposed models in a Bloch-McConnell equation: the dynamics of the free spin pool are confined to the hybrid state, and the dynamics of the semi-solid spin pool are described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three qMT parameters while accounting for all eight parameters of the two-pool model. We sparsely sample each time frame along this spin dynamics with a three-dimensional radial koosh-ball trajectory, reconstruct the data with subspace modeling, and fit the qMT model with a neural network for computational efficiency. RESULTS We extracted qMT parameter maps of the whole brain with an effective resolution of 1.24 mm from a 12.6-min scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi-solid spin pool and longer relaxation times, consistent with previous reports. CONCLUSION The encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for efficient quantitative magnetization transfer imaging with few constraints on model parameters.
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Affiliation(s)
- Jakob Assländer
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Cem Gultekin
- Courant Institute of Mathematical Sciences, New York University, New York, New York, USA
| | - Andrew Mao
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU School of Medicine, New York, New York, USA
- Vilcek Institute of Graduate Biomedical Sciences, NYU School of Medicine, New York, New York, USA
| | - Xiaoxia Zhang
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Quentin Duchemin
- Laboratoire d'analyse et de mathématiques appliquées, Université Gustave Eiffel, Champs-sur-Marne, France
| | - Kangning Liu
- Center for Data Science, New York University, New York, New York, USA
| | - Robert W Charlson
- Department of Neurology, NYU School of Medicine, New York, New York, USA
| | - Timothy M Shepherd
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
| | - Carlos Fernandez-Granda
- Courant Institute of Mathematical Sciences, New York University, New York, New York, USA
- Center for Data Science, New York University, New York, New York, USA
| | - Sebastian Flassbeck
- Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU School of Medicine, New York, New York, USA
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15
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Marriott A, Rioux J, Brewer K. Nonuniform sliding-window reconstruction for accelerated dual contrast agent quantification with MR fingerprinting. MAGMA (NEW YORK, N.Y.) 2024; 37:273-282. [PMID: 38217784 PMCID: PMC10994993 DOI: 10.1007/s10334-023-01140-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVE MR fingerprinting (MRF) can enable preclinical studies of cell tracking by quantifying multiple contrast agents simultaneously, but faster scan times are required for in vivo applications. Sliding window (SW)-MRF is one option for accelerating MRF, but standard implementations are not sufficient to preserve the accuracy of T2*, which is critical for tracking iron-labelled cells in vivo. PURPOSE To develop a SW approach to MRF which preserves the T2* accuracy required for accelerated concentration mapping of iron-labelled cells on single-channel preclinical systems. METHODS A nonuniform SW was applied to the MRF sequence and dictionary. Segments of the sequence most sensitive to T2* were subject to a shorter window length, preserving the T2* sensitivity. Phantoms containing iron-labelled CD8+ T cells and gadolinium were used to compare 24× undersampled uniform and nonuniform SW-MRF parameter maps. Dual concentration maps were generated for both uniform and nonuniform MRF and compared. RESULTS Lin's concordance correlation coefficient, compared to gold standard parameter values, was much greater for nonuniform SW-MRF than for uniform SW-MRF. A Wilcoxon signed-rank test showed no significant difference between nonuniform SW-MRF and gold standards. Nonuniform SW-MRF outperformed the uniform SW-MRF concentration maps for all parameters, providing a balance between T2* sensitivity of short window lengths, and SNR of longer window lengths. CONCLUSIONS Nonuniform SW-MRF improves the accuracy of matching compared to uniform SW-MRF, allowing higher accelerated concentration mapping for preclinical systems.
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Affiliation(s)
- Anna Marriott
- Biomedical MRI Research Laboratory (BMRL), IWK Health Centre, 5850/5980 University Avenue, Halifax, NS, B3K 6R8, Canada
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada
| | - James Rioux
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada
- Biomedical Translational Imaging Centre (BIOTIC), NS Health, Halifax, NS, Canada
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
| | - Kimberly Brewer
- Biomedical MRI Research Laboratory (BMRL), IWK Health Centre, 5850/5980 University Avenue, Halifax, NS, B3K 6R8, Canada.
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada.
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada.
- School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada.
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada.
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16
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Assländer J, Mao A, Marchetto E, Beck ES, La Rosa F, Charlson RW, Shepherd TM, Flassbeck S. Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools. ARXIV 2024:arXiv:2301.08394v3. [PMID: 36713253 PMCID: PMC9882584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Since the inception of magnetization transfer (MT) imaging, it has been widely assumed that Henkelman's two spin pools have similar longitudinal relaxation times, which motivated many researchers to constrain them to each other. However, several recent publications reported a T 1 s of the semi-solid spin pool that is much shorter than T 1 f of the free pool. While these studies tailored experiments for robust proofs-of-concept, we here aim to quantify the disentangled relaxation processes on a voxel-by-voxel basis in a clinical imaging setting, i.e., with an effective resolution of 1.24mm isotropic and full brain coverage in 12min. To this end, we optimized a hybrid-state pulse sequence for mapping the parameters of an unconstrained MT model. We scanned four people with relapsing-remitting multiple sclerosis (MS) and four healthy controls with this pulse sequence and estimated T 1 f ≈ 1.84 s and T 1 s ≈ 0.34 s in healthy white matter. Our results confirm the reports that T 1 s ≪ T 1 f and we argue that this finding identifies MT as an inherent driver of longitudinal relaxation in brain tissue. Moreover, we estimated a fractional size of the semi-solid spin pool of m 0 s ≈ 0.212 , which is larger than previously assumed. An analysis of T 1 f in normal-appearing white matter revealed statistically significant differences between individuals with MS and controls.
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Affiliation(s)
- Jakob Assländer
- Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI R), Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
| | - Andrew Mao
- Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI R), Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University School of Medicine, 550 1st Avenue, New York, 10016, NY, USA
| | - Elisa Marchetto
- Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI R), Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
| | - Erin S Beck
- Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Department of Neurology, Icahn School of Medicine at Mount Sinai, 5 East 98th Street, New York, 10029, NY, USA
| | - Francesco La Rosa
- Corinne Goldsmith Dickinson Center for Multiple Sclerosis, Department of Neurology, Icahn School of Medicine at Mount Sinai, 5 East 98th Street, New York, 10029, NY, USA
| | - Robert W Charlson
- Department of Neurology, New York University School of Medicine, 240 E 38th Street, New York, 10016, NY, USA
| | - Timothy M Shepherd
- Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
| | - Sebastian Flassbeck
- Center for Biomedical Imaging, Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI R), Dept. of Radiology, New York University School of Medicine, 650 1st Avenue, New York, 10016, NY, USA
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17
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Gu Y, Pan Y, Fang Z, Ma L, Zhu Y, Androjna C, Zhong K, Yu X, Shen D. Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T 1 and T 2 mapping of entire macaque brain. Magn Reson Med 2024; 91:1149-1164. [PMID: 37929695 DOI: 10.1002/mrm.29905] [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: 06/29/2023] [Revised: 09/10/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE Preclinical MR fingerprinting (MRF) suffers from long acquisition time for organ-level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning-assisted fast MRF framework for sub-millimeter T1 and T2 mapping of entire macaque brain on a preclinical 9.4 T MR system. METHODS Three dimensional MRF images were reconstructed by singular value decomposition (SVD) compressed reconstruction. T1 and T2 mapping for each axial slice exploited a self-attention assisted residual U-Net to suppress aliasing-induced quantification errors, and the transmit-field (B1 + ) measurements for robustness against B1 + inhomogeneity. Supervised network training used MRF images simulated via virtual parametric maps and a desired undersampling scheme. This strategy bypassed the difficulties of acquiring fully sampled preclinical MRF data to guide network training. The proposed fast MRF framework was tested on experimental data acquired from ex vivo and in vivo macaque brains. RESULTS The trained network showed reasonable adaptability to experimental MRF images, enabling robust delineation of various T1 and T2 distributions in the brain tissues. Further, the proposed MRF framework outperformed several existing fast MRF methods in handling the aliasing artifacts and capturing detailed cerebral structures in the mapping results. Parametric mapping of entire macaque brain at nominal resolution of 0.35× $$ \times $$ 0.35× $$ \times $$ 1 mm3 can be realized via a 20-min 3D MRF scan, which was sixfold faster than the baseline protocol. CONCLUSION Introducing deep learning to MRF framework paves the way for efficient organ-level high-resolution quantitative MRI in preclinical applications.
