1
|
Bhattacharya S, Price AN, Uus A, Sousa HS, Marenzana M, Colford K, Murkin P, Lee M, Cordero-Grande L, Teixeira RPAG, Malik SJ, Deprez M. In vivo T2 measurements of the fetal brain using single-shot fast spin echo sequences. Magn Reson Med 2024; 92:715-729. [PMID: 38623934 DOI: 10.1002/mrm.30094] [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: 10/26/2023] [Revised: 02/18/2024] [Accepted: 03/08/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE We propose a quantitative framework for motion-corrected T2 fetal brain measurements in vivo and validate the single-shot fast spin echo (SS-FSE) sequence to perform these measurements. METHODS Stacks of two-dimensional SS-FSE slices are acquired with different echo times (TE) and motion-corrected with slice-to-volume reconstruction (SVR). The quantitative T2 maps are obtained by a fit to a dictionary of simulated signals. The sequence is selected using simulated experiments on a numerical phantom and validated on a physical phantom scanned on a 1.5T system. In vivo quantitative T2 maps are obtained for five fetuses with gestational ages (GA) 21-35 weeks on the same 1.5T system. RESULTS The simulated experiments suggested that a TE of 400 ms combined with the clinically utilized TEs of 80 and 180 ms were most suitable for T2 measurements in the fetal brain. The validation on the physical phantom confirmed that the SS-FSE T2 measurements match the gold standard multi-echo spin echo measurements. We measured average T2s of around 200 and 280 ms in the fetal brain grey and white matter, respectively. This was slightly higher than fetal T2* and the neonatal T2 obtained from previous studies. CONCLUSION The motion-corrected SS-FSE acquisitions with varying TEs offer a promising practical framework for quantitative T2 measurements of the moving fetus.
Collapse
Affiliation(s)
- Suryava Bhattacharya
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Anthony N Price
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Alena Uus
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Helena S Sousa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | - Kathleen Colford
- Centre for the Developing Brain, King's College London, London, UK
| | - Peter Murkin
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maggie Lee
- Guy's and St Thomas' NHS Foundation Trust, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Biomedical Image Technologies, ETSI Telecomunicración, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Rui Pedro A G Teixeira
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Shaihan J Malik
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maria Deprez
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| |
Collapse
|
2
|
Casella C, Vecchiato K, Cromb D, Guo Y, Winkler AM, Hughes E, Dillon L, Green E, Colford K, Egloff A, Siddiqui A, Price A, Grande LC, Wood TC, Malik S, Teixeira RPA, Carmichael DW, O’Muircheartaigh J. Widespread, depth-dependent cortical microstructure alterations in pediatric focal epilepsy. Epilepsia 2024; 65:739-752. [PMID: 38088235 PMCID: PMC7616339 DOI: 10.1111/epi.17861] [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: 08/18/2023] [Revised: 12/11/2023] [Accepted: 12/11/2023] [Indexed: 12/27/2023]
Abstract
OBJECTIVE Tissue abnormalities in focal epilepsy may extend beyond the presumed focus. The underlying pathophysiology of these broader changes is unclear, and it is not known whether they result from ongoing disease processes or treatment-related side effects, or whether they emerge earlier. Few studies have focused on the period of onset for most focal epilepsies, childhood. Fewer still have utilized quantitative magnetic resonance imaging (MRI), which may provide a more sensitive and interpretable measure of tissue microstructural change. Here, we aimed to determine common spatial modes of changes in cortical architecture in children with heterogeneous drug-resistant focal epilepsy and, secondarily, whether changes were related to disease severity. METHODS To assess cortical microstructure, quantitative T1 and T2 relaxometry (qT1 and qT2) was measured in 43 children with drug-resistant focal epilepsy (age range = 4-18 years) and 46 typically developing children (age range = 2-18 years). We assessed depth-dependent qT1 and qT2 values across the neocortex, as well as their gradient of change across cortical depths. We also determined whether global changes seen in group analyses were driven by focal pathologies in individual patients. Finally, as a proof-of-concept, we trained a classifier using qT1 and qT2 gradient maps from patients with radiologically defined abnormalities (MRI positive) and healthy controls, and tested whether this could classify patients without reported radiological abnormalities (MRI negative). RESULTS We uncovered depth-dependent qT1 and qT2 increases in widespread cortical areas in patients, likely representing microstructural alterations in myelin or gliosis. Changes did not correlate with disease severity measures, suggesting they may represent antecedent neurobiological alterations. Using a classifier trained with MRI-positive patients and controls, sensitivity was 71.4% at 89.4% specificity on held-out MRI-negative patients. SIGNIFICANCE These findings suggest the presence of a potential imaging endophenotype of focal epilepsy, detectable irrespective of radiologically identified abnormalities.
Collapse
Affiliation(s)
- Chiara Casella
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Daniel Cromb
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Yourong Guo
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Anderson M. Winkler
- Department of Human Genetics, University of Texas Rio Grande Valley, Brownsville, Texas, USA
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Louise Dillon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Elaine Green
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Alexia Egloff
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Ata Siddiqui
- Department of Radiology, Guy’s and Saint Thomas’ Hospitals NHS Trust, London, UK
| | - Anthony Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Lucilio Cordero Grande
- Department of Biomedical Engineering, King’s College London, London, UK
- Biomedical Image Technologies, Telecommunication Engineering School (ETSIT), Technical University of Madrid, Bioengineering, Biomaterials and Nanomedicine Networking Biomedical Research Centre, National Institute of Health Carlos III, Madrid, Spain
| | - Tobias C. Wood
- Department of Neuroimaging, King’s College London, London, UK
| | - Shaihan Malik
- Department of Biomedical Engineering, King’s College London, London, UK
| | | | | | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
- Medical Research Council (MRC) Centre for Neurodevelopmental Disorders, London, UK
| |
Collapse
|
3
|
Harms RL, Fritz FJ, Schoenmakers S, Roebroeck A. Fast and robust quantification of uncertainty in non-linear diffusion MRI models. Neuroimage 2024; 285:120496. [PMID: 38101495 DOI: 10.1016/j.neuroimage.2023.120496] [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: 06/23/2023] [Revised: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/17/2023] Open
Abstract
Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.
