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Heydari A, Ahmadi A, Kim TH, Bilgic B. Joint MAPLE: Accelerated joint T 1 and T 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ mapping with scan-specific self-supervised networks. Magn Reson Med 2024; 91:2294-2309. [PMID: 38181183 PMCID: PMC11007829 DOI: 10.1002/mrm.29989] [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: 07/19/2023] [Revised: 10/30/2023] [Accepted: 12/11/2023] [Indexed: 01/07/2024]
Abstract
PURPOSE Quantitative MRI finds important applications in clinical and research studies. However, it is encoding intensive and may suffer from prohibitively long scan times. Accelerated MR parameter mapping techniques have been developed to help address these challenges. Here, an accelerated joint T1,T 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ , frequency and proton density mapping technique with scan-specific self-supervised network reconstruction is proposed to synergistically combine parallel imaging, model-based, and deep learning approaches to speed up parameter mapping. METHODS Proposed framework, Joint MAPLE, includes parallel imaging, signal modeling, and data consistency blocks which are optimized jointly in a combined loss function. A scan-specific self-supervised reconstruction is embedded into the framework, which takes advantage of multi-contrast data from a multi-echo, multi-flip angle, gradient echo acquisition. RESULTS In comparison with parallel reconstruction techniques powered by low-rank methods, emerging scan specific networks, and model-basedT 2 * $$ {{\mathrm{T}}_2}^{\ast } $$ estimation approaches, the proposed framework reduces the reconstruction error in parameter maps by approximately two-fold on average at acceleration rates as high as R = 16 with uniform sampling. It can outperform evaluated parallel reconstruction techniques up to four-fold on average in the presence of challenging sub-sampling masks. It is observed that Joint MAPLE performs well at extreme acceleration rates of R = 25 and R = 36 with error values less than 20%. CONCLUSION Joint MAPLE enables higher fidelity parameter estimation at high acceleration rates by synergistically combining parallel imaging and model-based parameter mapping and exploiting multi-echo, multi-flip angle datasets. Utilizing a scan-specific self-supervised reconstruction obviates the need for large data sets for training while improving the parameter estimation ability.
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Affiliation(s)
- Amir Heydari
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
| | - Abbas Ahmadi
- Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
| | - Tae Hyung Kim
- Department of Computer Engineering, Hongik University, Seoul, Korea
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Radiology, Harvard Medical School, Boston, MA, United States
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Radiology, Harvard Medical School, Boston, MA, United States
- Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States
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Liu J, Jakary A, Villanueva-Meyer JE, Butowski NA, Saloner D, Clarke JL, Taylor JW, Oberheim Bush NA, Chang SM, Xu D, Lupo JM. Automatic Brain Tissue and Lesion Segmentation and Multi-Parametric Mapping of Contrast-Enhancing Gliomas without the Injection of Contrast Agents: A Preliminary Study. Cancers (Basel) 2024; 16:1524. [PMID: 38672606 PMCID: PMC11049314 DOI: 10.3390/cancers16081524] [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: 01/27/2024] [Revised: 04/08/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
This study aimed to develop a rapid, 1 mm3 isotropic resolution, whole-brain MRI technique for automatic lesion segmentation and multi-parametric mapping without using contrast by continuously applying balanced steady-state free precession with inversion pulses throughout incomplete inversion recovery in a single 6 min scan. Modified k-means clustering was performed for automatic brain tissue and lesion segmentation using distinct signal evolutions that contained mixed T1/T2/magnetization transfer properties. Multi-compartment modeling was used to derive quantitative multi-parametric maps for tissue characterization. Fourteen patients with contrast-enhancing gliomas were scanned with this sequence prior to the injection of a contrast agent, and their segmented lesions were compared to conventionally defined manual segmentations of T2-hyperintense and contrast-enhancing lesions. Simultaneous T1, T2, and macromolecular proton fraction maps were generated and compared to conventional 2D T1 and T2 mapping and myelination water fraction mapping acquired with MAGiC. The lesion volumes defined with the new method were comparable to the manual segmentations (r = 0.70, p < 0.01; t-test p > 0.05). The T1, T2, and macromolecular proton fraction mapping values of the whole brain were comparable to the reference values and could distinguish different brain tissues and lesion types (p < 0.05), including infiltrating tumor regions within the T2-lesion. Highly efficient, whole-brain, multi-contrast imaging facilitated automatic lesion segmentation and quantitative multi-parametric mapping without contrast, highlighting its potential value in the clinic when gadolinium is contraindicated.
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Affiliation(s)
- Jing Liu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
| | - Angela Jakary
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
| | - Javier E. Villanueva-Meyer
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
| | - Nicholas A. Butowski
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
| | - David Saloner
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
- Radiology Service, VA Medical Center, San Francisco, CA 94121, USA
| | - Jennifer L. Clarke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Jennie W. Taylor
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Nancy Ann Oberheim Bush
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Susan M. Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (N.A.B.); (J.L.C.); (S.M.C.)
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California San Francisco and Berkeley, San Francisco, CA 94143, USA
| | - Janine M. Lupo
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94143, USA; (A.J.); (D.X.)
