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Aimo A, Huang L, Tyler A, Barison A, Martini N, Saccaro LF, Roujol S, Masci PG. Quantitative susceptibility mapping (QSM) of the cardiovascular system: challenges and perspectives. J Cardiovasc Magn Reson 2022; 24:48. [PMID: 35978351 PMCID: PMC9387036 DOI: 10.1186/s12968-022-00883-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 08/05/2022] [Indexed: 11/10/2022] Open
Abstract
Quantitative susceptibility mapping (QSM) is a powerful, non-invasive, magnetic resonance imaging (MRI) technique that relies on measurement of magnetic susceptibility. So far, QSM has been employed mostly to study neurological disorders characterized by iron accumulation, such as Parkinson's and Alzheimer's diseases. Nonetheless, QSM allows mapping key indicators of cardiac disease such as blood oxygenation and myocardial iron content. For this reason, the application of QSM offers an unprecedented opportunity to gain a better understanding of the pathophysiological changes associated with cardiovascular disease and to monitor their evolution and response to treatment. Recent studies on cardiovascular QSM have shown the feasibility of a non-invasive assessment of blood oxygenation, myocardial iron content and myocardial fibre orientation, as well as carotid plaque composition. Significant technical challenges remain, the most evident of which are related to cardiac and respiratory motion, blood flow, chemical shift effects and susceptibility artefacts. Significant work is ongoing to overcome these challenges and integrate the QSM technique into clinical practice in the cardiovascular field.
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Affiliation(s)
- Alberto Aimo
- Scuola Superiore Sant'Anna, Pisa, Italy
- Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | - Li Huang
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrew Tyler
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrea Barison
- Scuola Superiore Sant'Anna, Pisa, Italy
- Fondazione Toscana Gabriele Monasterio, Pisa, Italy
| | | | | | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- Department of Biomedical Engineering, School of Imaging Sciences & Biomedical Engineering, King's College London, St Thomas' Hospital, 4th Floor Lambeth Wing, London, SE1 7EH, UK.
| | - Pier-Giorgio Masci
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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Lu Z, Li J, Wang C, Ge R, Chen L, He H, Shi J. S2Q-Net: Mining the High-Pass Filtered Phase Data in Susceptibility Weighted Imaging for Quantitative Susceptibility Mapping. IEEE J Biomed Health Inform 2022; 26:3938-3949. [PMID: 35254999 DOI: 10.1109/jbhi.2022.3156548] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Susceptibility weighted imaging (SWI) is a routine magnetic resonance imaging (MRI) sequence that combines the magnitude and high-pass filtered phase images to qualitatively enhance the image contrasts related to tissue susceptibility. Tremendous amounts of the high-pass filtered phase data with low signal to noise ratio and incomplete background field removal have thus been collected under default clinical settings. Since SWI cannot quantitatively estimate the susceptibility, it is thus non-trivial to derive quantitative susceptibility mapping (QSM) directly from these redundant phase data, which effectively promotes the mining of the SWI data collected previously and even provides potentials for synchronous imaging of both SWI and QSM based on single SWI scanning in future. To this end, a novel deep learning based SWI-to-QSM-Net (S2Q-Net) is proposed for QSM reconstruction from SWI high-pass filtered phase data. S2Q-Net firstly estimates the edge maps of QSM to integrate edge prior into features, which benefits the network to reconstruct QSM with realistic and clear tissue boundaries. Furthermore, a novel Second-order Cross Dense Block is proposed in S2Q-Net, which can capture rich inter-region interactions to provide rich non-local phase information related to local tissue susceptibility. Experimental results on both simulated and in-vivo datasets demonstrate its superiority over all the compared QSM reconstruction methods, including conventional methods and the state-of-the-art DL-based algorithms. Our results suggest the potentials of S2Q-Net to reconstruct promising QSM from the high-pass filtered phase obtained in clinical SWI sequences.
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Bachrata B, Trattnig S, Robinson SD. Quantitative susceptibility mapping of the head-and-neck using SMURF fat-water imaging with chemical shift and relaxation rate corrections. Magn Reson Med 2022; 87:1461-1479. [PMID: 34850446 PMCID: PMC7612304 DOI: 10.1002/mrm.29069] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/23/2021] [Accepted: 10/15/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE To address the challenges posed by fat-water chemical shift artifacts and relaxation rate discrepancies to quantitative susceptibility mapping (QSM) outside the brain, and to generate accurate susceptibility maps of the head-and-neck at 3 and 7 Tesla. METHODS Simultaneous Multiple Resonance Frequency (SMURF) imaging was extended to 7 Tesla and used to acquire head-and-neck gradient echo images at both 3 and 7 Tesla. Separated fat and water images were corrected for Type 1 (displacement) and Type 2 (phase discrepancy) chemical shift artefacts, and for the bias resulting from differences in T1 and T 2 ∗ relaxation rates, recombined and used as the basis for QSM. A novel phase signal-based masking approach was used to generate head-and-neck masks. RESULTS SMURF generated well-separated fat and water images of the head-and-neck. Corrections for chemical shift artefacts and relaxation rate differences removed overestimation of the susceptibility values, blurring in the susceptibility maps, and the disproportionate influence of fat in mixed voxels. The resulting susceptibility maps showed high correspondence between the paramagnetic areas and the locations of fatty tissues and the susceptibility estimates were similar to literature values. The proposed masking approach was shown to provide a simple means of generating head-and-neck masks. CONCLUSION Corrections for Type 1 and Type 2 chemical shift artefacts and for fat-water relaxation rate differences, mainly in T1 , were shown to be required for accurate susceptibility mapping of fatty-body regions. SMURF made it possible to apply these corrections and generate high-quality susceptibility maps of the entire head-and-neck at both 3 and 7 Tesla.