Collapse
Affiliation(s)
- R L Harms
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands.
| | - F J Fritz
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - S Schoenmakers
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands.
| |
Collapse
|
4
|
Luu HM, Park SH. SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network. Neuroimage 2023; 284:120449. [PMID: 37951485 DOI: 10.1016/j.neuroimage.2023.120449] [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: 04/18/2023] [Revised: 09/21/2023] [Accepted: 11/07/2023] [Indexed: 11/14/2023] Open
Abstract
Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantitative maps (such as T1) for data fitting. A method based on multiple phase-cycled bSSFP was recently proposed to enable high-resolution 3D qMT imaging based on least square fitting without any extra acquisition, and thus has high potential for simplifying the qMT procedure. However, the quantification of qMT parameters with this method was suboptimal, limiting its potential for clinical application despite its simpler protocol and higher spatial resolution. To improve the fitting of qMT data obtained with multiple phase-cycled bSSFP, we propose SIMulation-based Physics-guided Learning of neural network for qMT parameters EXtraction, or SIMPLEX. In contrast to previous deep learning supervised approaches for quantitative MR that require the acquisition of input data and corresponding ground truth for training, we leveraged the MR signal model to generate training samples without expensive data curation. The network was trained exclusively with simulation data by predicting the simulation parameters. The same network was applied directly to in-vivo data without additional training. The approach was verified with both simulation and in-vivo data. SIMPLEX showed a decrease in fitting mean squared error for all simulation data compared to the existing least-square fitting method. The in-vivo experiment revealed that the network performed well with the real in vivo data unseen during training. For all experiments, we observed that SIMPLEX consistently improved the quantification quality of the qMT parameters whilst being more robust to noise compared to the prior technique. The proposed SIMPLEX will expedite the routine clinical application of qMT by providing qMT parameters (exchange rate, pool fraction) as well as T1, T2, and ΔB0 maps simultaneously with high spatial resolution, better reliability, and reduced processing time.
Collapse
Affiliation(s)
- Huan Minh Luu
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Rm 1002, CMS (E16) Building, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea
| | - Sung-Hong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Rm 1002, CMS (E16) Building, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea.
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Zhang X, Duchemin Q, Liu K, Gultekin C, Flassbeck S, Fernandez-Granda C, Assländer J. Cramér-Rao bound-informed training of neural networks for quantitative MRI. Magn Reson Med 2022; 88:436-448. [PMID: 35344614 DOI: 10.1002/mrm.29206] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 01/13/2022] [Accepted: 02/07/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE To improve the performance of neural networks for parameter estimation in quantitative MRI, in particular when the noise propagation varies throughout the space of biophysical parameters. THEORY AND METHODS A theoretically well-founded loss function is proposed that normalizes the squared error of each estimate with respective Cramér-Rao bound (CRB)-a theoretical lower bound for the variance of an unbiased estimator. This avoids a dominance of hard-to-estimate parameters and areas in parameter space, which are often of little interest. The normalization with corresponding CRB balances the large errors of fundamentally more noisy estimates and the small errors of fundamentally less noisy estimates, allowing the network to better learn to estimate the latter. Further, proposed loss function provides an absolute evaluation metric for performance: A network has an average loss of 1 if it is a maximally efficient unbiased estimator, which can be considered the ideal performance. The performance gain with proposed loss function is demonstrated at the example of an eight-parameter magnetization transfer model that is fitted to phantom and in vivo data. RESULTS Networks trained with proposed loss function perform close to optimal, that is, their loss converges to approximately 1, and their performance is superior to networks trained with the standard mean-squared error (MSE). The proposed loss function reduces the bias of the estimates compared to the MSE loss, and improves the match of the noise variance to the CRB. This performance gain translates to in vivo maps that align better with the literature. CONCLUSION Normalizing the squared error with the CRB during the training of neural networks improves their performance in estimating biophysical parameters.
Collapse
Affiliation(s)
- Xiaoxia Zhang
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York City, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York City, New York, USA
| | - Quentin Duchemin
- LAMA, Univ Gustave Eiffel, Univ Paris Est Creteil, Marne-la-Vallée, France
| | - Kangning Liu
- Center for Data Science, New York University Grossman School of Medicine, New York City, New York, USA
| | - Cem Gultekin
- Courant Institute of Mathematical Sciences, New York University, New York City, New York, USA
| | - Sebastian Flassbeck
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York City, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York City, New York, USA
| | - Carlos Fernandez-Granda
- Center for Data Science, New York University Grossman School of Medicine, New York City, New York, USA.,Courant Institute of Mathematical Sciences, New York University, New York City, New York, USA
| | - Jakob Assländer
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York City, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York City, New York, USA
| |
Collapse
|
7
|
Zibetti MVW, Sharafi A, Regatte RR. Optimization of spin-lock times in T 1ρ mapping of knee cartilage: Cramér-Rao bounds versus matched sampling-fitting. Magn Reson Med 2022; 87:1418-1434. [PMID: 34738252 PMCID: PMC8822470 DOI: 10.1002/mrm.29063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 10/12/2021] [Accepted: 10/13/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To compare different optimization approaches for choosing the spin-lock times (TSLs), in spin-lattice relaxation time in the rotating frame (T1ρ ) mapping. METHODS Optimization criteria for TSLs based on Cramér-Rao lower bounds (CRLB) are compared with matched sampling-fitting (MSF) approaches for T1ρ mapping on synthetic data, model phantoms, and knee cartilage. The MSF approaches are optimized using robust methods for noisy cost functions. The MSF approaches assume that optimal TSLs depend on the chosen fitting method. An iterative non-linear least squares (NLS) and artificial neural networks (ANN) are tested as two possible T1ρ fitting methods for MSF approaches. RESULTS All optimized criteria were better than non-optimized ones. However, we observe that a modified CRLB and an MSF based on the mean of the normalized absolute error (MNAE) were more robust optimization approaches, performing well in all tested cases. The optimized TSLs obtained the best performance with synthetic data (3.5-8.0% error), model phantoms (1.5-2.8% error), and healthy volunteers (7.7-21.1% error), showing stable and improved quality results, comparing to non-optimized approaches (4.2-13.3% error on synthetic data, 2.1-6.2% error on model phantoms, 9.8-27.8% error on healthy volunteers). CONCLUSION A modified CRLB and the MSF based on MNAE are robust optimization approaches for choosing TSLs in T1ρ mapping. All optimized criteria allowed good results even using rapid scans with two TSLs when a complex-valued fitting is done with iterative NLS or ANN.
Collapse
Affiliation(s)
- Marcelo V W Zibetti
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Azadeh Sharafi
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Ravinder R Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| |
Collapse
|
8
|
West DJ, Cruz G, Teixeira RPAG, Schneider T, Tournier JD, Hajnal JV, Prieto C, Malik SJ. An MR fingerprinting approach for quantitative inhomogeneous magnetization transfer imaging. Magn Reson Med 2022; 87:220-235. [PMID: 34418151 PMCID: PMC7614010 DOI: 10.1002/mrm.28984] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/15/2021] [Accepted: 08/05/2021] [Indexed: 01/07/2023]
Abstract
PURPOSE Magnetization transfer (MT) and inhomogeneous MT (ihMT) contrasts are used in MRI to provide information about macromolecular tissue content. In particular, MT is sensitive to macromolecules, and ihMT appears to be specific to myelinated tissue. This study proposes a technique to characterize MT and ihMT properties from a single acquisition, producing both semiquantitative contrast ratios and quantitative parameter maps. THEORY AND METHODS Building on previous work that uses multiband RF pulses to efficiently generate ihMT contrast, we propose a cyclic steady-state approach that cycles between multiband and single-band pulses to boost the achieved contrast. Resultant time-variable signals are reminiscent of an MR fingerprinting acquisition, except that the signal fluctuations are entirely mediated by MT effects. A dictionary-based low-rank inversion method is used to reconstruct the resulting images and to produce both semiquantitative MT ratio and ihMT ratio maps, as well as quantitative parameter estimates corresponding to an ihMT tissue model. RESULTS Phantom and in vivo brain data acquired at 1.5 Tesla demonstrate the expected contrast trends, with ihMT ratio maps showing contrast more specific to white matter, as has been reported by others. Quantitative estimation of semisolid fraction and dipolar T1 was also possible and yielded measurements consistent with literature values in the brain. CONCLUSION By cycling between multiband and single-band pulses, an entirely MT-mediated fingerprinting method was demonstrated. This proof-of-concept approach can be used to generate semiquantitative maps and quantitatively estimate some macromolecular-specific tissue parameters.