- UCSF/UC Berkeley Graduate Program in Bioengineering, University of California San Francisco and Berkeley, San Francisco, CA 94143, USA
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3
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Jang A, Han PK, Ma C, El Fakhri G, Wang N, Samsonov A, Liu F. B 1 inhomogeneity-corrected T 1 mapping and quantitative magnetization transfer imaging via simultaneously estimating Bloch-Siegert shift and magnetization transfer effects. Magn Reson Med 2023; 90:1859-1873. [PMID: 37427533 PMCID: PMC10528411 DOI: 10.1002/mrm.29778] [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: 02/13/2023] [Revised: 05/10/2023] [Accepted: 06/06/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE To introduce a method of inducing Bloch-Siegert shift and magnetization Transfer Simultaneously (BTS) and demonstrate its utilization for measuring binary spin-bath model parameters free pool spin-lattice relaxation (T 1 F $$ {T}_1^{\mathrm{F}} $$ ), macromolecular fraction (f $$ f $$ ), magnetization exchange rate (k F $$ {k}_{\mathrm{F}} $$ ) and local transmit field (B 1 + $$ {B}_1^{+} $$ ). THEORY AND METHODS Bloch-Siegert shift and magnetization transfer is simultaneously induced through the application of off-resonance irradiation in between excitation and acquisition of an RF-spoiled gradient-echo scheme. Applying the binary spin-bath model, an analytical signal equation is derived and verified through Bloch simulations. Monte Carlo simulations were performed to analyze the method's performance. The estimation of the binary spin-bath parameters withB 1 + $$ {B}_1^{+} $$ compensation was further investigated through experiments, both ex vivo and in vivo. RESULTS Comparing BTS with existing methods, simulations showed that existing methods can significantly biasT 1 $$ {T}_1 $$ estimation when not accounting for transmitB 1 $$ {B}_1 $$ heterogeneity and MT effects that are present. Phantom experiments further showed that the degree of this bias increases with increasing macromolecular proton fraction. Multi-parameter fit results from an in vivo brain study generated values in agreement with previous literature. Based on these studies, we confirmed that BTS is a robust method for estimating the binary spin-bath parameters in macromolecule-rich environments, even in the presence ofB 1 + $$ {B}_1^{+} $$ inhomogeneity. CONCLUSION A method of estimating Bloch-Siegert shift and magnetization transfer effect has been developed and validated. Both simulations and experiments confirmed that BTS can estimate spin-bath parameters (T 1 F $$ {T}_1^{\mathrm{F}} $$ ,f $$ f $$ ,k F $$ {k}_{\mathrm{F}} $$ ) that are free fromB 1 + $$ {B}_1^{+} $$ bias.
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Affiliation(s)
- Albert Jang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Paul K Han
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Chao Ma
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Nian Wang
- Indiana University, Indianapolis, Indiana, United States
| | | | - Fang Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
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4
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Kose R, Kose K, Terada Y. Implementation of the QRAPMASTER data analysis using dictionary matching and quantitative evaluation of the magnetization transfer effect. Magn Reson Imaging 2022; 90:26-36. [DOI: 10.1016/j.mri.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 03/22/2022] [Accepted: 03/27/2022] [Indexed: 10/18/2022]
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5
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Feng L, Ma D, Liu F. Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends. NMR IN BIOMEDICINE 2022; 35:e4416. [PMID: 33063400 PMCID: PMC8046845 DOI: 10.1002/nbm.4416] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/25/2020] [Accepted: 09/09/2020] [Indexed: 05/08/2023]
Abstract
Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T1 ), the spin-spin relaxation time (T2 ), and the spin-lattice relaxation in the rotating frame (T1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T1 -, T2 -, or T1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.
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Affiliation(s)
- Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, Massachusetts
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6
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Mohammadi S, Callaghan MF. Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging. J Neurosci Methods 2021; 348:108990. [PMID: 33129894 PMCID: PMC7840525 DOI: 10.1016/j.jneumeth.2020.108990] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/21/2020] [Accepted: 10/20/2020] [Indexed: 12/19/2022]
Abstract
BACKGROUND The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. NEW METHOD This is the second review on the topic of g-ratio mapping using MRI. RESULTS This review summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. COMPARISON WITH EXISTING METHODS Using simulations based on recently published data, this review reveals caveats to the state-of-the-art calibration methods that have been used for in vivo g-ratio mapping. It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. CONCLUSIONS We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the full potential of many novel techniques yet to be investigated.
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Affiliation(s)
- Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, UK
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7
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Dvorak AV, Ljungberg E, Vavasour IM, Lee LE, Abel S, Li DKB, Traboulsee A, MacKay AL, Kolind SH. Comparison of multi echo T 2 relaxation and steady state approaches for myelin imaging in the central nervous system. Sci Rep 2021; 11:1369. [PMID: 33446710 PMCID: PMC7809349 DOI: 10.1038/s41598-020-80585-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 12/22/2020] [Indexed: 12/11/2022] Open
Abstract
The traditional approach for measuring myelin-associated water with quantitative magnetic resonance imaging (MRI) uses multi-echo T2 relaxation data to calculate the myelin water fraction (MWF). A fundamentally different approach, abbreviated “mcDESPOT”, uses a more efficient steady-state acquisition to generate an equivalent metric (fM). Although previous studies have demonstrated inherent instability and bias in the complex mcDESPOT analysis procedure, fM has often been used as a surrogate for MWF. We produced and compared multivariate atlases of MWF and fM in healthy human brain and cervical spinal cord (available online) and compared their ability to detect multiple sclerosis pathology. A significant bias was found in all regions (p < 10–5), albeit reversed for spinal cord (fM-MWF = − 3.4%) compared to brain (+ 6.2%). MWF and fM followed an approximately linear relationship for regions with MWF < ~ 10%. For MWF > ~ 10%, the relationship broke down and fM no longer increased in tandem with MWF. For multiple sclerosis patients, MWF and fM Z score maps showed overlapping areas of low Z score and similar trends between patients and brain regions, although those of fM generally had greater spatial extent and magnitude of severity. These results will guide future choice of myelin-sensitive quantitative MRI and improve interpretation of studies using either myelin imaging approach.