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Affiliation(s)
- Beata Bachrata
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria
| | - Siegfried Trattnig
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria
| | - Simon Daniel Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria
- Centre of Advanced Imaging, University of Queensland, Brisbane, Australia
- Department of Neurology, Medical University of Graz, Graz, Austria
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Stone AJ, Tornifoglio B, Johnston RD, Shmueli K, Kerskens C, Lally C. Quantitative susceptibility mapping of carotid arterial tissue ex vivo: Assessing sensitivity to vessel microstructural composition. Magn Reson Med 2021; 86:2512-2527. [PMID: 34270122 DOI: 10.1002/mrm.28893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/27/2021] [Accepted: 05/31/2021] [Indexed: 01/01/2023]
Abstract
PURPOSE To characterize microstructural contributions to the magnetic susceptibility of carotid arteries. METHOD Arterial vessels were scanned using high-resolution quantitative susceptibility mapping (QSM) at 7 Tesla. Models of vessel degradation were generated using ex vivo porcine carotid arteries that were subjected to several different enzymatic digestion treatments that selectively removed microstructural components (smooth muscle cells, collagen, and elastin). Magnetic susceptibilities measured in these tissue models were compared to those in untreated (native) porcine arteries. Magnetic susceptibility measured in native porcine carotid arteries was further compared to the susceptibility of cadaveric human carotid arteries to investigate their similarity. RESULTS The magnetic susceptibility of native porcine vessels was diamagnetic (χnative = -0.1820 ppm), with higher susceptibilities in all models of vessel degradation (χelastin-degraded = -0.0163 ppm; χcollagen-degraded = -0.1158 ppm; χdecellularized = -0.1379 ppm; χfixed native = -0.2199 ppm). Magnetic susceptibility was significantly higher in collagen-degraded compared to native porcine vessels (Tukey-Kramer, P < .01) and between elastin-degraded and all other models (including native, Tukey-Kramer, P < .001). The susceptibility of fixed healthy human arterial tissue was diamagnetic, and no significant difference was found between fixed human and fixed porcine arterial tissue susceptibilities (analysis of variance, P > .05). CONCLUSIONS Magnetic susceptibility measured using QSM is sensitive to the microstructural composition of arterial vessels-most notably to collagen. The similarity of human and porcine arterial tissue susceptibility values provides a solid basis for translational studies. Because vessel microstructure becomes disrupted during the onset and progression of carotid atherosclerosis, QSM has the potential to provide a sensitive and specific marker of vessel disease.
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Affiliation(s)
- Alan J Stone
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Mechanical, Manufacturing and Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland
| | - Brooke Tornifoglio
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Mechanical, Manufacturing and Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland
| | - Robert D Johnston
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Mechanical, Manufacturing and Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Christian Kerskens
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Caitríona Lally
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Mechanical, Manufacturing and Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland.,Advanced Materials and Bioengineering Research Centre (AMBER), Royal College of Surgeons in Ireland and Trinity College Dublin, Dublin, Ireland
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Investigation of the magnetic susceptibility properties of fresh and fixed mouse heart, liver, skeletal muscle and brain tissue. Phys Med 2021; 88:37-44. [PMID: 34171574 DOI: 10.1016/j.ejmp.2021.06.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 06/08/2021] [Accepted: 06/13/2021] [Indexed: 11/22/2022] Open
Abstract
PURPOSE Several magnetic resonance imaging (MRI) techniques exploit the difference in magnetic susceptibilities between tissues, but systematic measurements of tissue susceptibility are lacking. Furthermore, there is the question as to whether chemical fixation that is used for ex vivo MRI studies, affects the magnetic properties of the tissue. Here, we determined the magnetic susceptibility and water content of fresh and chemically fixed mouse tissue. METHODS Mass susceptibility of brain, heart, liver and skeletal muscle samples were determined on a vibrating sample magnetometer at room temperature. Measurements at 50, 125, 200 and 295 K were performed to assess the temperature dependence of susceptibility. Moreover, we measured water content of fresh and fixed samples. RESULTS All samples show mass susceptibilities between -0.068 and -1.929 × 10-8 m3/kg, compared to -9.338 × 10-9 m3/kg of double distilled water. Heart tissue has a more diamagnetic susceptibility than the other tissues. Compared to fresh tissue, fixed tissue has a less diamagnetic susceptibility. Fixed tissue was not different in water content to fresh tissue and showed no consistent dependence of susceptibility with temperature, whereas fresh tissue shows a decrease to at least 125 K, indicative of a paramagnetic component. CONCLUSIONS Biological tissues are diamagnetic in comparison to water, where the heart is more diamagnetic than the other tissues, with paramagnetic contributions. Fixation rendered tissue less diamagnetic compared to fresh tissue. Our measurements revealed differences in tissue susceptibility between VSM and QSM, inviting more research to compare susceptibility-based MRI methods with physical measurements of tissue susceptibility.