Collapse
Affiliation(s)
- Daniel J. West
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Gastao Cruz
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Rui P. A. G. Teixeira
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | | | - Jacques-Donald Tournier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Joseph V. Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Claudia Prieto
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Shaihan J. Malik
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| |
Collapse
|
9
|
Using quantitative MRI to study brain responses to immune challenge with interferon-α. Brain Behav Immun Health 2021; 18:100376. [PMID: 34746879 PMCID: PMC8554453 DOI: 10.1016/j.bbih.2021.100376] [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: 09/30/2021] [Accepted: 10/18/2021] [Indexed: 11/20/2022] Open
Abstract
Inflammatory processes in the Central Nervous System (CNS) have been proposed to mediate the association between peripheral inflammation and the development of psychiatric disorders, but we currently lack sensitive measures of CNS inflammation for human studies in vivo. Here we used quantitative MRI (qMRI) to explore the in vivo central response to a peripheral immune challenge in healthy humans, and we assessed whether changes in quantitative relaxometry MRI parameters were associated with changes in peripheral inflammation. Quantitative relaxation times (T1 & T2) and Proton Density (PD) were measured in n = 6 healthy males (mean age = 30.5 ± 6.8 years) in two MRI assessments collected before and 24 hours after a subcutaneous injection of the proinflammatory cytokine and immune activator, interferon-alpha (IFN-α). Serum levels of immune markers and markers of blood-brain barrier integrity (S100B) were also measured before and after the injection. Region of interest and histogram-based analyses (optimized for the small sample size) showed a statistically significant increase of both T1 (t(5) = 3.78, p = 0.013) and PD (t(5) = 2.91, p = 0.033) parameters in the bilateral hippocampus after IFN-α administration. T1 peak values in bilateral hippocampus were positively correlated with serum Tumour Necrosis Factor-alpha levels at 24 h after the injection, when this cytokine peaked (Spearman's rho = 0.67, p = 0.018) and negatively correlated with S100B levels (Spearman's rho = -0.826, p = 0.001). Our data suggest that peripheral administration of IFN-α produces acute changes in brain qMRI which might indicate a brain immune response. This is supported by the association of such changes with low-grade peripheral inflammation.
Collapse
|
10
|
Leitão D, Teixeira RPAG, Price A, Uus A, Hajnal JV, Malik SJ. Efficiency analysis for quantitative MRI of T1 and T2 relaxometry methods. Phys Med Biol 2021; 66:15NT02. [PMID: 34192676 PMCID: PMC8312556 DOI: 10.1088/1361-6560/ac101f] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/12/2021] [Accepted: 06/30/2021] [Indexed: 11/11/2022]
Abstract
This study presents a comparison of quantitative MRI methods based on an efficiency metric that quantifies their intrinsic ability to extract information about tissue parameters. Under a regime of unbiased parameter estimates, an intrinsic efficiency metricηwas derived for fully-sampled experiments which can be used to both optimize and compare sequences. Here we optimize and compare several steady-state and transient gradient-echo based qMRI methods, such as magnetic resonance fingerprinting (MRF), for jointT1andT2mapping. The impact of undersampling was also evaluated, assuming incoherent aliasing that is treated as noise by parameter estimation.In vivovalidation of the efficiency metric was also performed. Transient methods such as MRF can be up to 3.5 times more efficient than steady-state methods, when spatial undersampling is ignored. If incoherent aliasing is treated as noise during least-squares parameter estimation, the efficiency is reduced in proportion to the SNR of the data, with reduction factors of 5 often seen for practical SNR levels.In vivovalidation showed a very good agreement between the theoretical and experimentally predicted efficiency. This work presents and validates an efficiency metric to optimize and compare the performance of qMRI methods. Transient methods were found to be intrinsically more efficient than steady-state methods, however the effect of spatial undersampling can significantly erode this advantage.
Collapse
Affiliation(s)
- David Leitão
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Communication Address: Perinatal Imaging and Health 1st Floor South Wing, St Thomas’ Hospital London SE1 7EHUK, United Kingdom
| | - Rui Pedro A. G. Teixeira
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Anthony Price
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Alena Uus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joseph V. Hajnal
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Shaihan J. Malik
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Centre for the Developing Brain, King’s College London, London, United Kingdom
| |
Collapse
|
11
|
van Riel MHC, Yu Z, Hodono S, Xia D, Chandarana H, Fujimoto K, Cloos MA. Free-breathing abdominal T 1 mapping using an optimized MR fingerprinting sequence. NMR IN BIOMEDICINE 2021; 34:e4531. [PMID: 33902155 PMCID: PMC8218311 DOI: 10.1002/nbm.4531] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 04/03/2021] [Accepted: 04/05/2021] [Indexed: 05/31/2023]
Abstract
In this work, we propose a free-breathing magnetic resonance fingerprinting (MRF) method that can be used to obtain B1+ -robust quantitative T1 maps of the abdomen in a clinically acceptable time. A three-dimensional MRF sequence with a radial stack-of-stars trajectory was implemented, and its k-space acquisition ordering was adjusted to improve motion-robustness in the context of MRF. The flip angle pattern was optimized using the Cramér-Rao Lower Bound, and the encoding efficiency of sequences with 300, 600, 900 and 1800 flip angles was evaluated. To validate the sequence, a movable multicompartment phantom was developed. Reference multiparametric maps were acquired under stationary conditions using a previously validated MRF method. Periodic motion of the phantom was used to investigate the motion-robustness of the proposed sequence. The best performing sequence length (600 flip angles) was used to image the abdomen during a free-breathing volunteer scan. When using a series of 600 or more flip angles, the estimated T1 values in the stationary phantom showed good agreement with the reference scan. Phantom experiments revealed that motion-related artifacts can appear in the quantitative maps and confirmed that a motion-robust k-space ordering is essential. The in vivo scan demonstrated that the proposed sequence can produce clean parameter maps while the subject breathes freely. Using this sequence, it is possible to generate B1+ -robust quantitative maps of T1 and B1+ next to M0 -weighted images under free-breathing conditions at a clinically usable resolution within 5 min.