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Affiliation(s)
- Adam V Dvorak
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada. .,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.
| | - Emil Ljungberg
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Irene M Vavasour
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Lisa Eunyoung Lee
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Shawna Abel
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - David K B Li
- Radiology, University of British Columbia, Vancouver, BC, Canada.,Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Anthony Traboulsee
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Alex L MacKay
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Shannon H Kolind
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada.,Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
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8
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Chan KS, Marques JP. Multi-compartment relaxometry and diffusion informed myelin water imaging – Promises and challenges of new gradient echo myelin water imaging methods. Neuroimage 2020; 221:117159. [DOI: 10.1016/j.neuroimage.2020.117159] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 07/06/2020] [Accepted: 07/07/2020] [Indexed: 01/08/2023] Open
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9
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Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence. Invest Radiol 2020; 55:601-616. [PMID: 32209816 PMCID: PMC7413678 DOI: 10.1097/rli.0000000000000666] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.
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Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| | | | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
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10
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Brui E, Efimtcev AY, Fokin VA, Fernandez R, Levchuk AG, Ogier AC, Samsonov AA, Mattei JP, Melchakova IV, Bendahan D, Andreychenko A. Deep learning-based fully automatic segmentation of wrist cartilage in MR images. NMR IN BIOMEDICINE 2020; 33:e4320. [PMID: 32394453 PMCID: PMC7784718 DOI: 10.1002/nbm.4320] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 04/10/2020] [Accepted: 04/14/2020] [Indexed: 05/10/2023]
Abstract
The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in 20 multi-slice MRI datasets acquired with two different coils in 11 subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and PB-U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sørensen-Dice similarity coefficient [DSC] = 0.81) in the representative (central coronal) slices with a large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC = 0.78-0.88 and 0.9, respectively). The proposed deep learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy.
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Affiliation(s)
- Ekaterina Brui
- University of Information Technology Mechanics and Optics, International Research Center Nanophotonics and Metamaterials, 199034 S.-Petersburg, Russia
| | - Aleksandr Y. Efimtcev
- University of Information Technology Mechanics and Optics, International Research Center Nanophotonics and Metamaterials, 199034 S.-Petersburg, Russia
- Federal Almazov North-West Medical Research Center, 197341 S.-Petersburg, Russia
| | - Vladimir A. Fokin
- University of Information Technology Mechanics and Optics, International Research Center Nanophotonics and Metamaterials, 199034 S.-Petersburg, Russia
- Federal Almazov North-West Medical Research Center, 197341 S.-Petersburg, Russia
| | - Remi Fernandez
- APHM, Service de Radiologie, Hôpital de la Conception, Marseille, France
| | - Anatoliy G. Levchuk
- Federal Almazov North-West Medical Research Center, 197341 S.-Petersburg, Russia
| | - Augustin C. Ogier
- Aix-Marseille Universite, CNRS, Centre de Résonance Magnétique Biologique et Médicale, UMR 7339, Marseille, France
| | - Alexey A. Samsonov
- University of Wisconsin-Madison, Department of Radiology, Madison, WI 53705-2275 USA
| | - Jean. P. Mattei
- Aix-Marseille Universite, CNRS, Centre de Résonance Magnétique Biologique et Médicale, UMR 7339, Marseille, France
- Assistance Publique Hôpitaux de Marseille, Institut de l’appareil locomoteur, Service de Rhumatologie, Hôpital Sainte Marguerite, Marseille, France
| | - Irina V. Melchakova
- University of Information Technology Mechanics and Optics, International Research Center Nanophotonics and Metamaterials, 199034 S.-Petersburg, Russia
| | - David Bendahan
- Aix-Marseille Universite, CNRS, Centre de Résonance Magnétique Biologique et Médicale, UMR 7339, Marseille, France
| | - Anna Andreychenko
- University of Information Technology Mechanics and Optics, International Research Center Nanophotonics and Metamaterials, 199034 S.-Petersburg, Russia
- Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow, Moscow, Russia
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11
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Wang X, Hernando D, Reeder SB. Phase-based T 2 mapping with gradient echo imaging. Magn Reson Med 2019; 84:609-619. [PMID: 31872470 DOI: 10.1002/mrm.28138] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 10/31/2019] [Accepted: 11/27/2019] [Indexed: 12/12/2022]
Abstract
PURPOSE Transverse relaxation time (T2 ) mapping with MRI has a plethora of clinical and research applications. Current T2 mapping techniques are based primarily on spin-echo (SE) relaxometry strategies that rely on the signal magnitude, and often suffer from lengthy acquisition times. In this work, we propose a phase-based T2 mapping technique where T2 information is encoded into the signal phase of rapid gradient echo (GRE) acquisitions. THEORY Bloch equation simulations demonstrate that the phase of GRE acquisitions obtained with a very small inter-repetition RF phase increment has a strong monotonic dependence on T2 , resulting from coherent transverse magnetization. This T2 -dependent phase behavior forms the basis of the proposed T2 mapping technique. To isolate T2 -dependent phase from background phase, at least 2 data sets with different RF phase increments are acquired. The proposed method can also be combined with chemical shift encoded MRI to separate water and fat signals. METHODS The feasibility of the proposed technique was validated in a phantom experiment. In vivo feasibility was demonstrated in the brain, knee, abdomen, and pelvis. Comparisons were made with SE-based T2 mapping, spectroscopy, and T2 values from the literature. RESULTS The proposed method produced accurate T2 maps compared with SE-based T2 mapping in the phantom. Good qualitative agreement was observed in vivo between the proposed method and the reference. T2 measured in various anatomies agreed well with values reported in the literature. CONCLUSION A phase-based T2 mapping technique was developed and its feasibility demonstrated in phantoms and in vivo.