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Jang H, Chang EY, Du J. Editorial for "Change in Susceptibility Values in Knee Cartilage After Marathon Running Measured Using Quantitative Susceptibility Mapping". J Magn Reson Imaging 2021; 54:1594-1595. [PMID: 34031941 DOI: 10.1002/jmri.27743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 04/23/2021] [Indexed: 11/08/2022] Open
Affiliation(s)
- Hyungseok Jang
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Eric Y Chang
- Department of Radiology, University of California San Diego, San Diego, California, USA.,Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, California, USA
| | - Jiang Du
- Department of Radiology, University of California San Diego, San Diego, California, USA
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Zhang M, Li Y, Feng R, Wang Z, Wang W, Zheng N, Wang S, Yan F, Lu Y, Tsai TY, Wei H. Change in Susceptibility Values in Knee Cartilage After Marathon Running Measured Using Quantitative Susceptibility Mapping. J Magn Reson Imaging 2021; 54:1585-1593. [PMID: 34031930 DOI: 10.1002/jmri.27745] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 05/10/2021] [Accepted: 05/12/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Quantitative susceptibility mapping (QSM) has been used to study the magnetic susceptibility properties of collagen fibers in articular cartilage; however, it is unclear whether QSM is sensitive to changes due to degradation caused by long-distance running. It is clinically important to understand the link between long-distance running and microstructural changes in knee cartilage. PURPOSE To investigate the ability of QSM to assess microstructural changes within cartilage after repetitive loading. STUDY TYPE Prospective. POPULATION Thirteen recreational, male long-distance runners. FIELD STRENGTH/SEQUENCE Three-dimensional gradient recalled echo acquired at 3 T. ASSESSMENT Magnetic resonance imaging (MRI) and 3D kinematics (translations and rotations during treadmill walking and running) of the knee joint were collected before and after marathon running. The compartments for analysis included the patella, trochlea, and subregions of femoral and tibial cartilage. Changes in regional susceptibility and cartilage thickness were calculated after marathon running. A susceptibility profile was obtained by fitting susceptibility as a function of the normalized depth of cartilage from the superficial to deep layers. STATISTICAL TESTS Paired t-test or Wilcoxon signed-rank test, 95% confidence interval (CI) of the depth-wise susceptibility profile, Pearson correlation or Spearman correlation. RESULTS There was a statistically significant increase in susceptibility value in the weight-bearing region of central medial femoral cartilage (cMF-c) after marathon running (pre-marathon: -0.0219 ± 0.0151 ppm, post-marathon: -0.0070 ± 0.0213 ppm, P < 0.05), while the cartilage thickness did not show significant changes in any regions (P-value range: 0.068-0.963). Significant susceptibility elevations occurred in the middle and deep layers of cMF-c (95% CIs did not overlap). A trend toward a positive correlation was found between the changes in susceptibility value in cMF-c and proximal-distal translation of the knee joint during walking (r = 0.55, P = 0.101) and running (r = 0.57, P = 0.089). DATA CONCLUSION Localized magnetic susceptibility alterations were observed within knee cartilage in the weight-bearing area after repetitive loading without any morphologic changes. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yufei Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ruimin Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhongzheng Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wenjin Wang
- School of Kinesiology, Shanghai University of Sport, Shanghai, China
| | - Nan Zheng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shaobai Wang
- School of Kinesiology, Shanghai University of Sport, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yong Lu
- Department of Radiology, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tsung-Yuan Tsai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
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Cao S, Wei H, Chen J, Liu C. Asymmetric susceptibility tensor imaging. Magn Reson Med 2021; 86:2266-2275. [PMID: 34014008 DOI: 10.1002/mrm.28823] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 04/08/2021] [Accepted: 04/11/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE To investigate the symmetry constraint in susceptibility tensor imaging. THEORY The linear relationship between the MRI frequency shift and the magnetic susceptibility tensor is derived without constraining the tensor to be symmetric. In the asymmetric case, the system matrix is shown to be maximally rank 6. Nonetheless, relaxing the symmetry constraint may still improve tensor estimation because noise and image artifacts do not necessarily follow the constraint. METHODS Gradient echo phase data are obtained from postmortem mouse brain and kidney samples. Both symmetric and asymmetric tensor reconstructions are applied to the data. The reconstructions are then used for susceptibility tensor imaging fiber tracking. Simulations with ground truth and at various noise levels are also performed. The reconstruction methods are compared qualitatively and quantitatively. RESULTS Compared to regularized and unregularized symmetric reconstructions, the asymmetric reconstruction shows reduced noise and streaking artifacts, better contrast, and more complete fiber tracking. In simulation, the asymmetric reconstruction achieves better mean squared error and better angular difference in the presence of noise. Decomposing the asymmetric tensor into its symmetric and antisymmetric components confirms that the underlying susceptibility tensor is symmetric and that the main sources of asymmetry are noise and streaking artifacts. CONCLUSION Whereas the susceptibility tensor is symmetric, asymmetric reconstruction is more effective in suppressing noise and artifacts, resulting in more accurate estimation of the susceptibility tensor.
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Affiliation(s)
- Steven Cao
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | - Hongjiang Wei
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA.,Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jingjia Chen
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA.,Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
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Bao L, Xiong C, Wei W, Chen Z, van Zijl PCM, Li X. Diffusion-regularized susceptibility tensor imaging (DRSTI) of tissue microstructures in the human brain. Med Image Anal 2020; 67:101827. [PMID: 33166777 DOI: 10.1016/j.media.2020.101827] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2019] [Revised: 08/19/2020] [Accepted: 08/31/2020] [Indexed: 10/23/2022]
Abstract
Susceptibility tensor imaging (STI) has been proposed as an alternative to diffusion tensor imaging (DTI) for non-invasive in vivo characterization of brain tissue microstructure and white matter fiber architecture, potentially benefitting from its high spatial resolution. In spite of different biophysical mechanisms, animal studies have demonstrated white matter fiber directions measured using STI to be reasonably consistent with those from diffusion tensor imaging (DTI). However, human brain STI is hampered by its requirement of acquiring data at more than 10 head rotations and a complicated processing pipeline. In this paper, we propose a diffusion-regularized STI method (DRSTI) that employs a tensor spectral decomposition constraint to regularize the STI solution using the fiber directions estimated by DTI as a priori. We then explore the high-resolution DRSTI with MR phase images acquired at only 6 head orientations. Compared to other STI approaches, the DRSTI generated susceptibility tensor components, mean magnetic susceptibility (MMS), magnetic susceptibility anisotropy (MSA) and fiber direction maps with fewer artifacts, especially in regions with large susceptibility variations, and with less erroneous quantifications. In addition, the DRSTI method allows us to distinguish more structural features that could not be identified in DTI, especially in deep gray matters. DRSTI enables a more accurate susceptibility tensor estimation with a reduced number of sampling orientations, and achieves better tracking of fiber pathways than previous STI attempts on in vivo human brain.