Collapse
Affiliation(s)
- Max H. C. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Zidan Yu
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
| | - Shota Hodono
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
- Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia
| | - Ding Xia
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Hersh Chandarana
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Koji Fujimoto
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Real World Data Research and Development, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Martijn A. Cloos
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, USA
- Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York, USA
- Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia
| |
Collapse
|
12
|
Loktyushin A, Herz K, Dang N, Glang F, Deshmane A, Weinmüller S, Doerfler A, Schölkopf B, Scheffler K, Zaiss M. MRzero - Automated discovery of MRI sequences using supervised learning. Magn Reson Med 2021; 86:709-724. [PMID: 33755247 DOI: 10.1002/mrm.28727] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 01/15/2021] [Accepted: 01/20/2021] [Indexed: 12/30/2022]
Abstract
PURPOSE A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with a flexible, task-driven cost function this allows for an efficient exploration of novel MR sequence strategies. METHODS The scanning and reconstruction process is simulated end-to-end in terms of RF events, gradient moment events in x and y, and delay times, acting on the input model spin system given in terms of proton density, T 1 and T 2 , and Δ B 0 . As a proof of concept, we use both conventional MR images and T 1 maps as targets and optimize from scratch using the loss defined by data fidelity, SAR penalty, and scan time. RESULTS In a first attempt, MRzero learns gradient and RF events from zero, and is able to generate a target image produced by a conventional gradient echo sequence. Using a neural network within the reconstruction module allows arbitrary targets to be learned successfully. Experiments could be translated to image acquisition at the real system (3T Siemens, PRISMA) and could be verified in the measurements of phantoms and a human brain in vivo. CONCLUSIONS Automated MR sequence generation is possible based on differentiable Bloch equation simulations and a supervised learning approach.
Collapse
Affiliation(s)
- A Loktyushin
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
- Empirical Inference, Max-Planck Institute for Intelligent Systems, Tübingen, Germany
| | - K Herz
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
- University of Tübingen, Tübingen, Germany
| | - N Dang
- Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Neuroradiology, University Clinic Erlangen, Erlangen, Germany
| | - F Glang
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - A Deshmane
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - S Weinmüller
- Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Neuroradiology, University Clinic Erlangen, Erlangen, Germany
| | - A Doerfler
- Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Neuroradiology, University Clinic Erlangen, Erlangen, Germany
| | - B Schölkopf
- Empirical Inference, Max-Planck Institute for Intelligent Systems, Tübingen, Germany
| | - K Scheffler
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
- University of Tübingen, Tübingen, Germany
| | - M Zaiss
- Magnetic Resonance Center, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany
- Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Neuroradiology, University Clinic Erlangen, Erlangen, Germany
| |
Collapse
|
13
|
Kim D, Wisnowski JL, Nguyen CT, Haldar JP. Multidimensional correlation spectroscopic imaging of exponential decays: From theoretical principles to in vivo human applications. NMR IN BIOMEDICINE 2020; 33:e4244. [PMID: 31909534 PMCID: PMC7338241 DOI: 10.1002/nbm.4244] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 10/09/2019] [Accepted: 11/27/2019] [Indexed: 05/02/2023]
Abstract
Multiexponential modeling of relaxation or diffusion MR signal decays is a popular approach for estimating and spatially mapping different microstructural tissue compartments. While this approach can be quite powerful, it is also limited by the fact that one-dimensional multiexponential modeling is an ill-posed inverse problem with substantial ambiguities. In this article, we present an overview of a recent multidimensional correlation spectroscopic imaging approach to this problem. This approach helps to alleviate ill-posedness by making advantageous use of multidimensional contrast encoding (e.g., 2D diffusion-relaxation encoding or 2D relaxation-relaxation encoding) combined with a regularized spatial-spectral estimation procedure. Theoretical calculations, simulations, and experimental results are used to illustrate the benefits of this approach relative to classical methods. In addition, we demonstrate an initial proof-of-principle application of this kind of approach to in vivo human MRI experiments.
Collapse
Affiliation(s)
- Daeun Kim
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, CA, USA
- Signal and Image Processing Institute, University of Southern California, CA, USA
- Correspondence Daeun Kim,
| | - Jessica L. Wisnowski
- Radiology, Children’s Hospital Los Angeles, CA, USA
- Pediatrics, Children’s Hospital Los Angeles, CA, USA
| | - Christopher T. Nguyen
- Harvard Medical School and Cardiovascular Research Center, Massachusetts General Hospital, MA, USA
- Martinos Center for Biomedical Imaging, Radiology, Massachusetts General Hospital, MA, USA
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, CA, USA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, CA, USA
- Signal and Image Processing Institute, University of Southern California, CA, USA
| |
Collapse
|
14
|
van der Heide O, Sbrizzi A, Luijten PR, van den Berg CA. High-resolution in vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm. NMR IN BIOMEDICINE 2020; 33:e4251. [PMID: 31985134 PMCID: PMC7079175 DOI: 10.1002/nbm.4251] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 11/12/2019] [Accepted: 12/05/2019] [Indexed: 05/25/2023]
Abstract
MR-STAT is a recently proposed framework that allows the reconstruction of multiple quantitative parameter maps from a single short scan by performing spatial localisation and parameter estimation on the time-domain data simultaneously, without relying on the fast Fourier transform (FFT). To do this at high resolution, specialized algorithms are required to solve the underlying large-scale nonlinear optimisation problem. We propose a matrix-free and parallelized inexact Gauss-Newton based reconstruction algorithm for this purpose. The proposed algorithm is implemented on a high-performance computing cluster and is demonstrated to be able to generate high-resolution (1 mm × 1 mm in-plane resolution) quantitative parameter maps in simulation, phantom, and in vivo brain experiments. Reconstructed T1 and T2 values for the gel phantoms are in agreement with results from gold standard measurements and, for the in vivo experiments, the quantitative values show good agreement with literature values. In all experiments, short pulse sequences with robust Cartesian sampling are used, for which MR fingerprinting reconstructions are shown to fail.
Collapse
Affiliation(s)
- Oscar van der Heide
- Center for Image SciencesUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Alessandro Sbrizzi
- Center for Image SciencesUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Peter R. Luijten
- Center for Image SciencesUniversity Medical Center UtrechtUtrechtthe Netherlands
| | | |
Collapse
|
15
|
McGivney DF, Boyacioğlu R, Jiang Y, Poorman ME, Seiberlich N, Gulani V, Keenan KE, Griswold MA, Ma D. Magnetic resonance fingerprinting review part 2: Technique and directions. J Magn Reson Imaging 2020; 51:993-1007. [PMID: 31347226 PMCID: PMC6980890 DOI: 10.1002/jmri.26877] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/05/2019] [Accepted: 07/05/2019] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR-sensitive tissue properties with a single acquisition. There have been numerous advances in MRF in the years since its inception. In this work we highlight some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:993-1007.