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Affiliation(s)
- Xiaoke Wang
- Department of Radiology, University of Wisconsin, Madison, Wisconsin.,Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin
| | - Diego Hernando
- Department of Radiology, University of Wisconsin, Madison, Wisconsin.,Department of Medical Physics, University of Wisconsin, Madison, Wisconsin
| | - Scott B Reeder
- Department of Radiology, University of Wisconsin, Madison, Wisconsin.,Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin.,Department of Medical Physics, University of Wisconsin, Madison, Wisconsin.,Department of Medicine, University of Wisconsin, Madison, Wisconsin.,Department of Emergency Medicine, University of Wisconsin, Madison, Wisconsin
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12
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Gray Matter Alterations in Early and Late Relapsing-Remitting Multiple Sclerosis Evaluated with Synthetic Quantitative Magnetic Resonance Imaging. Sci Rep 2019; 9:8147. [PMID: 31148572 PMCID: PMC6544650 DOI: 10.1038/s41598-019-44615-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 05/21/2019] [Indexed: 12/16/2022] Open
Abstract
Extensive gray matter (GM) involvement has been demonstrated in multiple sclerosis (MS) patients. This study was aimed to identify GM alterations in relapsing-remitting MS (RRMS) patients using synthetic quantitative MRI (qMRI). We assessed myelin volume fraction (MVF) in each voxel on the basis of R1 and R2 relaxation rates and proton density in 14 early and 28 late (disease duration ≤5 and >5 years, respectively) RRMS patients, and 15 healthy controls (HCs). The MVF and myelin volumes of GM (GM-MyVol) were compared between groups using GM-based spatial statistics (GBSS) and the Kruskal-Wallis test, respectively. Correlations between MVF or GM-MyVol and disease duration or expanded disability status scale were also evaluated. RRMS patients showed a lower MVF than HCs, predominantly in the limbic and para-limbic areas, with more extensive areas noted in late RRMS patients. Late-RRMS patients had the smallest GM-MyVol (20.44 mL; early RRMS, 22.77 mL; HCs, 23.36 mL). Furthermore, the GM-MyVol in the RRMS group was inversely correlated with disease duration (r = -0.43, p = 0.005). In conclusion, the MVF and MyVol obtained by synthetic qMRI can be used to evaluate GM differences in RRMS patients.
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West DJ, Teixeira RPAG, Wood TC, Hajnal JV, Tournier JD, Malik SJ. Inherent and unpredictable bias in multi-component DESPOT myelin water fraction estimation. Neuroimage 2019; 195:78-88. [PMID: 30930311 PMCID: PMC7100802 DOI: 10.1016/j.neuroimage.2019.03.049] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 03/13/2019] [Accepted: 03/21/2019] [Indexed: 01/13/2023] Open
Abstract
Multicomponent driven equilibrium steady-state observation of T1 and T2 (mcDESPOT) aims to quantify the Myelin Water Fraction (MWF) using a two-pool microstructural model. The MWF has been used to track neurodevelopment and neurodegeneration and has been histologically correlated to myelin content. mcDESPOT has a clinically feasible acquisition time and high signal-to-noise ratio (SNR) relative to other MWF techniques. However, disagreement exists in the literature between experimental studies that show MWF maps with plausible grey matter-white matter (GM-WM) contrast and theoretical work that questions the accuracy and precision of mcDESPOT. We demonstrate that mcDESPOT parameter estimation is inaccurate and imprecise if intercompartmental exchange is included in the microstructural model, but that significant bias results if exchange is neglected. The source of apparent MWF contrast is likely due to the complex convergence behaviour of the Stochastic Region Contraction (SRC) method commonly used to fit the mcDESPOT model. mcDESPOT-derived parameter estimates are hence not directly relatable to the underlying microstructural model and are only comparable to others using similar acquisition schemes and fitting constraints.
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Affiliation(s)
- Daniel J West
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, 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, SE1 7EH, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Tobias C Wood
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, Camberwell, London, SE5 8AB, United Kingdom
| | - Joseph V Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Jacques-Donald Tournier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Shaihan J Malik
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom
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14
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Wen J, Sukstanskii AL, Yablonskiy DA. Phase-sensitive B 1 mapping: Effects of relaxation and RF spoiling. Magn Reson Med 2018; 80:101-111. [PMID: 29159883 PMCID: PMC6433377 DOI: 10.1002/mrm.27009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 10/23/2017] [Accepted: 10/24/2017] [Indexed: 11/11/2022]
Abstract
PURPOSE To develop a phase-based B1 mapping technique accounting for the effects of imperfect RF spoiling and magnetization relaxation. THEORY AND METHODS The technique is based on a multi-gradient-echo sequence with 2 successive orthogonal radiofrequency (RF) excitation pulses followed by the train of gradient echoes measurements. We have derived a theoretical expression relating the MR signal phase produced by the 2 successive RF pulses to the B1 field and B0 -related frequency shift. The expression takes into account effects of imperfections of RF spoiling and T1 and T2* relaxations. RESULTS Our computer simulations and experiments revealed that imperfections of RF spoiling cause significant errors in B1 mapping if not accounted for. By accounting for these effects along with effects of magnetization relaxation and frequency shift, we demonstrated the high accuracy of our approach. The technique has been tested on spherical phantoms and a healthy volunteer. CONCLUSION In this paper, we have proposed, implemented, and demonstrated the accuracy of a new phase-based technique for fast and robust B1 mapping based on the measured MR signal phase, frequency, and relaxation. Because imperfect RF spoiling effects are accounted for, this technique can be applied with short TRs and therefore substantially reduces the scan time. Magn Reson Med 80:101-111, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
| | | | - Dmitriy A. Yablonskiy
- Correspondence to: Dmitriy A. Yablonskiy, Ph.D., Mallinckrodt Institute of Radiology, Washington University, 4525 Scott Ave., Room 3216, St. Louis, MO 63110.