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Affiliation(s)
- Lijun Bao
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361000, China.
| | - Congcong Xiong
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361000, China
| | - Wenping Wei
- Medical Imaging Diagnostic Center, First Affiliated Hospital of Xiamen University, Xiamen 361000, China
| | - Zhong Chen
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361000, China
| | - Peter C M van Zijl
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA
| | - Xu Li
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA
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Jang H, von Drygalski A, Wong J, Zhou JY, Aguero P, Lu X, Cheng X, Ball ST, Ma Y, Chang EY, Du J. Ultrashort echo time quantitative susceptibility mapping (UTE-QSM) for detection of hemosiderin deposition in hemophilic arthropathy: A feasibility study. Magn Reson Med 2020; 84:3246-3255. [PMID: 32662904 DOI: 10.1002/mrm.28388] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 05/31/2020] [Accepted: 06/01/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE The purpose of this study was to investigate the feasibility of ultrashort echo time quantitative susceptibility mapping (UTE-QSM) for assessment of hemosiderin deposition in the joints of hemophilic patients. METHODS The UTE-QSM technique was based on three sets of dual-echo 3D UTE Cones data acquired with TEs of 0.032/2.8, 0.2/3.6, and 0.4/4.4 ms. The images were processed with iterative decomposition of water and fat with echo asymmetry and least-squares estimation to estimate the B0 field map in the presence of fat. Then, the projection onto dipole field (PDF) algorithm was applied to acquire a local field map generated by tissues, followed by application of the morphology-enabled dipole inversion (MEDI) algorithm to estimate a final susceptibility map. Three healthy volunteers and three hemophilic patients were recruited to evaluate the UTE-QSM technique's ability to assess hemosiderin in the knee or ankle joint at 3T. One patient subsequently underwent total knee arthroplasty after the MR scan. The synovial tissues harvested from the knee joint during surgery were processed for histological analysis to confirm iron deposition. RESULTS UTE-QSM successfully yielded tissue susceptibility maps of joints in both volunteers and patients. Multiple regions with high susceptibility over 1 ppm were detected in the affected joints of hemophilic patients, while no localized regions with high susceptibility were detected in asymptomatic healthy volunteers. Histology confirmed the presence of iron in regions where high susceptibility was detected by UTE-QSM. CONCLUSION The UTE-QSM technique can detect hemosiderin deposition in the joint, and provides a potential sensitive biomarker for the diagnosis and prognosis of hemophilic arthropathy.
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Affiliation(s)
- Hyungseok Jang
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Annette von Drygalski
- Department of Medicine, University of California San Diego, San Diego, California, USA
| | - Jonathan Wong
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Jenny Y Zhou
- Department of Medicine, University of California San Diego, San Diego, California, USA
| | - Peter Aguero
- Department of Medicine, University of California San Diego, San Diego, California, USA
| | - Xing Lu
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Xin Cheng
- Department of Histology and Embryology, Jinan University, Guangzhou, China
| | - Scott T Ball
- Orthopedic Surgery, University of California San Diego, San Diego, California, USA
| | - Yajun Ma
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Eric Y Chang
- Department of Radiology, University of California San Diego, San Diego, California, USA.,Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, California, USA
| | - Jiang Du
- Department of Radiology, University of California San Diego, San Diego, California, USA
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Wei H, Cao S, Zhang Y, Guan X, Yan F, Yeom KW, Liu C. Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction. Neuroimage 2019; 202:116064. [PMID: 31377323 PMCID: PMC6819263 DOI: 10.1016/j.neuroimage.2019.116064] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 07/29/2019] [Accepted: 07/30/2019] [Indexed: 01/11/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g., in vivo mouse brain data and brains with lesions, which suggests that the network generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. Quantitative and qualitative comparisons were performed between autoQSM and other two-step QSM methods. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction, and high reconstruction speed demonstrate autoQSM's potential for future applications.
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Affiliation(s)
- Hongjiang Wei
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Steven Cao
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Yuyao Zhang
- School of Information and Science and Technology, ShanghaiTech University, Shanghai, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Fuhua Yan
- Department of Radiology, Rui Jin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Kristen W Yeom
- Department of Radiology, Lucile Packard Children's Hospital, Stanford University, Palo Alto, CA, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.