Collapse
Affiliation(s)
- Debra F. McGivney
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Rasim Boyacioğlu
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yun Jiang
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Megan E. Poorman
- Department of Physics, University of Colorado Boulder, Boulder, Colorado, USA
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Vikas Gulani
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA
| |
Collapse
|
16
|
Lu L, Chen Y, Shen C, Lian J, Das S, Marks L, Lin W, Zhu T. Initial assessment of 3D magnetic resonance fingerprinting (MRF) towards quantitative brain imaging for radiation therapy. Med Phys 2019; 47:1199-1214. [PMID: 31834641 DOI: 10.1002/mp.13967] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 12/02/2019] [Accepted: 12/06/2019] [Indexed: 12/17/2022] Open
Abstract
PURPOSE Magnetic resonance fingerprinting (MRF) provides quantitative T1/T2 maps, enabling applications in clinical radiotherapy such as large-scale, multi-center clinical trials for longitudinal assessment of therapy response. We evaluated the feasibility of a quantitative three-dimensional-MRF (3D-MRF) towards its radiotherapy applications of primary brain tumors. METHODS A fast whole-brain 3D-MRF sequence initially developed for diagnostic radiology was optimized using flexible body coils, which is the typical MR imaging setup for radiotherapy treatment planning and for MR imaging (MRI)-guided treatment delivery. Optimization criteria included the accuracy and the precision of T1/T2 quantifications of polyvinylpyrrolidone (PVP) solutions, compared to those from the 3D-MRF using a 32-channel head coil. The accuracy of T1/T2 quantifications from the optimized MRF was first examined in healthy volunteers with two different coil setups. The intra- and inter-scanner variations of image intensity from the optimized sequence were quantified by longitudinal scans of the PVP solutions on two 3T scanners. Using a 3D-printed MRI geometry phantom, susceptibility-induced distortion with the optimized 3D-MRF was quantified as the Dice coefficient of phantom contours, compared to those from CT images. By introducing intentional head motion during 10% of the scan, the robustness of the optimized 3D-MRF towards motion was evaluated through visual inspection of motion artifacts and through quantitative analysis of image sharpness in brain MRF maps. RESULTS The optimized sequence acquired whole-brain T1, T2 and proton density maps and with a resolution of 1.2 × 1.2 × 3 mm3 in 10 min, similar to the total acquisition time of 3D T1- and T2-weighted images of the same resolution. In vivo T1 and T2 values of the white and gray matter were consistent with literature. The intra- and inter-scanner variability of the intensity-normalized MRF T1 was 1.0% ± 0.7% and 2.3% ± 1.0% respectively, in contrast to 5.3% ± 3.8% and 3.2% ± 1.6% from the normalized T1-weighted MRI. Repeatability and reproducibility of MRF T1 were independent of intensity normalization. Both phantom and human data demonstrated that the optimized 3D-MRF is more robust to subject motion and artifacts from subject-specific susceptibility difference. Compared to CT contours, the Dice coefficient of phantom contours from 3D-MRF was 0.93, improved from 0.87 from the T1-weighted MRI. CONCLUSION Compared to conventional MRI, the optimized 3D-MRF demonstrated improved repeatability across time points and reproducibility across scanners for better tissue quantification, as well as improved robustness to subject-specific susceptibility and motion artifacts under a typical MR imaging setup for radiotherapy. More importantly, quantitative MRF T1/T2 measurements lead to promising potentials towards longitudinal quantitative assessment of treatment response for better adaptive therapy and for large-scale, multi-center clinical trials.
Collapse
Affiliation(s)
- Lan Lu
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yong Chen
- Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Colette Shen
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jun Lian
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Shiva Das
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lawrence Marks
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tong Zhu
- Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|
17
|
A G Teixeira RP, Neji R, Wood TC, Baburamani AA, Malik SJ, Hajnal JV. Controlled saturation magnetization transfer for reproducible multivendor variable flip angle T 1 and T 2 mapping. Magn Reson Med 2019; 84:221-236. [PMID: 31846122 PMCID: PMC7154666 DOI: 10.1002/mrm.28109] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 10/15/2019] [Accepted: 11/14/2019] [Indexed: 02/05/2023]
Abstract
Purpose The widespread clinical application of quantitative MRI has been hindered by a lack of reproducibility across sites and vendors. Previous work has attributed this to incorrect B1 mapping or insufficient spoiling conditions. We recently proposed the controlled saturation magnetization transfer (CSMT) framework and hypothesized that the lack of reproducibility can also be attributed to magnetization transfer effects. This work seeks to validate this hypothesis and demonstrate that reproducible multivendor single‐pool relaxometry can be achieved with the CSMT approach. Methods Three healthy volunteers were scanned on scanners from 3 vendors (GE Healthcare, Philips, Siemens). An extensive set of images necessary for joint T1 and T2 estimation were acquired with (1) each vendor default RF pulses and spoiling conditions; (2) harmonized RF spoiling; and (3) harmonized RF spoiling and CSMT pulses. Different subsets of images were used to generate 6 different T1 and T2 maps for each subject’s data from each vendor. Cross‐protocol, cross‐vendor, and test/retest variability were estimated. Results Harmonized RF spoiling conditions are insufficient to ensure good cross‐vendor reproducibility. Controlled saturation magnetization transfer allows cross‐protocol variability to be reduced from 18.3% to 4.0%. Whole‐brain variability using the same protocol was reduced from a maximum of 19% to 4.5% across sites. Both CSMT and native vendor RF conditions have a reported variability of less than 5% for repeat measures on the same vendor. Conclusion Magnetization transfer effects are a major contributor to intersite/intrasite variability of T1 and T2 estimation. Controlled saturation magnetization transfer stabilizes these effects, paving the way for the use of single‐pool T1 and T2 as a reliable source for clinical diagnosis across sites.
Collapse
Affiliation(s)
- Rui Pedro A G Teixeira
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Radhouene Neji
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Magnetic Resonance Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Tobias C Wood
- Department of Neuroimaging, King's College London, London, United Kingdom
| | - Ana A Baburamani
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Shaihan J Malik
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| |
Collapse
|
18
|
Bliesener Y, Lingala SG, Haldar JP, Nayak KS. Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects. Magn Reson Med 2019; 83:1625-1639. [PMID: 31605556 PMCID: PMC6982604 DOI: 10.1002/mrm.28024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 09/06/2019] [Accepted: 09/09/2019] [Indexed: 12/12/2022]
Abstract
Purpose To evaluate the impact of (k,t) data sampling on the variance of tracer‐kinetic parameter (TK) estimation in high‐resolution whole‐brain dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI) using digital reference objects. We study this in the context of TK model constraints, and in the absence of other constraints. Methods Three anatomically and physiologically realistic brain‐tumor digital reference objects were generated. Data sampling strategies included uniform and variable density; zone‐based, lattice, pseudo‐random, and pseudo‐radial; with 50‐time frames and 4‐fold to 25‐fold undersampling. In all cases, we assume a fully sampled first time frame, and prior knowledge of the arterial input function. TK parameters were estimated by indirect estimation (i.e., image‐time‐series reconstruction followed by model fitting), and direct estimation from the under‐sampled data. We evaluated methods based on the Cramér‐Rao bound and Monte‐Carlo simulations, over the range of signal‐to‐noise ratio (SNR) seen in clinical brain DCE‐MRI. Results Lattice‐based sampling provided the lowest SDs, followed by pseudo‐random, pseudo‐radial, and zone‐based. This ranking was consistent for the Patlak and extended Tofts model. Pseudo‐random sampling resulted in 19% higher averaged SD compared to lattice‐based sampling. Zone‐based sampling resulted in substantially higher SD at undersampling factors above 10. CRB analysis showed only a small difference between uniform and variable density for both lattice‐based and pseudo‐random sampling up to undersampling factors of 25. Conclusion Lattice sampling provided the lowest SDs, although the differences between sampling schemes were not substantial at low undersampling factors. The differences between lattice‐based and pseudo‐random sampling strategies with both uniform and variable density were within the range of error induced by other sources, at up to 25‐fold undersampling.