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15
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Malik SJ, Teixeira RPAG, Hajnal JV. Extended phase graph formalism for systems with magnetization transfer and exchange. Magn Reson Med 2017; 80:767-779. [PMID: 29243295 PMCID: PMC5947218 DOI: 10.1002/mrm.27040] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 11/02/2017] [Accepted: 11/19/2017] [Indexed: 01/23/2023]
Abstract
Purpose An extended phase graph framework (EPG‐X) for modeling systems with exchange or magnetization transfer (MT) is proposed. Theory EPG‐X models coupled two‐compartment systems by describing each compartment with separate phase graphs that exchange during evolution periods. There are two variants: EPG‐X(BM) for systems governed by the Bloch‐McConnell equations, and EPG‐X(MT) for the pulsed MT formalism. For the MT case, the “bound” protons have no transverse components, so their phase graph consists of only longitudinal states. Methods The EPG‐X model was validated against steady‐state solutions and isochromat‐based simulation of gradient‐echo sequences. Three additional test cases were investigated: (i) MT effects in multislice turbo spin‐echo; (ii) variable flip angle gradient‐echo imaging of the type used for MR fingerprinting; and (iii) water exchange in multi‐echo spin‐echo T2 relaxometry. Results EPG‐X was validated successfully against isochromat based transient simulations and known steady‐state solutions. EPG‐X(MT) simulations matched in‐vivo measurements of signal attenuation in white matter in multislice turbo spin‐echo images. Magnetic resonance fingerprinting–style experiments with a bovine serum albumin (MT) phantom showed that the data were not consistent with a single‐pool model, but EPG‐X(MT) could be used to fit the data well. The EPG‐X(BM) simulations of multi‐echo spin‐echo T2 relaxometry suggest that exchange could lead to an underestimation of the myelin‐water fraction. Conclusions The EPG‐X framework can be used for modeling both steady‐state and transient signal response of systems exhibiting exchange or MT. This may be particularly beneficial for relaxometry approaches that rely on characterizing transient rather than steady‐state sequences. Magn Reson Med 80:767–779, 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 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Shaihan J Malik
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Rui Pedro A G Teixeira
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom
| | - Joseph V Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, United Kingdom
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16
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Hagiwara A, Hori M, Yokoyama K, Nakazawa M, Ueda R, Horita M, Andica C, Abe O, Aoki S. Analysis of White Matter Damage in Patients with Multiple Sclerosis via a Novel In Vivo MR Method for Measuring Myelin, Axons, and G-Ratio. AJNR Am J Neuroradiol 2017; 38:1934-1940. [PMID: 28775058 PMCID: PMC7963610 DOI: 10.3174/ajnr.a5312] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 05/24/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Myelin and axon volume fractions can now be estimated via MR imaging in vivo, as can the g-ratio, which equals the ratio of the inner to the outer diameter of a nerve fiber. The purpose of this study was to evaluate WM damage in patients with MS via this novel MR imaging technique. MATERIALS AND METHODS Twenty patients with relapsing-remitting MS with a combined total of 149 chronic plaques were analyzed. Myelin volume fraction was calculated based on simultaneous tissue relaxometry. Intracellular and CSF compartment volume fractions were quantified via neurite orientation dispersion and density imaging. Axon volume fraction and g-ratio were calculated by combining these measurements. Myelin and axon volume fractions and g-ratio were measured in plaques, periplaque WM, and normal-appearing WM. RESULTS All metrics differed significantly across the 3 groups (P < .001, except P = .027 for g-ratio between periplaque WM and normal-appearing WM). Those in plaques differed most from those in normal-appearing WM. The percentage changes in plaque and periplaque WM metrics relative to normal-appearing WM were significantly larger in absolute value for myelin volume fraction than for axon volume fraction and g-ratio (P < .001, except P = .033 in periplaque WM relative to normal-appearing WM for comparison between myelin and axon volume fraction). CONCLUSIONS In this in vivo MR imaging study, the myelin of WM was more damaged than axons in plaques and periplaque WM of patients with MS. Myelin and axon volume fractions and g-ratio may potentially be useful for evaluating WM damage in patients with MS.
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Affiliation(s)
- A Hagiwara
- From the Departments of Radiology (A.H., M. Hori., M.N., R.U., M. Horita, C.A., S.A.)
- Department of Radiology (A.H., O.A.), Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - M Hori
- From the Departments of Radiology (A.H., M. Hori., M.N., R.U., M. Horita, C.A., S.A.)
| | - K Yokoyama
- Neurology (K.Y.), Juntendo University School of Medicine, Tokyo, Japan
| | - M Nakazawa
- From the Departments of Radiology (A.H., M. Hori., M.N., R.U., M. Horita, C.A., S.A.)
| | - R Ueda
- From the Departments of Radiology (A.H., M. Hori., M.N., R.U., M. Horita, C.A., S.A.)
- Department of Radiological Sciences (R.U.), Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - M Horita
- From the Departments of Radiology (A.H., M. Hori., M.N., R.U., M. Horita, C.A., S.A.)
| | - C Andica
- From the Departments of Radiology (A.H., M. Hori., M.N., R.U., M. Horita, C.A., S.A.)
| | - O Abe
- Department of Radiology (A.H., O.A.), Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - S Aoki
- From the Departments of Radiology (A.H., M. Hori., M.N., R.U., M. Horita, C.A., S.A.)