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Wei H, Decker K, Nguyen H, Cao S, Tsai TY, Dianne Guy C, Bashir M, Liu C. Imaging diamagnetic susceptibility of collagen in hepatic fibrosis using susceptibility tensor imaging. Magn Reson Med 2019; 83:1322-1330. [PMID: 31633237 DOI: 10.1002/mrm.27995] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 08/22/2019] [Accepted: 08/24/2019] [Indexed: 12/16/2022]
Abstract
PURPOSE To characterize the magnetic susceptibility changes of liver fibrosis using susceptibility tensor imaging. METHODS Liver biopsy tissue samples of patients with liver fibrosis were obtained. Three-dimensional gradient-echo and diffusion-weighted images were acquired at 9.4 T. Susceptibility tensors of the samples were calculated using the gradient-echo phase signal acquired at 12 different orientations relative to the B0 field. Susceptibility anisotropy of the liver collagen fibers was quantified and compared with diffusion anisotropy, measured by DTI. For validation, a comparison was made to histology including hematoxylin and eosin staining, iron staining, and Masson's trichrome staining. RESULTS Areas with strong diamagnetic susceptibility were observed in the tissue samples forming fibrous patterns. This diamagnetic susceptibility was highly anisotropic. Both the mean magnetic susceptibility and susceptibility anisotropy of collagen fibers exhibited a strong contrast against the surrounding nonfibrotic tissues. The same regions also showed an elevated diffusion anisotropy but with much lower tissue contrast. Masson's trichrome staining identified concentrated collagens in the fibrous regions with high susceptibility anisotropy, and a linear correlation was found between the susceptibility anisotropy and the histology-based staging. CONCLUSION Diamagnetic susceptibility indicates the presence of collagen in the fibrotic liver tissues. Mapping magnetic susceptibility anisotropy may serve as a potential marker to quantify collagen fiber changes in patients with liver fibrosis.
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Affiliation(s)
- Hongjiang Wei
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California
| | - Kyle Decker
- Center for In Vivo Microscopy, Duke University, Durham, North Carolina
| | - Hien Nguyen
- Department of Pathology, Duke University, Durham, North Carolina
| | - Steven Cao
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California
| | - Tsung-Yuan Tsai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | | | - Mustafa Bashir
- Department of Radiology, Duke University, Durham, North Carolina.,Center for Advanced Magnetic Resonance Development, Duke University, Durham, North Carolina.,Division of Gastroenterology, Department of Medicine, Duke University, Durham, North Carolina
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California.,Helen Wills Neuroscience Institute, University of California, Berkeley, California
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13
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Liu C, Özarslan E. Multimodal integration of diffusion MRI for better characterization of tissue biology. NMR IN BIOMEDICINE 2019; 32:e3939. [PMID: 30011138 DOI: 10.1002/nbm.3939] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 04/01/2018] [Accepted: 04/09/2018] [Indexed: 06/08/2023]
Abstract
The contrast in diffusion-weighted MR images is due to variations of diffusion properties within the examined specimen. Certain microstructural information on the underlying tissues can be inferred through quantitative analyses of the diffusion-sensitized MR signals. In the first part of the paper, we review two types of approach for characterizing diffusion MRI signals: Bloch's equations with diffusion terms, and statistical descriptions. Specifically, we discuss expansions in terms of cumulants and orthogonal basis functions, the confinement tensor formalism and tensor distribution models. Further insights into the tissue properties may be obtained by integrating diffusion MRI with other techniques, which is the subject of the second part of the paper. We review examples involving magnetic susceptibility, structural tensors, internal field gradients, transverse relaxation and functional MRI. Integrating information provided by other imaging modalities (MR based or otherwise) could be a key to improve our understanding of how diffusion MRI relates to physiology and biology.
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Affiliation(s)
- Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
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14
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Quantifying MRI frequency shifts due to structures with anisotropic magnetic susceptibility using pyrolytic graphite sheet. Sci Rep 2018; 8:6259. [PMID: 29674616 PMCID: PMC5908955 DOI: 10.1038/s41598-018-24650-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 03/26/2018] [Indexed: 01/23/2023] Open
Abstract
Magnetic susceptibility is an important source of contrast in magnetic resonance imaging (MRI), with spatial variations in the susceptibility of tissue affecting both the magnitude and phase of the measured signals. This contrast has generally been interpreted by assuming that tissues have isotropic magnetic susceptibility, but recent work has shown that the anisotropic magnetic susceptibility of ordered biological tissues, such as myelinated nerves and cardiac muscle fibers, gives rise to unexpected image contrast. This behavior occurs because the pattern of field variation generated by microstructural elements formed from material of anisotropic susceptibility can be very different from that predicted by modelling the effects in terms of isotropic susceptibility. In MR images of tissue, such elements are manifested at a sub-voxel length-scale, so the patterns of field variation that they generate cannot be directly visualized. Here, we used pyrolytic graphite sheet which has a large magnetic susceptibility anisotropy to form structures of known geometry with sizes large enough that the pattern of field variation could be mapped directly using MRI. This allowed direct validation of theoretical expressions describing the pattern of field variation from anisotropic structures with biologically relevant shapes (slabs, spherical shells and cylindrical shells).