Collapse
Affiliation(s)
- Yannick Bliesener
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Sajan G Lingala
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Justin P Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| |
Collapse
|
19
|
Lee PK, Watkins LE, Anderson TI, Buonincontri G, Hargreaves BA. Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations. Magn Reson Med 2019; 82:1438-1451. [PMID: 31131500 PMCID: PMC8057531 DOI: 10.1002/mrm.27832] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 05/06/2019] [Accepted: 05/08/2019] [Indexed: 12/21/2022]
Abstract
PURPOSE To investigate a computationally efficient method for optimizing the Cramér-Rao Lower Bound (CRLB) of quantitative sequences without using approximations or an analytical expression of the signal. METHODS Automatic differentiation was applied to Bloch simulations and used to optimize several quantitative sequences without the need for approximations or an analytical expression. The results were validated with in vivo measurements and comparisons to prior art. Multi-echo spin echo and DESPO T 1 were used as benchmarks to verify the CRLB implementation. The CRLB of the Magnetic Resonance Fingerprinting (MRF) sequence, which has a complicated analytical formulation, was also optimized using automatic differentiation. RESULTS The sequence parameters obtained for multi-echo spin echo and DESPO T 1 matched results obtained using conventional methods. In vivo, MRF scans demonstrate that the CRLB optimization obtained with automatic differentiation can improve performance in presence of white noise. For MRF, the CRLB optimization converges in 1.1 CPU hours for N TR = 400 and has O ( N TR ) asymptotic runtime scaling for the calculation of the CRLB objective and gradient. CONCLUSIONS Automatic differentiation can be used to optimize the CRLB of quantitative sequences without using approximations or analytical expressions. For MRF, the runtime is computationally efficient and can be used to investigate confounding factors as well as MRF sequences with a greater number of repetitions.
Collapse
Affiliation(s)
- Philip K. Lee
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Lauren E. Watkins
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | | | - Guido Buonincontri
- IRCCS Fondazione Stella Maris, Pisa, PI, 56128, Italy
- Fondazione Imago7, Pisa, PI, 56128, Italy
| | - Brian A. Hargreaves
- Radiology, Stanford University, Stanford, CA, 94305, USA
- Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
- Bioengineering, Stanford University, Stanford, CA, 94305, USA
| |
Collapse
|
20
|
Assländer J, Novikov DS, Lattanzi R, Sodickson DK, Cloos MA. Hybrid-state free precession in nuclear magnetic resonance. COMMUNICATIONS PHYSICS 2019; 2:73. [PMID: 31328174 PMCID: PMC6641569 DOI: 10.1038/s42005-019-0174-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 05/30/2019] [Indexed: 05/27/2023]
Abstract
The dynamics of large spin-1/2 ensembles are commonly described by the Bloch equation, which is characterized by the magnetization's non-linear response to the driving magnetic field. Consequently, most magnetic field variations result in non-intuitive spin dynamics, which are sensitive to small calibration errors. Although simplistic field variations result in robust spin dynamics, they do not explore the richness of the system's phase space. Here, we identify adiabaticity conditions that span a large experiment design space with tractable dynamics. All dynamics are trapped in a one-dimensional subspace, namely in the magnetization's absolute value, which is in a transient state, while its direction adiabatically follows the steady state. In this hybrid state, the polar angle is the effective drive of the spin dynamics. As an example, we optimize this drive for robust and efficient quantification of spin relaxation times and utilize it for magnetic resonance imaging of the human brain.
Collapse
Affiliation(s)
- Jakob Assländer
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, NY, USA
| | - Riccardo Lattanzi
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, NY, USA
- The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
| | - Daniel K Sodickson
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, NY, USA
- The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
| | - Martijn A Cloos
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, NY, USA
- The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
| |
Collapse
|
21
|
Assländer J, Lattanzi R, Sodickson DK, Cloos MA. Optimized quantification of spin relaxation times in the hybrid state. Magn Reson Med 2019; 82:1385-1397. [PMID: 31189025 DOI: 10.1002/mrm.27819] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 04/01/2019] [Accepted: 04/29/2019] [Indexed: 01/08/2023]
Abstract
PURPOSE The optimization and analysis of spin ensemble trajectories in the hybrid state-a state in which the direction of the magnetization adiabatically follows the steady state while the magnitude remains in a transient state. METHODS Numerical optimizations were performed to find spin ensemble trajectories that minimize the Cramér-Rao bound for T 1 -encoding, T 2 -encoding, and their weighted sum, respectively, followed by a comparison between the Cramér-Rao bounds obtained with our optimized spin-trajectories, Look-Locker sequences, and multi-spin-echo methods. Finally, we experimentally tested our optimized spin trajectories with in vivo scans of the human brain. RESULTS After a nonrecurring inversion segment on the southern half of the Bloch sphere, all optimized spin trajectories pursue repetitive loops on the northern hemisphere in which the beginning of the first and the end of the last loop deviate from the others. The numerical results obtained in this work align well with intuitive insights gleaned directly from the governing equation. Our results suggest that hybrid-state sequences outperform traditional methods. Moreover, hybrid-state sequences that balance T 1 - and T 2 -encoding still result in near optimal signal-to-noise efficiency for each relaxation time. Thus, the second parameter can be encoded at virtually no extra cost. CONCLUSIONS We provided new insights into the optimal encoding processes of spin relaxation times in order to guide the design of robust and efficient pulse sequences. We found that joint acquisitions of T 1 and T 2 in the hybrid state are substantially more efficient than sequential encoding techniques.
Collapse
Affiliation(s)
- Jakob Assländer
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York
| | - Riccardo Lattanzi
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York.,Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York
| | - Daniel K Sodickson
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York.,Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York
| | - Martijn A Cloos
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.,Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, New York.,Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, New York
| |
Collapse
|
22
|
Gómez PA, Molina-Romero M, Buonincontri G, Menzel MI, Menze BH. Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging. Sci Rep 2019; 9:8468. [PMID: 31186480 PMCID: PMC6560213 DOI: 10.1038/s41598-019-44832-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 05/22/2019] [Indexed: 02/01/2023] Open
Abstract
Magnetic resonance imaging (MRI) has evolved into an outstandingly versatile diagnostic modality, as it has the ability to non-invasively produce detailed information on a tissue's structure and function. Complementary data is normally obtained in separate measurements, either as contrast-weighted images, which are fast and simple to acquire, or as quantitative parametric maps, which offer an absolute quantification of underlying biophysical effects, such as relaxation times or flow. Here, we demonstrate how to acquire and reconstruct data in a transient-state with a dual purpose: 1 - to generate contrast-weighted images that can be adjusted to emphasise clinically relevant image biomarkers; exemplified with signal modulation according to flow to obtain angiography information, and 2 - to simultaneously infer multiple quantitative parameters with a single, highly accelerated acquisition. This is achieved by introducing three novel elements: a model that accounts for flowing blood, a method for sequence design using smooth flip angle excitation patterns that incorporates both parameter encoding and signal contrast, and the reconstruction of temporally resolved contrast-weighted images. From these images we simultaneously obtain angiography projections and multiple quantitative maps. By doing so, we increase the amount of clinically relevant data without adding measurement time, creating new dimensions for biomarker exploration and adding value to MR examinations for patients and clinicians alike.