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17
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Bouhrara M, Reiter DA, Sexton KW, Bergeron CM, Zukley LM, Spencer RG. Clinical high-resolution mapping of the proteoglycan-bound water fraction in articular cartilage of the human knee joint. Magn Reson Imaging 2017. [PMID: 28645697 DOI: 10.1016/j.mri.2017.06.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
PURPOSE We applied our recently introduced Bayesian analytic method to achieve clinically-feasible in-vivo mapping of the proteoglycan water fraction (PgWF) of human knee cartilage with improved spatial resolution and stability as compared to existing methods. MATERIALS AND METHODS Multicomponent driven equilibrium single-pulse observation of T1 and T2 (mcDESPOT) datasets were acquired from the knees of two healthy young subjects and one older subject with previous knee injury. Each dataset was processed using Bayesian Monte Carlo (BMC) analysis incorporating a two-component tissue model. We assessed the performance and reproducibility of BMC and of the conventional analysis of stochastic region contraction (SRC) in the estimation of PgWF. Stability of the BMC analysis of PgWF was tested by comparing independent high-resolution (HR) datasets from each of the two young subjects. RESULTS Unlike SRC, the BMC-derived maps from the two HR datasets were essentially identical. Furthermore, SRC maps showed substantial random variation in estimated PgWF, and mean values that differed from those obtained using BMC. In addition, PgWF maps derived from conventional low-resolution (LR) datasets exhibited partial volume and magnetic susceptibility effects. These artifacts were absent in HR PgWF images. Finally, our analysis showed regional variation in PgWF estimates, and substantially higher values in the younger subjects as compared to the older subject. CONCLUSIONS BMC-mcDESPOT permits HR in-vivo mapping of PgWF in human knee cartilage in a clinically-feasible acquisition time. HR mapping reduces the impact of partial volume and magnetic susceptibility artifacts compared to LR mapping. Finally, BMC-mcDESPOT demonstrated excellent reproducibility in the determination of PgWF.
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Affiliation(s)
- Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - David A Reiter
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - Kyle W Sexton
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - Christopher M Bergeron
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - Linda M Zukley
- Clinical Research Core, Office of the Scientific Director, National Institute on Aging, National Institutes of Health, Baltimore, MD 21225, USA.
| | - Richard G Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
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18
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Sharafi A, Chang G, Regatte RR. Biexponential T 2 relaxation estimation of human knee cartilage in vivo at 3T. J Magn Reson Imaging 2017; 47:809-819. [PMID: 28561955 DOI: 10.1002/jmri.25778] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 05/15/2017] [Indexed: 12/16/2022] Open
Abstract
PURPOSE To evaluate biexponential T2 relaxation mapping of human knee cartilage in vivo in clinically feasible scan times. MATERIALS AND METHODS T2 -weighted magnetic resonance (MR) images were acquired from eight healthy volunteers using a standard 3T clinical scanner. A 3D Turbo-Flash sequence was modified to enable T2 -weighted imaging with different echo times. Series of T2 -weighted images were fitted using mono- and biexponential models with two- and four-parametric nonlinear approaches, respectively. RESULTS Biexponential relaxation of T2 was detected in the knee cartilage in five regions of interest in all eight healthy volunteers. Short/long relaxation components of T2 were estimated to be 8.27 ± 0.68 / 45.35 ± 3.79 msec with corresponding fractions of 41.3 ± 1.1% / 58.6 ± 4.6%, respectively. The monoexponential relaxation of T2 was measured to be 26.9 ± 2.27 msec. The experiments showed good repeatability with coefficient of variation root mean square (CVrms ) < 18% in all regions. The only difference in gender was observed in medial tibial cartilage, where the biexponential T2 in female volunteers was significantly higher compared to male volunteers (P = 0.014). Significant differences were observed in T2 relaxation between different regions on interest. CONCLUSION Biexponential relaxation of T2 was observed in the human knee cartilage in vivo. The short and long components are thought to be related to the tightly bound and loosely bound macromolecular water compartments. These preliminary results of biexponential T2 analysis could potentially be used to increase the specificity for detection of early osteoarthritis by measuring different water compartments and their fractions. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:809-819.
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Affiliation(s)
- Azadeh Sharafi
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Gregory Chang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Ravinder R Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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Liu F, Velikina JV, Block WF, Kijowski R, Samsonov AA. Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:527-537. [PMID: 28113746 PMCID: PMC5322984 DOI: 10.1109/tmi.2016.2620961] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
We present MRiLab, a new comprehensive simulator for large-scale realistic MRI simulations on a regular PC equipped with a modern graphical processing unit (GPU). MRiLab combines realistic tissue modeling with numerical virtualization of an MRI system and scanning experiment to enable assessment of a broad range of MRI approaches including advanced quantitative MRI methods inferring microstructure on a sub-voxel level. A flexible representation of tissue microstructure is achieved in MRiLab by employing the generalized tissue model with multiple exchanging water and macromolecular proton pools rather than a system of independent proton isochromats typically used in previous simulators. The computational power needed for simulation of the biologically relevant tissue models in large 3D objects is gained using parallelized execution on GPU. Three simulated and one actual MRI experiments were performed to demonstrate the ability of the new simulator to accommodate a wide variety of voxel composition scenarios and demonstrate detrimental effects of simplified treatment of tissue micro-organization adapted in previous simulators. GPU execution allowed ∼ 200× improvement in computational speed over standard CPU. As a cross-platform, open-source, extensible environment for customizing virtual MRI experiments, MRiLab streamlines the development of new MRI methods, especially those aiming to infer quantitatively tissue composition and microstructure.