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15
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Langkammer C, Schweser F, Shmueli K, Kames C, Li X, Guo L, Milovic C, Kim J, Wei H, Bredies K, Buch S, Guo Y, Liu Z, Meineke J, Rauscher A, Marques JP, Bilgic B. Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge. Magn Reson Med 2018; 79:1661-1673. [PMID: 28762243 PMCID: PMC5777305 DOI: 10.1002/mrm.26830] [Citation(s) in RCA: 127] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 06/03/2017] [Accepted: 06/17/2017] [Indexed: 01/10/2023]
Abstract
PURPOSE The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully. METHODS Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations. Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions. RESULTS Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance. CONCLUSION Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria. Magn Reson Med 79:1661-1673, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA; Clinical and Translational Science Institute, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Christian Kames
- UBC MRI Research Centre, Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - Xu Li
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Li Guo
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Carlos Milovic
- Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile; Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Jinsuh Kim
- Department of Radiology, University of Illinois at Chicago, IL, USA
| | - Hongjiang Wei
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Kristian Bredies
- Institute of Mathematics and Scientific Computing, University of Graz, Austria
| | - Sagar Buch
- The MRI Institute for Biomedical Research, Waterloo, Ontario, Canada
| | - Yihao Guo
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Zhe Liu
- Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA
| | | | - Alexander Rauscher
- UBC MRI Research Centre, Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - José P. Marques
- Donders Centre for Cognitive Neuroimaging, Radboud University, The Netherlands
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, MGH, Boston, MA, USA
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16
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Manning WJ. Review of Journal of Cardiovascular Magnetic Resonance (JCMR) 2015-2016 and transition of the JCMR office to Boston. J Cardiovasc Magn Reson 2017; 19:108. [PMID: 29284487 PMCID: PMC5747150 DOI: 10.1186/s12968-017-0423-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 12/07/2017] [Indexed: 02/06/2023] Open
Abstract
The Journal of Cardiovascular Magnetic Resonance (JCMR) is the official publication of the Society for Cardiovascular Magnetic Resonance (SCMR). In 2016, the JCMR published 93 manuscripts, including 80 research papers, 6 reviews, 5 technical notes, 1 protocol, and 1 case report. The number of manuscripts published was similar to 2015 though with a 12% increase in manuscript submissions to an all-time high of 369. This reflects a decrease in the overall acceptance rate to <25% (excluding solicited reviews). The quality of submissions to JCMR continues to be high. The 2016 JCMR Impact Factor (which is published in June 2016 by Thomson Reuters) was steady at 5.601 (vs. 5.71 for 2015; as published in June 2016), which is the second highest impact factor ever recorded for JCMR. The 2016 impact factor means that the JCMR papers that were published in 2014 and 2015 were on-average cited 5.71 times in 2016.In accordance with Open-Access publishing of Biomed Central, the JCMR articles are published on-line in the order that they are accepted with no collating of the articles into sections or special thematic issues. For this reason, over the years, the Editors have felt that it is useful to annually summarize the publications into broad areas of interest or themes, so that readers can view areas of interest in a single article in relation to each other and other recent JCMR articles. The papers are presented in broad themes with previously published JCMR papers to guide continuity of thought in the journal. In addition, I have elected to open this publication with information for the readership regarding the transition of the JCMR editorial office to the Beth Israel Deaconess Medical Center, Boston and the editorial process.Though there is an author publication charge (APC) associated with open-access to cover the publisher's expenses, this format provides a much wider distribution/availability of the author's work and greater manuscript citation. For SCMR members, there is a substantial discount in the APC. I hope that you will continue to send your high quality manuscripts to JCMR for consideration. Importantly, I also ask that you consider referencing recent JCMR publications in your submissions to the JCMR and elsewhere as these contribute to our impact factor. I also thank our dedicated Associate Editors, Guest Editors, and reviewers for their many efforts to ensure that the review process occurs in a timely and responsible manner and that the JCMR continues to be recognized as the leading publication in our field.
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Affiliation(s)
- Warren J Manning
- From the Journal of Cardiovascular Magnetic Resonance Editorial Office and the Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
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17
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Wei H, Gibbs E, Zhao P, Wang N, Cofer GP, Zhang Y, Johnson GA, Liu C. Susceptibility tensor imaging and tractography of collagen fibrils in the articular cartilage. Magn Reson Med 2017; 78:1683-1690. [PMID: 28856712 PMCID: PMC5786159 DOI: 10.1002/mrm.26882] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Revised: 07/24/2017] [Accepted: 07/30/2017] [Indexed: 12/17/2022]
Abstract
PURPOSE To investigate the B0 orientation-dependent magnetic susceptibility of collagen fibrils within the articular cartilage and to determine whether susceptibility tensor imaging (STI) can detect the 3D collagen network within cartilage. METHODS Multiecho gradient echo datasets (100-μm isotropic resolution) were acquired from fixed porcine articular cartilage specimens at 9.4 T. The susceptibility tensor was calculated using phase images acquired at 12 or 15 different orientations relative to B0 . The susceptibility anisotropy of the collagen fibril was quantified and diffusion tensor imaging (DTI) was compared against STI. 3D tractography was performed to visualize and track the collagen fibrils with DTI and STI. RESULTS STI experiments showed the distinct and significant anisotropic magnetic susceptibility of collagen fibrils within the articular cartilage. STI can be used to measure and quantify susceptibility anisotropy maps. Furthermore, STI provides orientation information of the underlying collagen network via 3D tractography. CONCLUSION The findings of this study demonstrate that STI can characterize the orientation variation of collagen fibrils where diffusion anisotropy fails. We believe that STI could serve as a sensitive and noninvasive marker to study the collagen fibrils microstructure. Magn Reson Med 78:1683-1690, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Hongjiang Wei
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Eric Gibbs
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Peida Zhao
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Nian Wang
- Center for In Vivo Microscopy, Duke University, Durham, NC, USA
| | - Gary P. Cofer
- Center for In Vivo Microscopy, Duke University, Durham, NC, USA
| | - Yuyao Zhang
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | | | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
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18
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Hutson MR, Keyte AL, Hernández-Morales M, Gibbs E, Kupchinsky ZA, Argyridis I, Erwin KN, Pegram K, Kneifel M, Rosenberg PB, Matak P, Xie L, Grandl J, Davis EE, Katsanis N, Liu C, Benner EJ. Temperature-activated ion channels in neural crest cells confer maternal fever-associated birth defects. Sci Signal 2017; 10:10/500/eaal4055. [PMID: 29018170 DOI: 10.1126/scisignal.aal4055] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Birth defects of the heart and face are common, and most have no known genetic cause, suggesting a role for environmental factors. Maternal fever during the first trimester is an environmental risk factor linked to these defects. Neural crest cells are precursor populations essential to the development of both at-risk tissues. We report that two heat-activated transient receptor potential (TRP) ion channels, TRPV1 and TRPV4, were present in neural crest cells during critical windows of heart and face development. TRPV1 antagonists protected against the development of hyperthermia-induced defects in chick embryos. Treatment with chemical agonists of TRPV1 or TRPV4 replicated hyperthermia-induced birth defects in chick and zebrafish embryos. To test whether transient TRPV channel permeability in neural crest cells was sufficient to induce these defects, we engineered iron-binding modifications to TRPV1 and TRPV4 that enabled remote and noninvasive activation of these channels in specific cellular locations and at specific developmental times in chick embryos with radio-frequency electromagnetic fields. Transient stimulation of radio frequency-controlled TRP channels in neural crest cells replicated fever-associated defects in developing chick embryos. Our data provide a previously undescribed mechanism for congenital defects, whereby hyperthermia activates ion channels that negatively affect fetal development.