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Affiliation(s)
- Yuning Gu
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Yongsheng Pan
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Zhenghan Fang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Lei Ma
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
| | - Yuran Zhu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Charlie Androjna
- Cleveland Clinic Pre-Clinical Magnetic Resonance Imaging Center, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Kai Zhong
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, China
- Anhui Province Key Laboratory of High Field Magnetic Resonance Imaging, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- Biomedical Engineering Department, Peking University, Beijing, China
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dinggang Shen
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
- Shanghai United Imaging Intelligence, Shanghai, China
- Shanghai Clinical Research and Trial Center, ShanghaiTech University, Shanghai, China
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18
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Flassbeck S, Assländer J. Minimization of eddy current artifacts in sequences with periodic dynamics. Magn Reson Med 2024; 91:1067-1074. [PMID: 37994235 DOI: 10.1002/mrm.29945] [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: 07/14/2023] [Revised: 10/18/2023] [Accepted: 11/10/2023] [Indexed: 11/24/2023]
Abstract
PURPOSE To minimize eddy current artifacts in periodic pulse sequences with balanced gradient moments as, for example, used for quantitative MRI. THEORY AND METHODS Eddy current artifacts in balanced sequences result from large jumps in k-space. In quantitative MRI, one often samples some spin dynamics repeatedly while acquiring different parts of k-space. We swap individual k-space lines between different repetitions in order to minimize jumps in temporal succession without changing the overall trajectory. This reordering can be formulated as a traveling salesman problem and we tackle the discrete optimization with a simulated annealing algorithm. RESULTS Compared to the default ordering, we observe a substantial reduction of artifacts in the reconstructed images and the derived quantitative parameter maps. Comparing two variants of our algorithm, one that resembles the pairing approach originally proposed by Bieri et al., and one that minimizes all k-space jumps equally, we observe slightly lower artifact levels in the latter. CONCLUSION The proposed reordering scheme effectively reduces eddy current artifacts in sequences with balanced gradient moments. In contrast to previous approaches, we capitalize on the periodicity of the sampled signal dynamics, enabling both efficient k-space sampling and minimizing artifacts caused by eddy currents.
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Affiliation(s)
- Sebastian Flassbeck
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Jakob Assländer
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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19
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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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20
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Cencini M, Lancione M, Pasquariello R, Peretti L, Pirkl CM, Schulte RF, Buonincontri G, Arduino A, Zilberti L, Biagi L, Tosetti M. Fast high-resolution electric properties tomography using three-dimensional quantitative transient-state imaging-based water fraction estimation. NMR IN BIOMEDICINE 2024; 37:e5039. [PMID: 37714527 DOI: 10.1002/nbm.5039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/18/2023] [Accepted: 08/28/2023] [Indexed: 09/17/2023]
Abstract
In this study, we aimed to develop a fast and robust high-resolution technique for clinically feasible electrical properties tomography based on water content maps (wEPT) using Quantitative Transient-state Imaging (QTI), a multiparametric transient state-based method that is similar to MR fingerprinting. Compared with the original wEPT implementation based on standard spin-echo acquisition, QTI provides robust electrical properties quantification towards B1 + inhomogeneities and full quantitative relaxometry data. To validate the proposed approach, 3D QTI data of 12 healthy volunteers were acquired on a 1.5 T scanner. QTI-provided T1 maps were used to compute water content maps of the tissues using an empirical relationship based on literature ex-vivo measurements. Assuming that electrical properties are modulated mainly by tissue water content, the water content maps were used to derive electrical conductivity and relative permittivity maps. The proposed technique was compared with a conventional phase-only Helmholtz EPT (HH-EPT) acquisition both within whole white matter, gray matter, and cerebrospinal fluid masks, and within different white and gray matter subregions. In addition, QTI-based wEPT was retrospectively applied to four multiple sclerosis adolescent and adult patients, compared with conventional contrast-weighted imaging in terms of lesion delineation, and quantitatively assessed by measuring the variation of electrical properties in lesions. Results obtained with the proposed approach agreed well with theoretical predictions and previous in vivo findings in both white and gray matter. The reconstructed maps showed greater anatomical detail and lower variability compared with standard phase-only HH-EPT. The technique can potentially improve delineation of pathology when compared with conventional contrast-weighted imaging and was able to detect significant variations in lesions with respect to normal-appearing tissues. In conclusion, QTI can reliably measure conductivity and relative permittivity of brain tissues within a short scan time, opening the way to the study of electric properties in clinical settings.
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Affiliation(s)
- Matteo Cencini
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
| | | | | | | | | | | | | | | | - Luca Zilberti
- Istituto Nazionale di Ricerca Metrologica (INRiM), Torino, Italy
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21
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Cabini RF, Barzaghi L, Cicolari D, Arosio P, Carrazza S, Figini S, Filibian M, Gazzano A, Krause R, Mariani M, Peviani M, Pichiecchio A, Pizzagalli DU, Lascialfari A. Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting. NMR IN BIOMEDICINE 2024; 37:e5028. [PMID: 37669779 DOI: 10.1002/nbm.5028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/05/2023] [Accepted: 07/27/2023] [Indexed: 09/07/2023]
Abstract
We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T1 and T2 maps acquired with the magnetic resonance fingerprinting (MRF) methodology. We applied two different MRF sequence routines to acquire images of ex vivo rat brain phantoms using a 7-T preclinical scanner. Subsequently, the DL model was trained using experimental data, completely excluding the use of any theoretical MRI signal simulator. The best combination of the DL parameters was implemented by an automatic hyperparameter optimization strategy, whose key aspect is to include all the parameters to the fit, allowing the simultaneous optimization of the neural network architecture, the structure of the DL model, and the supervised learning algorithm. By comparing the reconstruction performances of the DL technique with those achieved from the traditional dictionary-based method on an independent dataset, the DL approach was shown to reduce the mean percentage relative error by a factor of 3 for T1 and by a factor of 2 for T2 , and to improve the computational time by at least a factor of 37. Furthermore, the proposed DL method enables maintaining comparable reconstruction performance, even with a lower number of MRF images and a reduced k-space sampling percentage, with respect to the dictionary-based method. Our results suggest that the proposed DL methodology may offer an improvement in reconstruction accuracy, as well as speeding up MRF for preclinical, and in prospective clinical, investigations.
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Affiliation(s)
- Raffaella Fiamma Cabini
- Department of Mathematics, University of Pavia, Pavia, Italy
- INFN, Istituto Nazionale di Fisica Nucleare, Pavia, Italy
| | - Leonardo Barzaghi
- Department of Mathematics, University of Pavia, Pavia, Italy
- INFN, Istituto Nazionale di Fisica Nucleare, Pavia, Italy
- Advanced Imaging and Artificial Intelligence, Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy
| | - Davide Cicolari
- Department of Physics, University of Pavia, Pavia, Italy
- Department of Physics, University of Milan, Milan, Italy
- INFN, Istituto Nazionale di Fisica Nucleare, Milan, Italy
- Department of Medical Physics, ASST GOM Niguarda, Milan, Italy
| | - Paolo Arosio
- Department of Physics, University of Milan, Milan, Italy
- INFN, Istituto Nazionale di Fisica Nucleare, Milan, Italy
| | - Stefano Carrazza
- Department of Physics, University of Milan, Milan, Italy
- INFN, Istituto Nazionale di Fisica Nucleare, Milan, Italy
| | - Silvia Figini
- INFN, Istituto Nazionale di Fisica Nucleare, Pavia, Italy
- Department of Social and Political Science, University of Pavia, Pavia, Italy
| | - Marta Filibian
- INFN, Istituto Nazionale di Fisica Nucleare, Pavia, Italy
- Centro Grandi Strumenti, University of Pavia, Pavia, Italy
| | - Andrea Gazzano
- Laboratory of Cellular and Molecular Neuropharmacology, Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia, Italy
| | | | - Manuel Mariani
- Department of Physics, University of Pavia, Pavia, Italy
| | - Marco Peviani
- Laboratory of Cellular and Molecular Neuropharmacology, Department of Biology and Biotechnology "L. Spallanzani", University of Pavia, Pavia, Italy
| | - Anna Pichiecchio
- Advanced Imaging and Artificial Intelligence, Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | | | - Alessandro Lascialfari
- INFN, Istituto Nazionale di Fisica Nucleare, Pavia, Italy
- Department of Physics, University of Pavia, Pavia, Italy
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22
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Rashid I, Lima da Cruz G, Seiberlich N, Hamilton JI. Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications. J Magn Reson Imaging 2023:10.1002/jmri.29206. [PMID: 38153855 PMCID: PMC11211246 DOI: 10.1002/jmri.29206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 12/30/2023] Open
Abstract
Cardiovascular magnetic resonance (CMR) is an established imaging modality with proven utility in assessing cardiovascular diseases. The ability of CMR to characterize myocardial tissue using T1 - and T2 -weighted imaging, parametric mapping, and late gadolinium enhancement has allowed for the non-invasive identification of specific pathologies not previously possible with modalities like echocardiography. However, CMR examinations are lengthy and technically complex, requiring multiple pulse sequences and different anatomical planes to comprehensively assess myocardial structure, function, and tissue composition. To increase the overall impact of this modality, there is a need to simplify and shorten CMR exams to improve access and efficiency, while also providing reproducible quantitative measurements. Multiparametric MRI techniques that measure multiple tissue properties offer one potential solution to this problem. This review provides an in-depth look at one such multiparametric approach, cardiac magnetic resonance fingerprinting (MRF). The article is structured as follows. First, a brief review of single-parametric and (non-Fingerprinting) multiparametric CMR mapping techniques is presented. Second, a general overview of cardiac MRF is provided covering pulse sequence implementation, dictionary generation, fast k-space sampling methods, and pattern recognition. Third, recent technical advances in cardiac MRF are covered spanning a variety of topics, including simultaneous multislice and 3D sampling, motion correction algorithms, cine MRF, synthetic multicontrast imaging, extensions to measure additional clinically important tissue properties (proton density fat fraction, T2 *, and T1ρ ), and deep learning methods for image reconstruction and parameter estimation. The last section will discuss potential clinical applications, concluding with a perspective on how multiparametric techniques like MRF may enable streamlined CMR protocols. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Imran Rashid
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Gastao Lima da Cruz
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, USA
| | - Nicole Seiberlich
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, USA
| | - Jesse I. Hamilton
- School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, USA
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23
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Mao A, Flassbeck S, Gultekin C, Assländer J. Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI. ARXIV 2023:arXiv:2305.00326v2. [PMID: 37961734 PMCID: PMC10635289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
We extend the traditional framework for estimating subspace bases that maximize the preserved signal energy to additionally preserve the Cramér-Rao bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and precision in the quantitative maps. To this end, we introduce an approximate compressed CRB based on orthogonalized versions of the signal's derivatives with respect to the model parameters. This approximation permits singular value decomposition (SVD)-based minimization of both the CRB and signal losses during compression. Compared to the traditional SVD approach, the proposed method better preserves the CRB across all biophysical parameters with negligible cost to the preserved signal energy, leading to reduced bias and variance of the parameter estimates in simulation. In vivo, improved accuracy and precision are observed in two quantitative neuroimaging applications, permitting the use of smaller basis sizes in subspace reconstruction and offering significant computational savings.