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20
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Abstract
Myelin is critical for healthy brain function. An accurate in vivo measure of myelin content has important implications for understanding brain plasticity and neurodegenerative diseases. Myelin water imaging is a magnetic resonance imaging method which can be used to visualize myelination in the brain and spinal cord in vivo. This review presents an overview of myelin water imaging data acquisition and analysis, post-mortem validation work, findings in both animal and human studies and a brief discussion about other MR techniques purported to provide in vivo myelin content. Multi-echo T2 relaxation approaches continue to undergo development and whole-brain imaging time now takes less than 10 minutes; the standard analysis method for this type of data acquisition is a non-negative least squares approach. Alternate methods including the multi-flip angle gradient echo mcDESPOT are also being used for myelin water imaging. Histological validation studies in animal and human brain and spinal cord tissue demonstrate high specificity of myelin water imaging for myelin. Potential confounding factors for in vivo myelin water fraction measurement include the presence of myelin debris and magnetization exchange processes. Myelin water imaging has successfully been used to study animal models of injury, applied in healthy human controls and can be used to assess damage and injury in conditions such as multiple sclerosis, neuromyelitis optica, schizophrenia, phenylketonuria, neurofibromatosis, niemann pick’s disease, stroke and concussion. Other quantitative magnetic resonance approaches that are sensitive to, but not specific for, myelin exist including magnetization transfer, diffusion tensor imaging and T1 weighted imaging.
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Affiliation(s)
- Alex L MacKay
- Department of Radiology, University of British Columbia, Vancouver, Canada.,Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - Cornelia Laule
- Department of Radiology, University of British Columbia, Vancouver, Canada.,Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada
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21
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Bouhrara M, Spencer RG. Rapid simultaneous high-resolution mapping of myelin water fraction and relaxation times in human brain using BMC-mcDESPOT. Neuroimage 2016; 147:800-811. [PMID: 27729276 DOI: 10.1016/j.neuroimage.2016.09.064] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2016] [Revised: 08/21/2016] [Accepted: 09/26/2016] [Indexed: 10/20/2022] Open
Abstract
A number of central nervous system (CNS) diseases exhibit changes in myelin content and magnetic resonance longitudinal, T1, and transverse, T2, relaxation times, which therefore represent important biomarkers of CNS pathology. Among the methods applied for measurement of myelin water fraction (MWF) and relaxation times, the multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) approach is of particular interest. mcDESPOT permits whole brain mapping of multicomponent T1 and T2, with data acquisition accomplished within a clinically realistic acquisition time. Unfortunately, previous studies have indicated the limited performance of mcDESPOT in the setting of the modest signal-to-noise range of high-resolution mapping, required for the depiction of small structures and to reduce partial volume effects. Recently, we showed that a new Bayesian Monte Carlo (BMC) analysis substantially improved determination of MWF from mcDESPOT imaging data. However, our previous study was limited in that it did not discuss determination of relaxation times. Here, we extend the BMC analysis to the simultaneous determination of whole-brain MWF and relaxation times using the two-component mcDESPOT signal model. Simulation analyses and in-vivo human brain studies indicate the overall greater performance of this approach compared to the stochastic region contraction (SRC) algorithm, conventionally used to derive parameter estimates from mcDESPOT data. SRC estimates of the transverse relaxation time of the long T2 fraction, T2,l, and the longitudinal relaxation time of the short T1 fraction, T1,s, clustered towards the lower and upper parameter search space limits, respectively, indicating failure of the fitting procedure. We demonstrate that this effect is absent in the BMC analysis. Our results also showed improved parameter estimation for BMC as compared to SRC for high-resolution mapping. Overall we find that the combination of BMC analysis and mcDESPOT, BMC-mcDESPOT, shows excellent performance for accurate high-resolution whole-brain mapping of MWF and bi-component transverse and longitudinal relaxation times within a clinically realistic acquisition time.
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Affiliation(s)
- Mustapha Bouhrara
- Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Intramural Research Program, BRC 04B-116, 251 Bayview Boulevard, Baltimore, MD 21224, USA.
| | - Richard G Spencer
- Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Intramural Research Program, BRC 04B-116, 251 Bayview Boulevard, Baltimore, MD 21224, USA.
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22
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Liu F, Chaudhary R, Block WF, Samsonov A, Kijowski R. Multicomponent T2 analysis of articular cartilage with synovial fluid partial volume correction. J Magn Reson Imaging 2016; 43:1140-7. [PMID: 26435385 PMCID: PMC4878387 DOI: 10.1002/jmri.25061] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Accepted: 09/18/2015] [Indexed: 12/20/2022] Open
Abstract
PURPOSE To investigate the use of a three-pool model to account for the confounding effects of synovial fluid on multicomponent T2 analysis of articular cartilage using Multicomponent Driven Equilibrium Single Shot Observation of T1 and T2 (mcDESPOT). MATERIALS AND METHODS mcDESPOT was performed on the knee of eight asymptomatic volunteers and eight patients with osteoarthritis at 3.0T with multicomponent T2 maps created using the two-pool model and a three-pool model containing a nonexchanging synovial fluid water pool. The fraction of the fast-relaxing water component (FF ) and the T2 relaxation times for the fast-relaxing (T2F ) and slow-relaxing (T2S ) water components were measured in the superficial and deep layers of patellar cartilage using the two-pool and three-pool models in asymptomatic volunteers and patients with osteoarthritis and were compared using Wilcoxon signed rank tests. RESULTS Within the superficial layer of patellar cartilage, FF was 22.5% and 25.6% for asymptomatic volunteers and 21.3% and 22.8% for patients with osteoarthritis when using the two-pool and three-pool models, respectively, while T2S was 73.9 msec and 62.0 msec for asymptomatic volunteers and 72.0 msec and 63.1 msec for patients with osteoarthritis when using the two-pool and three-pool models, respectively. For both asymptomatic volunteers and patients with osteoarthritis, the two-pool model provided significantly (P < 0.05) lower FF and higher T2S than the three-pool model, likely due to the effects of synovial fluid partial volume averaging. CONCLUSION The effects of partial volume averaging between superficial cartilage and synovial fluid may result in biased multicomponent T2 measurements that can be corrected using an mcDESPOT three-pool model containing a nonexchanging synovial fluid water pool.