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Affiliation(s)
- Mary R Hutson
- Division of Neonatology, Department of Pediatrics, Duke University Medical Center, Jean and George Brumley, Jr. Neonatal-Perinatal Institute, Durham, NC 27710, USA
| | - Anna L Keyte
- Division of Neonatology, Department of Pediatrics, Duke University Medical Center, Jean and George Brumley, Jr. Neonatal-Perinatal Institute, Durham, NC 27710, USA.,Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710, USA.,Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Miriam Hernández-Morales
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710, USA.,Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA.,Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.,Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA
| | - Eric Gibbs
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710, USA.,Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Zachary A Kupchinsky
- Center for Human Disease Modeling, Duke University Medical Center, Durham, NC 27710, USA
| | - Ioannis Argyridis
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710, USA.,Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Kyle N Erwin
- Division of Neonatology, Department of Pediatrics, Duke University Medical Center, Jean and George Brumley, Jr. Neonatal-Perinatal Institute, Durham, NC 27710, USA
| | - Kelly Pegram
- Division of Neonatology, Department of Pediatrics, Duke University Medical Center, Jean and George Brumley, Jr. Neonatal-Perinatal Institute, Durham, NC 27710, USA
| | - Margaret Kneifel
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710, USA.,Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Paul B Rosenberg
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - Pavle Matak
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Luke Xie
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710, USA.,Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Jörg Grandl
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Erica E Davis
- Center for Human Disease Modeling, Duke University Medical Center, Durham, NC 27710, USA
| | - Nicholas Katsanis
- Center for Human Disease Modeling, Duke University Medical Center, Durham, NC 27710, USA
| | - Chunlei Liu
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710, USA. .,Department of Radiology, Duke University School of Medicine, Durham, NC 27710, USA.,Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.,Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA
| | - Eric J Benner
- Division of Neonatology, Department of Pediatrics, Duke University Medical Center, Jean and George Brumley, Jr. Neonatal-Perinatal Institute, Durham, NC 27710, USA.
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19
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Zhang Y, Wei H. Atlas construction of cardiac fiber architecture using a multimodal registration approach. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.08.125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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20
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Dymerska B, Bohndorf K, Schennach P, Rauscher A, Trattnig S, Robinson SD. In vivo phase imaging of human epiphyseal cartilage at 7 T. Magn Reson Med 2017; 79:2149-2155. [PMID: 28758241 DOI: 10.1002/mrm.26858] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 07/10/2017] [Accepted: 07/11/2017] [Indexed: 12/23/2022]
Abstract
PURPOSE To assess the potential clinical utility of in vivo susceptibility-weighted imaging and quantitative susceptibility mapping of growth cartilage in the juvenile human knee at 7 T. METHODS High-resolution gradient-echo images of the knees of six healthy children and adolescents aged 6 to 15 were acquired with a 28-channel coil at 7 T. Phase images from the coils were combined using a short echo-time reference scan method (COMPOSER). RESULTS Veins oriented perpendicular to the static B0 field appeared doubled in susceptibility-weighted imaging, but not quantitative susceptibility mapping. Veins and layers in the cartilage were visible in all children up to the age of 13. CONCLUSIONS Phase imaging using susceptibility-weighted imaging and quantitative susceptibility mapping allows the in vivo visualization of veins and layers in human growth cartilage. Magn Reson Med 79:2149-2155, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Barbara Dymerska
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Klaus Bohndorf
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Paul Schennach
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Alexander Rauscher
- UBC MRI Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Siegfried Trattnig
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Simon D Robinson
- High Field Magnetic Resonance Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
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21
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Wen Y, Nguyen TD, Liu Z, Spincemaille P, Zhou D, Dimov A, Kee Y, Deh K, Kim J, Weinsaft JW, Wang Y. Cardiac quantitative susceptibility mapping (QSM) for heart chamber oxygenation. Magn Reson Med 2017; 79:1545-1552. [PMID: 28653375 DOI: 10.1002/mrm.26808] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Revised: 05/30/2017] [Accepted: 05/30/2017] [Indexed: 12/18/2022]
Abstract
PURPOSE To demonstrate the feasibility of in vivo quantitative susceptibility mapping (QSM) in cardiac MRI and to show that mixed-venous oxygen saturation (SvO2 ) can be measured non-invasively using QSM. METHODS Electrocardiographic-gated multi-echo 2D gradient echo data were collected at 1.5 T from 14 healthy volunteers during successive breath-holds. Phase wraps and fat chemical shift were removed using a graph-cut-based phase analysis and IDEAL in an iterative approach. The large susceptibility range from air in the lungs to blood in the heart was addressed by using the preconditioning approach in the dipole field inversion. SvO2 was calculated based on the difference in blood susceptibility between the right ventricle (RV) and left ventricle (LV). Cardiac QSM quality was assessed by two independent readers. RESULTS Nine out of fourteen volunteers (64%) yielded interpretable cardiac QSM. QSM maps showed strong differential contrast between RV and LV blood with RV blood having higher susceptibility values (291.5 ± 32.4 ppb), which correspond to 78.3 ± 2.3% SvO2 . CONCLUSION In vivo cardiac QSM is feasible and can be used to measure SvO2 , but improvements in data acquisition are needed. Magn Reson Med 79:1545-1552, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Yan Wen