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Affiliation(s)
- Andrew Mao
- Center for Biomedical Imaging, NYU School of Medicine, New York, NY 10016
| | | | - Cem Gultekin
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10012
| | - Jakob Assländer
- Center for Biomedical Imaging, NYU School of Medicine, New York, NY 10016
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24
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Hu S, Chen Y, Zong X, Lin W, Griswold M, Ma D. Improving motion robustness of 3D MR fingerprinting with a fat navigator. Magn Reson Med 2023; 90:1802-1817. [PMID: 37345703 PMCID: PMC10524525 DOI: 10.1002/mrm.29761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 05/01/2023] [Accepted: 05/26/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE To develop a 3D MR fingerprinting (MRF) method in combination with fat navigators to improve its motion robustness for neuroimaging. METHODS A rapid fat navigator was developed using the stack-of-spirals acquisition and non-Cartesian spiral GRAPPA. The fat navigator module was implemented in the 3D MRF sequence with high scan efficiency. The developed method was first validated in phantoms and five healthy subjects with intentional head motion. The method was further applied to infants with neonatal opioid withdrawal symptoms. The 3D MRF scans with fat navigators acquired with and without acceleration along the partition-encoding direction were both examined in the study. RESULTS Both phantom and in vivo results demonstrated that the added fat navigator modules did not influence the quantification accuracy in MRF. In combination with non-Cartesian spiral GRAPPA, a rapid fat navigator sampling with whole-brain coverage was achieved in ˜0.5 s at 3T, reducing its sensitivity to potential motion. Based on the motion waveforms extracted from fat navigators, the motion robustness of the 3D MRF was largely improved. With the proposed method, the motion-corrupted MRF datasets yielded T1 and T2 maps with significantly reduced artifacts and high correlations with measurements from the reference motion-free MRF scans. CONCLUSION We developed a 3D MRF method coupled with rapid fat navigators to improve its motion robustness for quantitative neuroimaging. Our results demonstrate that (1) accurate tissue quantification was preserved with the fat navigator modules and (2) the motion robustness for quantitative tissue mapping was largely improved with the developed method.
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Affiliation(s)
- Siyuan Hu
- Department of Biomedical Engineering Cleveland, Ohio, USA
| | - Yong Chen
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Xiaopeng Zong
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Weili Lin
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Mark Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Biomedical Engineering Cleveland, Ohio, USA
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25
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Jun Y, Cho J, Wang X, Gee M, Grant PE, Bilgic B, Gagoski B. SSL-QALAS: Self-Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS. Magn Reson Med 2023; 90:2019-2032. [PMID: 37415389 PMCID: PMC10527557 DOI: 10.1002/mrm.29786] [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: 03/06/2023] [Revised: 05/27/2023] [Accepted: 06/15/2023] [Indexed: 07/08/2023]
Abstract
PURPOSE To develop and evaluate a method for rapid estimation of multiparametric T1 , T2 , proton density, and inversion efficiency maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary. METHODS An SSL-based QALAS mapping method (SSL-QALAS) was developed for rapid and dictionary-free estimation of multiparametric maps from 3D-QALAS measurements. The accuracy of the reconstructed quantitative maps using dictionary matching and SSL-QALAS was evaluated by comparing the estimated T1 and T2 values with those obtained from the reference methods on an International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. The SSL-QALAS and the dictionary-matching methods were also compared in vivo, and generalizability was evaluated by comparing the scan-specific, pre-trained, and transfer learning models. RESULTS Phantom experiments showed that both the dictionary-matching and SSL-QALAS methods produced T1 and T2 estimates that had a strong linear agreement with the reference values in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. Further, SSL-QALAS showed similar performance with dictionary matching in reconstructing the T1 , T2 , proton density, and inversion efficiency maps on in vivo data. Rapid reconstruction of multiparametric maps was enabled by inferring the data using a pre-trained SSL-QALAS model within 10 s. Fast scan-specific tuning was also demonstrated by fine-tuning the pre-trained model with the target subject's data within 15 min. CONCLUSION The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.
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Affiliation(s)
- Yohan Jun
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Jaejin Cho
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Xiaoqing Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Michael Gee
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - P. Ellen Grant
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States
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26
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Liu C, Li T, Cao P, Hui ES, Wong YL, Wang Z, Xiao H, Zhi S, Zhou T, Li W, Lam SK, Cheung ALY, Lee VHF, Ying M, Cai J. Respiratory-Correlated 4-Dimensional Magnetic Resonance Fingerprinting for Liver Cancer Radiation Therapy Motion Management. Int J Radiat Oncol Biol Phys 2023; 117:493-504. [PMID: 37116591 DOI: 10.1016/j.ijrobp.2023.04.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 04/04/2023] [Accepted: 04/18/2023] [Indexed: 04/30/2023]
Abstract
PURPOSE The objective of this study was to develop a respiratory-correlated (RC) 4-dimensional (4D) imaging technique based on magnetic resonance fingerprinting (MRF) (RC-4DMRF) for liver tumor motion management in radiation therapy. METHODS AND MATERIALS Thirteen patients with liver cancer were prospectively enrolled in this study. k-space MRF signals of the liver were acquired during free-breathing using the fast acquisition with steady-state precession sequence on a 3T scanner. The signals were binned into 8 respiratory phases based on respiratory surrogates, and interphase displacement vector fields were estimated using a phase-specific low-rank optimization method. Hereafter, the tissue property maps, including T1 and T2 relaxation times, and proton density, were reconstructed using a pyramid motion-compensated method that alternatively optimized interphase displacement vector fields and subspace images. To evaluate the efficacy of RC-4DMRF, amplitude motion differences and Pearson correlation coefficients were determined to assess measurement agreement in tumor motion between RC-4DMRF and cine magnetic resonance imaging (MRI); mean absolute percentage errors of the RC-4DMRF-derived tissue maps were calculated to reveal tissue quantification accuracy using digital human phantom; and tumor-to-liver contrast-to-noise ratio of RC-4DMRF images was compared with that of planning CT and contrast-enhanced MRI (CE-MRI) images. A paired Student t test was used for statistical significance analysis with a P value threshold of .05. RESULTS RC-4DMRF achieved excellent agreement in motion measurement with cine MRI, yielding the mean (± standard deviation) Pearson correlation coefficients of 0.95 ± 0.05 and 0.93 ± 0.09 and amplitude motion differences of 1.48 ± 1.06 mm and 0.81 ± 0.64 mm in the superior-inferior and anterior-posterior directions, respectively. Moreover, RC-4DMRF achieved high accuracy in tissue property quantification, with mean absolute percentage errors of 8.8%, 9.6%, and 5.0% for T1, T2, and proton density, respectively. Notably, the tumor contrast-to-noise ratio in RC-4DMRI-derived T1 maps (6.41 ± 3.37) was found to be the highest among all tissue property maps, approximately equal to that of CE-MRI (6.96 ± 1.01, P = .862), and substantially higher than that of planning CT (2.91 ± 1.97, P = .048). CONCLUSIONS RC-4DMRF demonstrated high accuracy in respiratory motion measurement and tissue properties quantification, potentially facilitating tumor motion management in liver radiation therapy.