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Affiliation(s)
- Fang Liu
- Department of Medical Physics, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Rajeev Chaudhary
- Department of Biomedical Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Walter F. Block
- Department of Medical Physics, University of Wisconsin - Madison, Madison, Wisconsin, USA
- Department of Biomedical Engineering, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Alexey Samsonov
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA
| | - Richard Kijowski
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA
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Sukstanskii AL, Wen J, Cross AH, Yablonskiy DA. Simultaneous multi-angular relaxometry of tissue with MRI (SMART MRI): Theoretical background and proof of concept. Magn Reson Med 2016; 77:1296-1306. [PMID: 26991525 DOI: 10.1002/mrm.26176] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 01/29/2016] [Accepted: 01/31/2016] [Indexed: 01/09/2023]
Abstract
PURPOSE Accurate measurement of tissue-specific relaxation parameters is an ultimate goal of quantitative MRI. The objective of this study is to introduce a new technique, simultaneous multiangular relaxometry of tissue with MRI (SMART MRI), which provides naturally coregistered quantitative spin density, longitudinal and transverse relaxation rate constant maps along with parameters characterizing magnetization transfer (MT) effects. THEORY AND METHODS SMART MRI is based on a gradient-recalled echo MRI sequence with multiple flip angles and multiple gradient echoes and a derived theoretical expression for the MR signal generated in this experimental conditions. The theory, based on Bloch-McConnell equations, takes into consideration cross-relaxation between two water pools: "free" and "bound" to macromolecules. It describes the role of cross-relaxation effects in formation of longitudinal and transverse relaxation of "free" water signal, thus providing background for measurements of these effects without using MT pulses. Bayesian analysis is used to optimize SMART MRI sequence parameters. RESULTS Data obtained on three participants demonstrate feasibility of the proposed approach. CONCLUSION SMART MRI provides quantitative measurements of longitudinal and transverse relaxation rate constants of "free" water signal affected by cross-relaxation effects. It also provides information on some essential MT parameters without requiring off-resonance MT pulses. Magn Reson Med 77:1296-1306, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
| | - Jie Wen
- Department of Radiology, Washington University, St. Louis, Missouri, USA
| | - Anne H Cross
- Department of Radiology, Washington University, St. Louis, Missouri, USA
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Bouhrara M, Spencer RG. Improved determination of the myelin water fraction in human brain using magnetic resonance imaging through Bayesian analysis of mcDESPOT. Neuroimage 2016; 127:456-471. [PMID: 26499810 PMCID: PMC4854306 DOI: 10.1016/j.neuroimage.2015.10.034] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 09/26/2015] [Accepted: 10/14/2015] [Indexed: 10/22/2022] Open
Abstract
Myelin water fraction (MWF) mapping with magnetic resonance imaging has led to the ability to directly observe myelination and demyelination in both the developing brain and in disease. Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) has been proposed as a rapid approach for multicomponent relaxometry and has been applied to map MWF in the human brain. However, even for the simplest two-pool signal model consisting of myelin-associated and non-myelin-associated water, the dimensionality of the parameter space for obtaining MWF estimates remains high. This renders parameter estimation difficult, especially at low-to-moderate signal-to-noise ratios (SNRs), due to the presence of local minima and the flatness of the fit residual energy surface used for parameter determination using conventional nonlinear least squares (NLLS)-based algorithms. In this study, we introduce three Bayesian approaches for analysis of the mcDESPOT signal model to determine MWF. Given the high-dimensional nature of the mcDESPOT signal model, and, therefore the high-dimensional marginalizations over nuisance parameters needed to derive the posterior probability distribution of the MWF, the Bayesian analyses introduced here use different approaches to reduce the dimensionality of the parameter space. The first approach uses normalization by average signal amplitude, and assumes that noise can be accurately estimated from signal-free regions of the image. The second approach likewise uses average amplitude normalization, but incorporates a full treatment of noise as an unknown variable through marginalization. The third approach does not use amplitude normalization and incorporates marginalization over both noise and signal amplitude. Through extensive Monte Carlo numerical simulations and analysis of in vivo human brain datasets exhibiting a range of SNR and spatial resolution, we demonstrated markedly improved accuracy and precision in the estimation of MWF using these Bayesian methods as compared to the stochastic region contraction (SRC) implementation of NLLS.
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Affiliation(s)
- Mustapha Bouhrara
- Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - Richard G Spencer
- Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
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Bouhrara M, Spencer RG. Incorporation of nonzero echo times in the SPGR and bSSFP signal models used in mcDESPOT. Magn Reson Med 2015; 74:1227-35. [PMID: 26407635 PMCID: PMC4619140 DOI: 10.1002/mrm.25984] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Revised: 08/03/2015] [Accepted: 08/21/2015] [Indexed: 12/22/2022]
Abstract
PURPOSE To analyze the effect of neglecting nonzero echo times (TEs) in the conventional model of multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT). THEORY AND METHODS Formulations of the two-component spoiled gradient recalled echo (SPGR) and balanced steady state free precession (bSSFP) models that incorporate nonzero TE effects are presented in the context of mcDESPOT and compared with the conventionally used SPGR and bSSFP models which ignore nonzero TEs. Relative errors in derived parameter estimates from conventional mcDESPOT, omitting TE effects, are assessed using simulations over a wide range of experimental and sample parameters. RESULTS The neglect of nonzero TE leads to an overestimate of the SPGR signal and an underestimate of the bSSFP signal. These effects can introduce large errors in parameter estimates derived from conventional mcDESPOT under realistic imaging conditions. CONCLUSION SPGR and bSSFP signal models accounting for nonzero TE effects should be incorporated into quantitative mcDESPOT analyses.
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Affiliation(s)
- Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Richard G. Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
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