Collapse
|
23
|
Zhao B, Haldar JP, Liao C, Ma D, Jiang Y, Griswold MA, Setsompop K, Wald LL. Optimal Experiment Design for Magnetic Resonance Fingerprinting: Cramér-Rao Bound Meets Spin Dynamics. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:844-861. [PMID: 30295618 PMCID: PMC6447464 DOI: 10.1109/tmi.2018.2873704] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Magnetic resonance (MR) fingerprinting is a new quantitative imaging paradigm, which simultaneously acquires multiple MR tissue parameter maps in a single experiment. In this paper, we present an estimation-theoretic framework to perform experiment design for MR fingerprinting. Specifically, we describe a discrete-time dynamic system to model spin dynamics, and derive an estimation-theoretic bound, i.e., the Cramér-Rao bound, to characterize the signal-to-noise ratio (SNR) efficiency of an MR fingerprinting experiment. We then formulate an optimal experiment design problem, which determines a sequence of acquisition parameters to encode MR tissue parameters with the maximal SNR efficiency, while respecting the physical constraints and other constraints from the image decoding/reconstruction process. We evaluate the performance of the proposed approach with numerical simulations, phantom experiments, and in vivo experiments. We demonstrate that the optimized experiments substantially reduce data acquisition time and/or improve parameter estimation. For example, the optimized experiments achieve about a factor of two improvement in the accuracy of T2 maps, while keeping similar or slightly better accuracy of T1 maps. Finally, as a remarkable observation, we find that the sequence of optimized acquisition parameters appears to be highly structured rather than randomly/pseudo-randomly varying as is prescribed in the conventional MR fingerprinting experiments.
Collapse
Affiliation(s)
- Bo Zhao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129 USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115 USA
| | - Justin P. Haldar
- Signal and Image Processing Institute and Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129 USA
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang Province 310027 China
| | - Dan Ma
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Yun Jiang
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Mark A. Griswold
- Department of Radiology, Case Western Reserve University, Cleveland, OH 44106 USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129 USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115 USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Lawrence L. Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129 USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115 USA, and also with the Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| |
Collapse
|
24
|
Ramos-Llorden G, Vegas-Sanchez-Ferrero G, Bjork M, Vanhevel F, Parizel PM, San Jose Estepar R, den Dekker AJ, Sijbers J. NOVIFAST: A Fast Algorithm for Accurate and Precise VFA MRI Mapping. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2414-2427. [PMID: 29993537 PMCID: PMC6277233 DOI: 10.1109/tmi.2018.2833288] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
In quantitative magnetic resonance mapping, the variable flip angle (VFA) steady state spoiled gradient recalled echo (SPGR) imaging technique is popular as it provides a series of high resolution weighted images in a clinically feasible time. Fast, linear methods that estimate maps from these weighted images have been proposed, such as DESPOT1 and iterative re-weighted linear least squares. More accurate, non-linear least squares (NLLS) estimators are in play, but these are generally much slower and require careful initialization. In this paper, we present NOVIFAST, a novel NLLS-based algorithm specifically tailored to VFA SPGR mapping. By exploiting the particular structure of the SPGR model, a computationally efficient, yet accurate and precise map estimator is derived. Simulation and in vivo human brain experiments demonstrate a twenty-fold speed gain of NOVIFAST compared with conventional gradient-based NLLS estimators while maintaining a high precision and accuracy. Moreover, NOVIFAST is eight times faster than the efficient implementations of the variable projection (VARPRO) method. Furthermore, NOVIFAST is shown to be robust against initialization.
Collapse
|
25
|
Abstract
The emergence of multiparametric diffusion models combining diffusion and relaxometry measurements provides powerful new ways to explore tissue microstructure, with the potential to provide new insights into tissue structure and function. However, their ability to provide rich analyses and the potential for clinical translation critically depends on the availability of efficient, integrated, multi-dimensional acquisitions. We propose a fully integrated sequence simultaneously sampling the acquisition parameter spaces required for T1 and T2* relaxometry and diffusion MRI. Slice-level interleaved diffusion encoding, multiple spin/gradient echoes and slice-shuffling are combined for higher efficiency, sampling flexibility and enhanced internal consistency. In-vivo data was successfully acquired on healthy adult brains. Obtained parametric maps as well as clustering results demonstrate the potential of the technique to provide eloquent data with an acceleration of roughly 20 compared to conventionally used approaches. The proposed integrated acquisition, which we call ZEBRA, offers significant acceleration and flexibility compared to existing diffusion-relaxometry studies, and thus facilitates wider use of these techniques both for research-driven and clinical applications.
Collapse
|
26
|
A G Teixeira RP, Malik SJ, Hajnal JV. Fast quantitative MRI using controlled saturation magnetization transfer. Magn Reson Med 2018; 81:907-920. [PMID: 30257044 PMCID: PMC6492254 DOI: 10.1002/mrm.27442] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2018] [Revised: 06/04/2018] [Accepted: 06/10/2018] [Indexed: 01/31/2023]
Abstract
Purpose This study demonstrates magnetization transfer (MT) effects directly affect relaxometry measurements and develops a framework that allows single‐pool models to be valid in 2‐pool MT systems. Methods A theoretical framework is developed in which a 2‐pool MT system effectively behaves as a single‐pool if the RMS RF magnetic field (B1rms{\text{B}}_{1}^{{{\text{rms}}}}) is kept fixed across all measurements. A practical method for achieving controlled saturation magnetization transfer (CSMT) using multiband RF pulses is proposed. Numerical, Phantom, and in vivo validations were performed directly comparing steady state (SS) estimation approaches that under correct single‐pool assumptions would be expected to vary in precision but not accuracy. Results Numerical simulations predict single‐pool estimates obtained from MT model generated data are not consistent for different SS estimation methods, and a systematic underestimation of T2 is expected. Neither effect occurs under the proposed CSMT approach. Both phantom and in vivo experiments corroborate the numerical predictions. Experimental data highlights that even when using the same relaxometry method, different estimates are obtained depending on which combination of flip angles (FAs) and TRs are used if the CSMT approach is not used. Using CSMT, stable measurements of both T1 and T2 are obtained. The measured T1(T1CSMT)) depends on B1rms{\text{B}}_{1}^{{{\text{rms}}}}, which is therefore an important parameter to specify. Conclusion This work demonstrates that conventional single pool relaxometry, which is highly efficient for human studies, results in unreliable parameter estimates in biological tissues because of MT effects. The proposed CSMT framework is shown to allow single‐pool assumptions to be valid, enabling reliable and efficient quantitative imaging to be performed.