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Affiliation(s)
- Chenyang Liu
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Tian Li
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Peng Cao
- Department of Diagnostic Radiology, University of Hong Kong, Hong Kong SAR, China
| | - Edward S Hui
- Department of Imaging and Interventional Radiology, Chinese University of Hong Kong, Hong Kong SAR, China; Department of Psychiatry, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yat-Lam Wong
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Zuojun Wang
- Department of Diagnostic Radiology, University of Hong Kong, Hong Kong SAR, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shaohua Zhi
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Ta Zhou
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Wen Li
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Sai Kit Lam
- Department of Biomedical Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China; Research Institute for Smart Ageing, Hong Kong Polytechnic University, Hong Kong SAR, China
| | | | - Victor Ho-Fun Lee
- Department of Clinical Oncology, University of Hong Kong, Hong Kong SAR, China
| | - Michael Ying
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China.
| | - Jing Cai
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong SAR, China; Research Institute for Smart Ageing, Hong Kong Polytechnic University, Hong Kong SAR, China.
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27
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Sheagren CD, Cao T, Patel JH, Chen Z, Lee HL, Wang N, Christodoulou AG, Wright GA. Motion-compensated T 1 mapping in cardiovascular magnetic resonance imaging: a technical review. Front Cardiovasc Med 2023; 10:1160183. [PMID: 37790594 PMCID: PMC10542904 DOI: 10.3389/fcvm.2023.1160183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/22/2023] [Indexed: 10/05/2023] Open
Abstract
T 1 mapping is becoming a staple magnetic resonance imaging method for diagnosing myocardial diseases such as ischemic cardiomyopathy, hypertrophic cardiomyopathy, myocarditis, and more. Clinically, most T 1 mapping sequences acquire a single slice at a single cardiac phase across a 10 to 15-heartbeat breath-hold, with one to three slices acquired in total. This leaves opportunities for improving patient comfort and information density by acquiring data across multiple cardiac phases in free-running acquisitions and across multiple respiratory phases in free-breathing acquisitions. Scanning in the presence of cardiac and respiratory motion requires more complex motion characterization and compensation. Most clinical mapping sequences use 2D single-slice acquisitions; however newer techniques allow for motion-compensated reconstructions in three dimensions and beyond. To further address confounding factors and improve measurement accuracy, T 1 maps can be acquired jointly with other quantitative parameters such as T 2 , T 2 ∗ , fat fraction, and more. These multiparametric acquisitions allow for constrained reconstruction approaches that isolate contributions to T 1 from other motion and relaxation mechanisms. In this review, we examine the state of the literature in motion-corrected and motion-resolved T 1 mapping, with potential future directions for further technical development and clinical translation.
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Affiliation(s)
- Calder D. Sheagren
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Tianle Cao
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, CA, United States
| | - Jaykumar H. Patel
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Zihao Chen
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, CA, United States
| | - Hsu-Lei Lee
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Nan Wang
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Anthony G. Christodoulou
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Bioengineering, University of California, Los Angeles, CA, United States
| | - Graham A. Wright
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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28
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Yu VY, Otazo R, Wu C, Subashi E, Baumann M, Koken P, Doneva M, Mazurkewitz P, Shasha D, Zelefsky M, Cervino L, Cohen O. Quantitative longitudinal mapping of radiation-treated prostate cancer using MR fingerprinting with radial acquisition and subspace reconstruction. Magn Reson Imaging 2023; 101:25-34. [PMID: 37015305 PMCID: PMC10623548 DOI: 10.1016/j.mri.2023.03.019] [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: 02/01/2023] [Accepted: 03/29/2023] [Indexed: 04/06/2023]
Abstract
MR fingerprinting (MRF) enables fast multiparametric quantitative imaging with a single acquisition and has been shown to improve diagnosis of prostate cancer. However, most prostate MRF studies were performed with spiral acquisitions that are sensitive to B0 inhomogeneities and consequent blurring. In this work, a radial MRF acquisition with a novel subspace reconstruction technique was developed to enable fast T1/T2 mapping in the prostate in under 4 min. The subspace reconstruction exploits the extensive temporal correlations in the MRF dictionary to pre-compute a low dimensional space for the solution and thus reduce the number of radial spokes to accelerate the acquisition. Iterative reconstruction with the subspace model and additional regularization of the signal representation in the subspace is performed to minimize the number of spokes and maintain matching quality and SNR. Reconstruction accuracy was assessed using the ISMRM NIST phantom. In-vivo validation was performed on two healthy subjects and two prostate cancer patients undergoing radiation therapy. The longitudinal repeatability was quantified using the concordance correlation coefficient (CCC) in one of the healthy subjects by repeated scans over 1 year. One prostate cancer patient was scanned at three time points, before initiating therapy and following brachytherapy and external beam radiation. Changes in the T1/T2 maps obtained with the proposed method were quantified. The prostate, peripheral and transitional zones, and visible dominant lesion were delineated for each study, and the statistics and distribution of the quantitative mapping values were analyzed. Significant image quality improvements compared with standard reconstruction methods were obtained with the proposed subspace reconstruction method. A notable decrease in the spread of the T1/T2 values without biasing the estimated mean values was observed with the subspace reconstruction and agreed with reported literature values. The subspace reconstruction enabled visualization of small differences in T1/T2 values in the tumor region within the peripheral zone. Longitudinal imaging of a volunteer subject yielded CCC of 0.89 for MRF T1, and 0.81 for MRF T2 in the prostate gland. Longitudinal imaging of the prostate patient confirmed the feasibility of capturing radiation treatment related changes. This work is a proof-of-concept for a high resolution and fast quantitative mapping using golden-angle radial MRF combined with a subspace reconstruction technique for longitudinal treatment response assessment in subjects undergoing radiation treatment.
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Affiliation(s)
- Victoria Y Yu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ergys Subashi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Peter Koken
- Philips Research, MR Research, Hamburg, Germany
| | | | | | - Daniel Shasha
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael Zelefsky
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Laura Cervino
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ouri Cohen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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29
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Kang B, Singh M, Park H, Heo HY. Only-train-once MR fingerprinting for B 0 and B 1 inhomogeneity correction in quantitative magnetization-transfer contrast. Magn Reson Med 2023; 90:90-102. [PMID: 36883726 PMCID: PMC10149616 DOI: 10.1002/mrm.29629] [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: 09/29/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 03/09/2023]
Abstract
PURPOSE To develop a fast, deep-learning approach for quantitative magnetization-transfer contrast (MTC)-MR fingerprinting (MRF) that simultaneously estimates multiple tissue parameters and corrects the effects of B0 and B1 variations. METHODS An only-train-once recurrent neural network was designed to perform the fast tissue-parameter quantification for a large range of different MRF acquisition schedules. It enabled a dynamic scan-wise linear calibration of the scan parameters using the measured B0 and B1 maps, which allowed accurate, multiple-tissue parameter mapping. MRF images were acquired from 8 healthy volunteers at 3 T. Estimated parameter maps from the MRF images were used to synthesize the MTC reference signal (Zref ) through Bloch equations at multiple saturation power levels. RESULTS The B0 and B1 errors in MR fingerprints, if not corrected, would impair the tissue quantification and subsequently corrupt the synthesized MTC reference images. Bloch equation-based numerical phantom studies and synthetic MRI analysis demonstrated that the proposed approach could correctly estimate water and semisolid macromolecule parameters, even with severe B0 and B1 inhomogeneities. CONCLUSION The only-train-once deep-learning framework can improve the reconstruction accuracy of brain-tissue parameter maps and be further combined with any conventional MRF or CEST-MRF method.
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Affiliation(s)
- Beomgu Kang
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Munendra Singh
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - HyunWook Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea
| | - Hye-Young Heo
- Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
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Liu Y, Hamilton J, Jiang Y, Seiberlich N. Assessment of MRF for simultaneous T 1 and T 2 quantification and water-fat separation in the liver at 0.55 T. MAGMA (NEW YORK, N.Y.) 2023; 36:513-523. [PMID: 36574163 PMCID: PMC10293475 DOI: 10.1007/s10334-022-01057-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 11/10/2022] [Accepted: 12/13/2022] [Indexed: 12/28/2022]
Abstract
OBJECTIVE The goal of this work was to assess the feasibility of performing MRF in the liver on a 0.55 T scanner and to examine the feasibility of water-fat separation using rosette MRF at 0.55 T. MATERIALS AND METHODS Spiral and rosette MRF sequences were implemented on a commercial 0.55 T scanner. The accuracy of both sequences in T1 and T2 quantification was validated in the ISMRM/NIST system phantom. The efficacy of rosette MRF in water-fat separation was evaluated in simulations and water/oil phantoms. Both spiral and rosette MRF were performed in the liver of healthy subjects. RESULTS In the ISMRM/NIST phantom, both spiral and rosette MRF achieved good agreement with reference values in T1 and T2 measurements. In addition, rosette MRF enables water-fat separation and can generate water- and fat- specific T1 maps, T2 maps, and proton density images from the same dataset for a spatial resolution of 1.56 × 1.56 × 5mm3 within the acquisition time of 15 s. CONCLUSION It is feasible to measure T1 and T2 simultaneously in the liver using MRF on a 0.55 T system with lower performance gradients compared to state-of-the-art 1.5 T and 3 T systems within an acquisition time of 15 s. In addition, rosette MRF enables water-fat separation along with T1 and T2 quantification with no time penalty.
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Affiliation(s)
- Yuchi Liu
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA.
| | - Jesse Hamilton
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Yun Jiang
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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31
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Cruz G, Hua A, Munoz C, Ismail TF, Chiribiri A, Botnar RM, Prieto C. Low-rank motion correction for accelerated free-breathing first-pass myocardial perfusion imaging. Magn Reson Med 2023; 90:64-78. [PMID: 36861454 PMCID: PMC10952238 DOI: 10.1002/mrm.29626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 12/29/2022] [Accepted: 02/10/2023] [Indexed: 03/03/2023]
Abstract
PURPOSE Develop a novel approach for accelerated 2D free-breathing myocardial perfusion via low-rank motion-corrected (LRMC) reconstructions. METHODS Myocardial perfusion imaging requires high spatial and temporal resolution, despite scan time constraints. Here, we incorporate LRMC models into the reconstruction-encoding operator, together with high-dimensionality patch-based regularization, to produce high quality, motion-corrected myocardial perfusion series from free-breathing acquisitions. The proposed framework estimates beat-to-beat nonrigid respiratory (and any other incidental) motion and the dynamic contrast subspace from the actual acquired data, which are then incorporated into the proposed LRMC reconstruction. LRMC was compared with iterative SENSitivity Encoding (SENSE) (itSENSE) and low-rank plus sparse (LpS) reconstruction in 10 patients based on image-quality scoring and ranking by two clinical expert readers. RESULTS LRMC achieved significantly improved results relative to itSENSE and LpS in terms of image sharpness, temporal coefficient of variation, and expert reader evaluation. Left ventricle image sharpness was approximately 75%, 79%, and 86% for itSENSE, LpS and LRMC, respectively, indicating improved image sharpness for the proposed approach. Corresponding temporal coefficient of variation results were 23%, 11% and 7%, demonstrating improved temporal fidelity of the perfusion signal with the proposed LRMC. Corresponding clinical expert reader scores (1-5, from poor to excellent image quality) were 3.3, 3.9 and 4.9, demonstrating improved image quality with the proposed LRMC, in agreement with the automated metrics. CONCLUSION LRMC produces motion-corrected myocardial perfusion in free-breathing acquisitions with substantially improved image quality when compared with iterative SENSE and LpS reconstructions.
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Affiliation(s)
- Gastao Cruz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Alina Hua
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Camila Munoz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Tevfik Fehmi Ismail
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - René Michael Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Escuela de Ingeniería, Pontificia Universidad Católica de ChileSantiagoChile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTHSantiagoChile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
- Escuela de Ingeniería, Pontificia Universidad Católica de ChileSantiagoChile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTHSantiagoChile
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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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33
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Beracha I, Seginer A, Tal A. Adaptive model-based Magnetic Resonance. Magn Reson Med 2023. [PMID: 37154407 DOI: 10.1002/mrm.29688] [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: 10/06/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 05/10/2023]
Abstract
PURPOSE Conventional sequences are static in nature, fixing measurement parameters in advance in anticipation of a wide range of expected tissue parameter values. We set out to design and benchmark a new, personalized approach-termed adaptive MR-in which incoming subject data is used to update and fine-tune the pulse sequence parameters in real time. METHODS We implemented an adaptive, real-time multi-echo (MTE) experiment for estimating T2 s. Our approach combined a Bayesian framework with model-based reconstruction. It maintained and continuously updated a prior distribution of the desired tissue parameters, including T2 , which was used to guide the selection of sequence parameters in real time. RESULTS Computer simulations predicted accelerations between 1.7- and 3.3-fold for adaptive multi-echo sequences relative to static ones. These predictions were corroborated in phantom experiments. In healthy volunteers, our adaptive framework accelerated the measurement of T2 for n-acetyl-aspartate by a factor of 2.5. CONCLUSION Adaptive pulse sequences that alter their excitations in real time could provide substantial reductions in acquisition times. Given the generality of our proposed framework, our results motivate further research into other adaptive model-based approaches to MRI and MRS.
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Affiliation(s)
- Inbal Beracha
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
| | | | - Assaf Tal
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, Israel
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Hu S, Jordan S, Boyacioglu R, Rozada I, Troyer M, Griswold M, McGivney D, Ma D. A fast MR fingerprinting simulator for direct error estimation and sequence optimization. Magn Reson Imaging 2023; 98:105-114. [PMID: 36681312 PMCID: PMC10002151 DOI: 10.1016/j.mri.2023.01.011] [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: 03/22/2022] [Accepted: 01/14/2023] [Indexed: 01/19/2023]
Abstract
Magnetic resonance fingerprinting (MRF) is a novel quantitative MR technique that simultaneously provides multiple tissue property maps. When optimizing MRF scans, modeling undersampling errors and field imperfections in cost functions for direct measurement of quantitative errors will make the optimization results more practical and robust. However, optimizing such cost function is computationally expensive and impractical for MRF optimization with tens of thousands of iterations. Here, we introduce a fast MRF simulator to simulate aliased images from actual scan scenarios including undersampling and system imperfections, which substantially reduces computational time and allows for direct error estimation of the quantitative maps and efficient sequence optimization. We evaluate the performance and computational speed of the proposed approach by simulations and in vivo experiments. The simulations from the proposed method closely approximate the signals and MRF maps from in vivo scans, with 158 times shorter processing time than the conventional simulation method using Non-uniform Fourier transform. We also demonstrate the power of applying the fast MRF simulator in MRF sequence optimization. The optimized sequences are validated with in vivo scans to assess the image quality and accuracy. The optimized sequences produce artifact-free T1 and T2 maps in 2D and 3D scans with equivalent mapping accuracy as the human-designed sequence but at shorter scan times. Incorporating the proposed simulator in the MRF optimization framework makes direct estimation of undersampling errors during the optimization process feasible, and provide optimized MRF sequences that are robust against undersampling artifacts and field inhomogeneity.
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Affiliation(s)
- Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | | | - Rasim Boyacioglu
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Ignacio Rozada
- 1QBit Information Technologies Inc., Vancouver, BC V6E 4B1, Canada
| | | | - Mark Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Debra McGivney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
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Ullah I, Hassan AM, Saad RM, Omer H. GPU accelerated grouped magnetic resonance fingerprinting using clustering techniques. Magn Reson Imaging 2023; 97:13-23. [PMID: 36581213 DOI: 10.1016/j.mri.2022.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/19/2022] [Accepted: 12/23/2022] [Indexed: 12/28/2022]
Abstract
Magnetic Resonance Fingerprinting (MRF) is a new quantitative technique of Magnetic Resonance Imaging (MRI). Conventionally, MRF requires sequential correlation of the acquired MRF signals with all the signals of (a large sized) MRF dictionary. This is a computationally intensive matching process and is a major challenge in MRF image reconstruction. This paper introduces the use of clustering techniques (to reduce the effective size of MRF dictionary) by splitting MRF dictionary into multiple small sized MRF dictionary components called MRF signal groups. The proposed method has been further optimized for parallel processing to reduce the computation time of MRF pattern matching. A multi-core GPU based parallel framework has been developed that enables the MRF algorithm to process multiple MRF signals simultaneously. Experiments have been performed on human head and phantom datasets. The results show that the proposed method accelerates the conventional MRF (MATLAB based) reconstruction time up to 25× with single-core CPU implementation, 300× with multi- core CPU implementation and 1035× with the proposed multi-core GPU based framework by keeping the SNR of the resulting images in a clinically acceptable range. Furthermore, experimental results show that the memory requirements of MRF dictionary get significantly reduced (due to efficient memory utilization) in the proposed method.
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Affiliation(s)
- Irfan Ullah
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan.
| | - Abdul Moiz Hassan
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Rana Muhammad Saad
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Hammad Omer
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
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36
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Menon RG, Sharafi A, Muccio M, Smith T, Kister I, Ge Y, Regatte RR. Three-dimensional multi-parameter brain mapping using MR fingerprinting. RESEARCH SQUARE 2023:rs.3.rs-2675278. [PMID: 36993561 PMCID: PMC10055680 DOI: 10.21203/rs.3.rs-2675278/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
The purpose of this study was to develop and test a 3D multi-parameter MR fingerprinting (MRF) method for brain imaging applications. The subject cohort included 5 healthy volunteers, repeatability tests done on 2 healthy volunteers and tested on two multiple sclerosis (MS) patients. A 3D-MRF imaging technique capable of quantifying T1, T2 and T1ρ was used. The imaging sequence was tested in standardized phantoms and 3D-MRF brain imaging with multiple shots (1, 2 and 4) in healthy human volunteers and MS patients. Quantitative parametric maps for T1, T2, T1ρ, were generated. Mean gray matter (GM) and white matter (WM) ROIs were compared for each mapping technique, Bland-Altman plots and intra-class correlation coefficient (ICC) were used to assess repeatability and Student T-tests were used to compare results in MS patients. Standardized phantom studies demonstrated excellent agreement with reference T1/T2/T1ρ mapping techniques. This study demonstrates that the 3D-MRF technique is able to simultaneously quantify T1, T2 and T1ρ for tissue property characterization in a clinically feasible scan time. This multi-parametric approach offers increased potential to detect and differentiate brain lesions and to better test imaging biomarker hypotheses for several neurological diseases, including MS.