Collapse
Affiliation(s)
- Rui Pedro A G Teixeira
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Shaihan J Malik
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Centre for the Developing Brain, King's College London, London, United Kingdom
| |
Collapse
|
27
|
Bonny J, Pagès G. Uncertainties of calculated Cramér‐Rao lower bounds: implications for quantitative MRS. Magn Reson Med 2018; 81:759-764. [DOI: 10.1002/mrm.27415] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 05/18/2018] [Accepted: 06/01/2018] [Indexed: 11/11/2022]
Affiliation(s)
- Jean‐Marie Bonny
- INRA UR370 Qualité des Produits AnimauxSaint‐Genès‐Champanelle France
| | - Guilhem Pagès
- INRA UR370 Qualité des Produits AnimauxSaint‐Genès‐Champanelle France
| |
Collapse
|
28
|
Gilmore JH, Knickmeyer RC, Gao W. Imaging structural and functional brain development in early childhood. Nat Rev Neurosci 2018; 19:123-137. [PMID: 29449712 PMCID: PMC5987539 DOI: 10.1038/nrn.2018.1] [Citation(s) in RCA: 460] [Impact Index Per Article: 76.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In humans, the period from term birth to ∼2 years of age is characterized by rapid and dynamic brain development and plays an important role in cognitive development and risk of disorders such as autism and schizophrenia. Recent imaging studies have begun to delineate the growth trajectories of brain structure and function in the first years after birth and their relationship to cognition and risk of neuropsychiatric disorders. This Review discusses the development of grey and white matter and structural and functional networks, as well as genetic and environmental influences on early-childhood brain development. We also discuss initial evidence regarding the usefulness of early imaging biomarkers for predicting cognitive outcomes and risk of neuropsychiatric disorders.
Collapse
Affiliation(s)
- John H Gilmore
- Department of Psychiatry, CB# 7160, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Rebecca C Knickmeyer
- Department of Psychiatry, CB# 7160, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Wei Gao
- Biomedical Imaging Research Institute, Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Medicine, University of California, Los Angeles, CA, USA
| |
Collapse
|
29
|
Does MD. Inferring brain tissue composition and microstructure via MR relaxometry. Neuroimage 2018; 182:136-148. [PMID: 29305163 DOI: 10.1016/j.neuroimage.2017.12.087] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 12/25/2017] [Accepted: 12/27/2017] [Indexed: 11/28/2022] Open
Abstract
MRI relaxometry is sensitive to a variety of tissue characteristics in a complex manner, which makes it both attractive and challenging for characterizing tissue. This article reviews the most common water proton relaxometry measures, T1, T2, and T2*, and reports on their development and current potential to probe the composition and microstructure of brain tissue. The development of these relaxometry measures is challenged by the need for suitably accurate tissue models, as well as robust acquisition and analysis methodologies. MRI relaxometry has been established as a tool for characterizing neural tissue, particular with respect to myelination, and the potential for further development exists.
Collapse
Affiliation(s)
- Mark D Does
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.
| |
Collapse
|
30
|
de Boer A, Leiner T, Vink EE, Blankestijn PJ, van den Berg CAT. Modified dixon-based renal dynamic contrast-enhanced MRI facilitates automated registration and perfusion analysis. Magn Reson Med 2017; 80:66-76. [PMID: 29134673 PMCID: PMC5900902 DOI: 10.1002/mrm.26999] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 10/17/2017] [Accepted: 10/18/2017] [Indexed: 12/24/2022]
Abstract
Purpose Renal dynamic contrast‐enhanced (DCE) MRI provides information on renal perfusion and filtration. However, clinical implementation is hampered by challenges in postprocessing as a result of misalignment of the kidneys due to respiration. We propose to perform automated image registration using the fat‐only images derived from a modified Dixon reconstruction of a dual‐echo acquisition because these provide consistent contrast over the dynamic series. Methods DCE data of 10 hypertensive patients was used. Dual‐echo images were acquired at 1.5 T with temporal resolution of 3.9 s during contrast agent injection. Dixon fat, water, and in‐phase and opposed‐phase (OP) images were reconstructed. Postprocessing was automated. Registration was performed both to fat images and OP images for comparison. Perfusion and filtration values were extracted from a two‐compartment model fit. Results Automatic registration to fat images performed better than automatic registration to OP images with visible contrast enhancement. Median vertical misalignment of the kidneys was 14 mm prior to registration, compared to 3 mm and 5 mm with registration to fat images and OP images, respectively (P = 0.03). Mean perfusion values and MR‐based glomerular filtration rates (GFR) were 233 ± 64 mL/100 mL/min and 60 ± 36 mL/minute, respectively, based on fat‐registered images. MR‐based GFR correlated with creatinine‐based GFR (P = 0.04) for fat‐registered images. For unregistered and OP‐registered images, this correlation was not significant. Conclusion Absence of contrast changes on Dixon fat images improves registration in renal DCE MRI and enables automated postprocessing, resulting in a more accurate estimation of GFR. Magn Reson Med 80:66–76, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Collapse
Affiliation(s)
- Anneloes de Boer
- Utrecht University Medical Center, Utrecht University, Utrecht, The Netherlands
| | - Tim Leiner
- Utrecht University Medical Center, Utrecht University, Utrecht, The Netherlands
| | - Eva E Vink
- Utrecht University Medical Center, Utrecht University, Utrecht, The Netherlands
| | - Peter J Blankestijn
- Utrecht University Medical Center, Utrecht University, Utrecht, The Netherlands
| | | |
Collapse
|
31
|
Lankford CL, Does MD. Propagation of error from parameter constraints in quantitative MRI: Example application of multiple spin echo T 2 mapping. Magn Reson Med 2017; 79:673-682. [PMID: 28426147 DOI: 10.1002/mrm.26713] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 03/21/2017] [Accepted: 03/23/2017] [Indexed: 12/20/2022]
Abstract
PURPOSE Quantitative MRI may require correcting for nuisance parameters which can or must be constrained to independently measured or assumed values. The noise and/or bias in these constraints propagate to fitted parameters. For example, the case of refocusing pulse flip angle constraint in multiple spin echo T2 mapping is explored. METHODS An analytical expression for the mean-squared error of a parameter of interest was derived as a function of the accuracy and precision of an independent estimate of a nuisance parameter. The expression was validated by simulations and then used to evaluate the effects of flip angle (θ) constraint on the accuracy and precision of T⁁2 for a variety of multi-echo T2 mapping protocols. RESULTS Constraining θ improved T⁁2 precision when the θ-map signal-to-noise ratio was greater than approximately one-half that of the first spin echo image. For many practical scenarios, constrained fitting was calculated to reduce not just the variance but the full mean-squared error of T⁁2, for bias in θ⁁≲6%. CONCLUSION The analytical expression derived in this work can be applied to inform experimental design in quantitative MRI. The example application to T2 mapping provided specific cases, depending on θ⁁ accuracy and precision, in which θ⁁ measurement and constraint would be beneficial to T⁁2 variance or mean-squared error. Magn Reson Med 79:673-682, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
Collapse
Affiliation(s)
- Christopher L Lankford
- Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, USA
| | - Mark D Does
- Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.,Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, USA.,Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, USA.,Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| |
Collapse
|