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Affiliation(s)
| | | | | | - Tyler Smith
- New York University Grossman School of Medicine
| | - Ilya Kister
- New York University Grossman School of Medicine
| | - Yulin Ge
- New York University Grossman School of Medicine
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37
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Phair A, Cruz G, Qi H, Botnar RM, Prieto C. Free-running 3D whole-heart T 1 and T 2 mapping and cine MRI using low-rank reconstruction with non-rigid cardiac motion correction. Magn Reson Med 2023; 89:217-232. [PMID: 36198014 PMCID: PMC9828568 DOI: 10.1002/mrm.29449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/14/2022] [Accepted: 08/18/2022] [Indexed: 01/12/2023]
Abstract
PURPOSE To introduce non-rigid cardiac motion correction into a novel free-running framework for the simultaneous acquisition of 3D whole-heart myocardial <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images, enabling a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> <mml:annotation>$$ \sim $$</mml:annotation></mml:semantics> </mml:math> 3-min scan. METHODS Data were acquired using a free-running 3D golden-angle radial readout interleaved with inversion recovery and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> -preparation pulses. After correction for translational respiratory motion, non-rigid cardiac-motion-corrected reconstruction with dictionary-based low-rank compression and patch-based regularization enabled 3D whole-heart <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> mapping at any given cardiac phase as well as whole-heart cardiac cine imaging. The framework was validated and compared with established methods in 11 healthy subjects. RESULTS Good quality 3D <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images were reconstructed for all subjects. Septal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> values using the proposed approach ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1200</mml:mn> <mml:mo>±</mml:mo> <mml:mn>50</mml:mn></mml:mrow> <mml:annotation>$$ 1200\pm 50 $$</mml:annotation></mml:semantics> </mml:math> ms) were higher than those from a 2D MOLLI sequence ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>1063</mml:mn> <mml:mo>±</mml:mo> <mml:mn>33</mml:mn></mml:mrow> <mml:annotation>$$ 1063\pm 33 $$</mml:annotation></mml:semantics> </mml:math> ms), which is known to underestimate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> , while <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> values from the proposed approach ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>51</mml:mn> <mml:mo>±</mml:mo> <mml:mn>4</mml:mn></mml:mrow> <mml:annotation>$$ 51\pm 4 $$</mml:annotation></mml:semantics> </mml:math> ms) were in good agreement with those from a 2D GraSE sequence ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mn>51</mml:mn> <mml:mo>±</mml:mo> <mml:mn>2</mml:mn></mml:mrow> <mml:annotation>$$ 51\pm 2 $$</mml:annotation></mml:semantics> </mml:math> ms). CONCLUSION The proposed technique provides 3D <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_1 $$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow> <mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:mrow> <mml:annotation>$$ {T}_2 $$</mml:annotation></mml:semantics> </mml:math> maps and cine images with isotropic spatial resolution in a single <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>∼</mml:mo></mml:mrow> <mml:annotation>$$ \sim $$</mml:annotation></mml:semantics> </mml:math> 3.3-min scan.
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Affiliation(s)
- Andrew Phair
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Gastão Cruz
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Haikun Qi
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
| | - René M. Botnar
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK,Instituto de Ingeniería Biológica y MédicaPontificia Universidad Católica de ChileSantiagoChile,Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile,Millennium Institute for Intelligent Healthcare EngineeringSantiagoChile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK,Escuela de IngenieríaPontificia Universidad Católica de ChileSantiagoChile,Millennium Institute for Intelligent Healthcare EngineeringSantiagoChile
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Nagtegaal M, Hartsema E, Koolstra K, Vos F. Multicomponent MR fingerprinting reconstruction using joint-sparsity and low-rank constraints. Magn Reson Med 2023; 89:286-298. [PMID: 36121015 PMCID: PMC9825911 DOI: 10.1002/mrm.29442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/12/2022] [Accepted: 08/12/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE To develop an efficient algorithm for multicomponent MR fingerprinting (MC-MRF) reconstructions directly from highly undersampled data without making prior assumptions about tissue relaxation times and expected number of tissues. METHODS The proposed method reconstructs MC-MRF maps from highly undersampled data by iteratively applying a joint-sparsity constraint to the estimated tissue components. Intermediate component maps are obtained by a low-rank multicomponent alternating direction method of multipliers (MC-ADMM) including the non-negativity of tissue weights as an extra regularization term. Over iterations, the used dictionary compression is adjusted. The proposed method (k-SPIJN) is compared with a two-step approach in which image reconstruction and multicomponent estimations are performed sequentially and tested in numerical simulations and in vivo by applying different undersampling factors in eight healthy volunteers. In the latter case, fully sampled data serves as the reference. RESULTS The proposed method shows improved precision and accuracy in simulations compared with a state-of-art sequential approach. Obtained in vivo magnetization fraction maps for different tissue types show reduced systematic errors and reduced noise-like effects. Root mean square errors in estimated magnetization fraction maps significantly reduce from 13.0% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 5.8% with the conventional, two-step approach to 9.6% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 3.9% and 9.6% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mo>±</mml:mo></mml:mrow> <mml:annotation>$$ \pm $$</mml:annotation></mml:semantics> </mml:math> 3.2% with the proposed MC-ADMM and k-SPIJN methods, respectively. Mean standard deviation in homogeneous white matter regions reduced significantly from 8.6% to 2.9% (two step vs. k-SPIJN). CONCLUSION The proposed MC-ADMM and k-SPIJN reconstruction methods estimate MC-MRF maps from highly undersampled data resulting in improved image quality compared with the existing method.
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Affiliation(s)
- Martijn Nagtegaal
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Emiel Hartsema
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands
| | - Kirsten Koolstra
- Division of Image Processing, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Frans Vos
- Department of Imaging PhysicsDelft University of TechnologyDelftThe Netherlands,Department of RadiologyErasmus MCRotterdamThe Netherlands
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Sharafi A, Zibetti MVW, Chang G, Cloos M, Regatte RR. 3D magnetic resonance fingerprinting for rapid simultaneous T1, T2, and T1ρ volumetric mapping of human articular cartilage at 3 T. NMR IN BIOMEDICINE 2022; 35:e4800. [PMID: 35815660 PMCID: PMC9669203 DOI: 10.1002/nbm.4800] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 06/28/2022] [Accepted: 07/08/2022] [Indexed: 05/25/2023]
Abstract
Quantitative MRI can detect early biochemical changes in cartilage; however, the conventional techniques only measure one parameter (e.g., T1 , T2 , and T1ρ ) at a time while also being comparatively slow. We implemented a 3D magnetic resonance fingerprinting (3D-MRF) technique for simultaneous, volumetric mapping of T1 , T2 , and T1ρ in knee articular cartilage in under 9 min. It is evaluated on 11 healthy volunteers (mean age: 53 ± 9 years), five mild knee osteoarthritis (OA) patients (Kellgren-Lawrence (KL) score: 2, mean age: 60 ± 4 years), and the National Institute of Standards and Technology (NIST)/International Society for Magnetic Resonance in Medicine (ISMRM) system phantom. Proton density image, and T1 , T2, T1ρ relaxation times, and B1 + were estimated in the NIST/ISMRM system phantom as well as in the human knee medial and lateral femur, medial and lateral tibia, and patellar cartilage. The repeatability and reproducibility of the proposed technique were assessed in the phantom using analysis of the Bland-Altman plots. The intrasubject repeatability was assessed with the coefficient of variation (CV) and root mean square CV (rmsCV). The Mann-Whitney U test was used to assess the difference between healthy subjects and mild knee OA patients. The Bland-Altman plots in the NIST/ISMRM phantom demonstrated an average difference of 0.001% ± 015%, 1.2% ± 7.1%, and 0.47% ± 3% between two scans from the same 3-T scanner (repeatability), and 0.002% ± 015%, 0.62% ± 10.5%, and 0.97% ± 14% between the scans acquired on two different 3-T scanners (reproducibility) for T1 , T2 , and T1ρ , respectively. The in vivo knee study showed excellent repeatability with rmsCV less than 1%, 2%, and 1% for T1 , T2 , and T1ρ , respectively. T1ρ relaxation time in the mild knee OA patients was significantly higher (p < 0.05) than in healthy subjects. The proposed 3D-MRF sequence is fast, reproducible, robust to B1 + inhomogeneity, and can simultaneously measure the T1 , T2 , T1ρ , and B1 + volumetric maps of the knee joint in a single scan within a clinically feasible scan time.
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Affiliation(s)
- Azadeh Sharafi
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Marcelo V. W. Zibetti
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Gregory Chang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Martijn Cloos
- Center of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ravinder R. Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
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Quantitative Relaxometry Metrics for Brain Metastases Compared to Normal Tissues: A Pilot MR Fingerprinting Study. Cancers (Basel) 2022; 14:cancers14225606. [PMID: 36428699 PMCID: PMC9688653 DOI: 10.3390/cancers14225606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 11/17/2022] Open
Abstract
The purpose of the present pilot study was to estimate T1 and T2 metric values derived simultaneously from a new, rapid Magnetic Resonance Fingerprinting (MRF) technique, as well as to assess their ability to characterize-brain metastases (BM) and normal-appearing brain tissues. Fourteen patients with BM underwent MRI, including prototype MRF, on a 3T scanner. In total, 108 measurements were analyzed: 42 from solid parts of BM's (21 each on T1 and T2 maps) and 66 from normal-appearing brain tissue (11 ROIs each on T1 and T2 maps for gray matter [GM], white matter [WM], and cerebrospinal fluid [CSF]). The BM's mean T1 and T2 values differed significantly from normal-appearing WM (p < 0.05). The mean T1 values from normal-appearing GM, WM, and CSF regions were 1205 ms, 840 ms, and 4233 ms, respectively. The mean T2 values were 108 ms, 78 ms, and 442 ms, respectively. The mean T1 and T2 values for untreated BM (n = 4) were 2035 ms and 168 ms, respectively. For treated BM (n = 17) the T1 and T2 values were 2163 ms and 141 ms, respectively. MRF technique appears to be a promising and rapid quantitative method for the characterization of free water content and tumor morphology in BMs.
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Cardiac MR fingerprinting with a short acquisition window in consecutive patients referred for clinical CMR and healthy volunteers. Sci Rep 2022; 12:18705. [PMID: 36333385 PMCID: PMC9636181 DOI: 10.1038/s41598-022-23573-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 11/02/2022] [Indexed: 11/06/2022] Open
Abstract
Cardiac Magnetic Resonance Fingerprinting (cMRF) has been demonstrated to enable robust and accurate T1 and T2 mapping for the detection of myocardial fibrosis and edema. However, the relatively long acquisition window (250 ms) used in previous cMRF studies might leave it vulnerable to motion artifacts in patients with high heart rates. The goal of this study was therefore to compare cMRF with a short acquisition window (154 ms) and low-rank reconstruction to routine cardiac T1 and T2 mapping at 1.5 T. Phantom studies showed that the proposed cMRF had a high T1 and T2 accuracy over a wider range than routine mapping techniques. In 9 healthy volunteers, the proposed cMRF showed small but significant myocardial T1 and T2 differences compared to routine mapping (ΔT1 = 1.5%, P = 0.031 and ΔT2 = - 7.1%, P < 0.001). In 61 consecutive patients referred for CMR, the native T1 values were slightly lower (ΔT1 = 1.6%; P = 0.02), while T2 values did not show statistical difference (ΔT2 = 4.3%; P = 0.11). However, the difference was higher in post-contrast myocardial T1 values (ΔT1 = 12.3%; P < 0.001), which was reflected in the extracellular volume (ΔECV = 2.4%; P < 0.001). Across all subjects, the proposed cMRF had a lower precision when compared to routine techniques, although its higher spatial resolution enabled the visualization of smaller details.
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Liu H, van der Heide O, Mandija S, van den Berg CAT, Sbrizzi A. Acceleration Strategies for MR-STAT: Achieving High-Resolution Reconstructions on a Desktop PC Within 3 Minutes. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2681-2692. [PMID: 35436186 DOI: 10.1109/tmi.2022.3168436] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
MR-STAT is an emerging quantitative magnetic resonance imaging technique which aims at obtaining multi-parametric tissue parameter maps from single short scans. It describes the relationship between the spatial-domain tissue parameters and the time-domain measured signal by using a comprehensive, volumetric forward model. The MR-STAT reconstruction solves a large-scale nonlinear problem, thus is very computationally challenging. In previous work, MR-STAT reconstruction using Cartesian readout data was accelerated by approximating the Hessian matrix with sparse, banded blocks, and can be done on high performance CPU clusters with tens of minutes. In the current work, we propose an accelerated Cartesian MR-STAT algorithm incorporating two different strategies: firstly, a neural network is trained as a fast surrogate to learn the magnetization signal not only in the full time-domain but also in the compressed low-rank domain; secondly, based on the surrogate model, the Cartesian MR-STAT problem is re-formulated and split into smaller sub-problems by the alternating direction method of multipliers. The proposed method substantially reduces the computational requirements for runtime and memory. Simulated and in-vivo balanced MR-STAT experiments show similar reconstruction results using the proposed algorithm compared to the previous sparse Hessian method, and the reconstruction times are at least 40 times shorter. Incorporating sensitivity encoding and regularization terms is straightforward, and allows for better image quality with a negligible increase in reconstruction time. The proposed algorithm could reconstruct both balanced and gradient-spoiled in-vivo data within 3 minutes on a desktop PC, and could thereby facilitate the translation of MR-STAT in clinical settings.
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Lo W, Bittencourt LK, Panda A, Jiang Y, Tokuda J, Seethamraju R, Tempany‐Afdhal C, Obmann V, Wright K, Griswold M, Seiberlich N, Gulani V. Multicenter Repeatability and Reproducibility of MR Fingerprinting in Phantoms and in Prostatic Tissue. Magn Reson Med 2022; 88:1818-1827. [PMID: 35713379 PMCID: PMC9469467 DOI: 10.1002/mrm.29264] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 02/15/2022] [Accepted: 03/22/2022] [Indexed: 11/12/2022]
Abstract
PURPOSE To evaluate multicenter repeatability and reproducibility of T1 and T2 maps generated using MR fingerprinting (MRF) in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI system phantom and in prostatic tissues. METHODS MRF experiments were performed on 5 different 3 Tesla MRI scanners at 3 different institutions: University Hospitals Cleveland Medical Center (Cleveland, OH), Brigham and Women's Hospital (Boston, MA) in the United States, and Diagnosticos da America (Rio de Janeiro, RJ) in Brazil. Raw MRF data were reconstructed using a Gadgetron-based MRF online reconstruction pipeline to yield quantitative T1 and T2 maps. The repeatability of T1 and T2 values over 6 measurements in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology MRI system phantom was assessed to demonstrate intrascanner variation. The reproducibility between the 4 clinical scanners was assessed to demonstrate interscanner variation. The same-day test-retest normal prostate mean T1 and T2 values from peripheral zone and transitional zone were also compared using the intraclass correlation coefficient and Bland-Altman analysis. RESULTS The intrascanner variation of values measured using MRF was less than 2% for T1 and 4.7% for T2 for relaxation values, within the range of 307.7 to 2360 ms for T1 and 19.1 to 248.5 ms for T2 . Interscanner measurements showed that the T1 variation was less than 4.9%, and T2 variation was less than 8.1% between multicenter scanners. Both T1 and T2 values in in vivo prostatic tissue demonstrated high test-retest reliability (intraclass correlation coefficient > 0.92) and strong linear correlation (R2 > 0.840). CONCLUSION Prostate MRF measurements of T1 and T2 are repeatable and reproducible between MRI scanners at different centers on different continents for the above measurement ranges.
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Affiliation(s)
- Wei‐Ching Lo
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOhio
- Siemens Medical Solutions IncBostonMassachusetts
| | - Leonardo Kayat Bittencourt
- Department of RadiologyUniversity Hospital and Case Western Reserve UniversityClevelandOhio
- DASA companyRio de JaneiroRJBrazil
| | - Ananya Panda
- Department of RadiologyMayo ClinicRochesterMinnesota
| | - Yun Jiang
- Department of RadiologyUniversity of MichiganAnn ArborMichigan
| | - Junichi Tokuda
- Department of Radiology, Harvard Medical SchoolHarvard UniversityBostonMassachusetts
- Department of RadiologyBrigham and Women's HospitalBostonMassachusetts
| | | | - Clare Tempany‐Afdhal
- Department of Radiology, Harvard Medical SchoolHarvard UniversityBostonMassachusetts
- Department of RadiologyBrigham and Women's HospitalBostonMassachusetts
| | - Verena Obmann
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital BernUniversity of BernBerneSwitzerland
| | | | - Mark Griswold
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOhio
- Department of RadiologyUniversity Hospital and Case Western Reserve UniversityClevelandOhio
| | | | - Vikas Gulani
- Department of RadiologyUniversity of MichiganAnn ArborMichigan
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Chaudhary J, Pandey AK, Sharma PD, Jaleel J, Patel C, Bal CS, Kumar R. Near-lossless compression of PET/CT images using singular value decomposition. Nucl Med Commun 2022; 43:1099-1106. [PMID: 35989610 DOI: 10.1097/mnm.0000000000001603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AIMS AND OBJECTIVES The aim of the study is to compare the single matrix approach and slice-by-slice approach for computing singular value decomposition (SVD) to achieve near-lossless compression of PET/CT images. MATERIALS AND METHODS The parameters used for comparison were SVD computation time, percentage compression and percentage difference between ROI counts on compressed and original images. SVD of 49 F-18-FDG PET/CT studies (33 370 PET/CT images) was computed using both approaches. The smaller singular values contributing insignificant information to the image were truncated, and then, the compressed image was reconstructed. A mask (101 × 101pixels) was used to extract the ROI counts from compressed and original images. Two nuclear medicine physicians compared compressed images with their corresponding original images for loss of clinical details and the presence of generated artifacts. Structural Similarity Index Measure, blur, brightness, contrast per pixel and global contrast factor were used for objective assessment of image quality. Wilcoxon test was applied to find a statistically significant difference between the parameters used for comparison at alpha = 0.05. RESULTS Nuclear medicine physicians found compressed image identical to the corresponding original image. The values of comparation parameters were significantly larger for the single matrix approach in comparison with the slice-by-slice approach. The maximum percentage error between the compressed image and original image was less than 5%. CONCLUSIONS Up to 64 % and 44% near-lossless compression of PET and CT images were achieved, respectively using the slice-by-slice approach, and up to 58 and 53% near-lossless compression of PET and CT images were achieved respectively using the single matrix approach.
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Affiliation(s)
- Jagrati Chaudhary
- Department of Nuclear Medicine, All India Institute of Medical Sciences
| | - Anil Kumar Pandey
- Department of Nuclear Medicine, All India Institute of Medical Sciences
| | - Param D Sharma
- Department of Computer Science, SGTB Khalsa College, University of Delhi, India
| | - Jasim Jaleel
- Department of Nuclear Medicine, All India Institute of Medical Sciences
| | - Chetan Patel
- Department of Nuclear Medicine, All India Institute of Medical Sciences
| | | | - Rakesh Kumar
- Department of Nuclear Medicine, All India Institute of Medical Sciences
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Velasco C, Fletcher TJ, Botnar RM, Prieto C. Artificial intelligence in cardiac magnetic resonance fingerprinting. Front Cardiovasc Med 2022; 9:1009131. [PMID: 36204566 PMCID: PMC9530662 DOI: 10.3389/fcvm.2022.1009131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T1 and T2 mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.
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Affiliation(s)
- Carlos Velasco
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- *Correspondence: Carlos Velasco
| | - Thomas J. Fletcher
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - René M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile
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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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Hamilton JI. A Self-Supervised Deep Learning Reconstruction for Shortening the Breathhold and Acquisition Window in Cardiac Magnetic Resonance Fingerprinting. Front Cardiovasc Med 2022; 9:928546. [PMID: 35811730 PMCID: PMC9260051 DOI: 10.3389/fcvm.2022.928546] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/06/2022] [Indexed: 01/14/2023] Open
Abstract
The aim of this study is to shorten the breathhold and diastolic acquisition window in cardiac magnetic resonance fingerprinting (MRF) for simultaneous T1, T2, and proton spin density (M0) mapping to improve scan efficiency and reduce motion artifacts. To this end, a novel reconstruction was developed that combines low-rank subspace modeling with a deep image prior, termed DIP-MRF. A system of neural networks is used to generate spatial basis images and quantitative tissue property maps, with training performed using only the undersampled k-space measurements from the current scan. This approach avoids difficulties with obtaining in vivo MRF training data, as training is performed de novo for each acquisition. Calculation of the forward model during training is accelerated by using GRAPPA operator gridding to shift spiral k-space data to Cartesian grid points, and by using a neural network to rapidly generate fingerprints in place of a Bloch equation simulation. DIP-MRF was evaluated in simulations and at 1.5 T in a standardized phantom, 18 healthy subjects, and 10 patients with suspected cardiomyopathy. In addition to conventional mapping, two cardiac MRF sequences were acquired, one with a 15-heartbeat(HB) breathhold and 254 ms acquisition window, and one with a 5HB breathhold and 150 ms acquisition window. In simulations, DIP-MRF yielded decreased nRMSE compared to dictionary matching and a sparse and locally low rank (SLLR-MRF) reconstruction. Strong correlation (R2 > 0.999) with T1 and T2 reference values was observed in the phantom using the 5HB/150 ms scan with DIP-MRF. DIP-MRF provided better suppression of noise and aliasing artifacts in vivo, especially for the 5HB/150 ms scan, and lower intersubject and intrasubject variability compared to dictionary matching and SLLR-MRF. Furthermore, it yielded a better agreement between myocardial T1 and T2 from 15HB/254 ms and 5HB/150 ms MRF scans, with a bias of −9 ms for T1 and 2 ms for T2. In summary, this study introduces an extension of the deep image prior framework for cardiac MRF tissue property mapping, which does not require pre-training with in vivo scans, and has the potential to reduce motion artifacts by enabling a shortened breathhold and acquisition window.
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Affiliation(s)
- Jesse I. Hamilton
- Department of Radiology, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- *Correspondence: Jesse I. Hamilton,
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Sharafi A, Zibetti MVW, Chang G, Cloos MA, Regatte RR. Simultaneous bilateral T 1 , T 2 , and T 1ρ relaxation mapping of the hip joint with magnetic resonance fingerprinting. NMR IN BIOMEDICINE 2022; 35:e4651. [PMID: 34825750 PMCID: PMC9233946 DOI: 10.1002/nbm.4651] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 06/13/2023]
Abstract
Quantitative MRI can detect early biochemical changes in cartilage, but its bilateral use in clinical routines is challenging. The aim of this prospective study was to demonstrate the feasibility of magnetic resonance fingerprinting for bilateral simultaneous T1 , T2 , and T1ρ mapping of the hip joint. The study population consisted of six healthy volunteers with no known trauma or pain in the hip. Monoexponential T1 , T2 , and T1ρ relaxation components were assessed in femoral lateral, superolateral, and superomedial, and inferior, as well as acetabular, superolateral, and superomedial subregions in left and right hip cartilage. Aligned ranked nonparametric factorial analysis was used to assess the side's impact on the subregions. Kruskal-Wallis and Wilcoxon tests were used to compare subregions, and coefficient of variation to assess repeatability. Global averages of T1 (676.0 ± 45.4 and 687.6 ± 44.5 ms), T2 (22.5 ± 2.6 and 22.1 ± 2.5 ms), and T1ρ (38.2 ± 5.5 and 38.2 ± 5.5 ms) were measured in the left and right hip, and articular cartilage, respectively. The Kruskal-Wallis test showed a significant difference between different subregions' relaxation times regardless of the hip side (p < 0.001 for T1 , p = 0.012 for T2 , and p < 0.001 for T1ρ ). The Wilcoxon test showed that T1 of femoral layers was significantly (p < 0.003) higher than that for acetabular cartilage. The experiments showed excellent repeatability with CVrms of 1%, 2%, and 4% for T1 , T2 , and T1ρ, respectively. It was concluded that bilateral T1 , T2 , and T1ρ relaxation times, as well as B1+ maps, can be acquired simultaneously from hip joints using the proposed MRF sequence.
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Affiliation(s)
- Azadeh Sharafi
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Marcelo V. W. Zibetti
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Gregory Chang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Martijn A. Cloos
- Center of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ravinder R. Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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Hu Y, Li P, Chen H, Zou L, Wang H. High-Quality MR Fingerprinting Reconstruction Using Structured Low-Rank Matrix Completion and Subspace Projection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1150-1164. [PMID: 34871169 DOI: 10.1109/tmi.2021.3133329] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Due to the capability of fast multiparametric quantitative imaging, magnetic resonance fingerprinting (MRF) is becoming a promising quantitative magnetic resonance imaging approach. However, the artifacts caused by the highly undersampled data acquisition lead to inaccurate estimation of the tissue parameter maps. Based on the assumption that the 3-D MRF data can be modeled as a piecewise smooth signal, with the discontinuities localized to the zero sets of a bandlimited function, we exploit the low-rank property of the structured Toeplitz matrix constructed from the Fourier measurements. In addition, we adopt the subspace projection scheme to improve the accuracy of parameter estimation. In order to efficiently solve the regularized problem, we propose an iterative two-stage algorithm, which alternately updates the k -space data and projects the space-time matrix into the dictionary space. Numerical experiments demonstrate that the proposed algorithm shows significant improvement in MRF time-series images reconstruction and can provide more accurate parameter maps over the state-of-the-art algorithms.
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50
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Cao P, Wang Z, Liu C, Li T, Hui E, Cai J. Motion-resolved and free-breathing liver MRF. Magn Reson Imaging 2022; 91:69-80. [DOI: 10.1016/j.mri.2022.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 05/01/2022] [Accepted: 05/22/2022] [Indexed: 11/28/2022]
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