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Zhe Liu
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | | | - Dong Zhou
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Alexey Dimov
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Youngwook Kee
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Kofi Deh
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Jiwon Kim
- Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | | | - Yi Wang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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22
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Babaei M, Jones IC, Dayal K, Mauter MS. Computing the Diamagnetic Susceptibility and Diamagnetic Anisotropy of Membrane Proteins from Structural Subunits. J Chem Theory Comput 2017; 13:2945-2953. [PMID: 28418668 DOI: 10.1021/acs.jctc.6b01251] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
The behavior of large, complex molecules in the presence of magnetic fields is experimentally challenging to measure and computationally intensive to predict. This work proposes a novel, mixed-methods approach for efficiently computing the principal magnetic susceptibilities and diamagnetic anisotropy of membrane proteins. The hierarchical primary (amino acid), secondary (α helical and β sheet), and tertiary (α helix and β barrel) structure of transmembrane proteins enables analysis of a complex molecule using discrete subunits of varying size and resolution. The proposed method converts the magnetic susceptibility tensor for all protein subunits to a unit coordinate system and sums them to build the magnetic susceptibility tensor for the membrane protein. Using this approach, we calculate the diamagnetic anisotropy for all transmembrane proteins of known structure and investigate the effect of different subunit resolutions on the resulting predictions of diamagnetic anisotropy. We demonstrate that amino acid residues with aromatic side groups exhibit higher diamagnetic anisotropies. On average, high percentages of aromatic amino acid subunits, a β barrel tertiary structure, and a small volume are correlated with high volumetric diamagnetic anisotropy. Finally, we demonstrate that accounting for the spatial position of the residues with respect to one another is critical to accurately computing the magnetic properties of the complex protein molecule.
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Affiliation(s)
- Mahnoush Babaei
- Department of Civil and Environmental Engineering, Carnegie Mellon University , 5000 Forbes Ave., Pittsburgh, Pennsylvania 15213, United States
| | - Isaac C Jones
- Department of Civil and Environmental Engineering, Carnegie Mellon University , 5000 Forbes Ave., Pittsburgh, Pennsylvania 15213, United States
| | - Kaushik Dayal
- Department of Civil and Environmental Engineering, Carnegie Mellon University , 5000 Forbes Ave., Pittsburgh, Pennsylvania 15213, United States
| | - Meagan S Mauter
- Department of Civil and Environmental Engineering, Carnegie Mellon University , 5000 Forbes Ave., Pittsburgh, Pennsylvania 15213, United States.,Department of Engineering and Public Policy, Carnegie Mellon University , 5000 Forbes Ave., Pittsburgh, Pennsylvania 15213, United States
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23
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Dibb R, Xie L, Wei H, Liu C. Magnetic susceptibility anisotropy outside the central nervous system. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3544. [PMID: 27199082 PMCID: PMC5112155 DOI: 10.1002/nbm.3544] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Revised: 03/29/2016] [Accepted: 03/30/2016] [Indexed: 06/01/2023]
Abstract
Magnetic-susceptibility-based MRI has made important contributions to the characterization of tissue microstructure, chemical composition, and organ function. This has motivated a number of studies to explore the link between microstructure and susceptibility in organs and tissues throughout the body, including the kidney, heart, and connective tissue. These organs and tissues have anisotropic magnetic susceptibility properties and cellular organizations that are distinct from the lipid organization of myelin in the brain. For instance, anisotropy is traced to the epithelial lipid orientation in the kidney, the myofilament proteins in the heart, and the collagen fibrils in the knee cartilage. The magnetic susceptibility properties of these and other tissues are quantified using specific MRI tools: susceptibility tensor imaging (STI), quantitative susceptibility mapping (QSM), and individual QSM measurements with respect to tubular and filament directions determined from diffusion tensor imaging. These techniques provide complementary and supplementary information to that produced by traditional MRI methods. In the kidney, STI can track tubules in all layers including the cortex, outer medulla, and inner medulla. In the heart, STI detected myofibers throughout the myocardium. QSM in the knee revealed three unique layers in articular cartilage by exploiting the anisotropic susceptibility features of collagen. While QSM and STI are promising tools to study tissue susceptibility, certain technical challenges must be overcome in order to realize routine clinical use. This paper reviews essential experimental findings of susceptibility anisotropy in the body, the underlying mechanisms, and the associated MRI methodologies. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Russell Dibb
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710
| | - Luke Xie
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710
- Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, Utah 84108
| | - Hongjiang Wei
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina, 27710
| | - Chunlei Liu
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina, 27710
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24
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Wei H, Zhang Y, Gibbs E, Chen NK, Wang N, Liu C. Joint 2D and 3D phase processing for quantitative susceptibility mapping: application to 2D echo-planar imaging. NMR IN BIOMEDICINE 2017; 30:e3501. [PMID: 26887812 DOI: 10.1002/nbm.3501] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Revised: 12/23/2015] [Accepted: 01/26/2016] [Indexed: 06/05/2023]
Abstract
Quantitative susceptibility mapping (QSM) measures tissue magnetic susceptibility and typically relies on time-consuming three-dimensional (3D) gradient-echo (GRE) MRI. Recent studies have shown that two-dimensional (2D) multi-slice gradient-echo echo-planar imaging (GRE-EPI), which is commonly used in functional MRI (fMRI) and other dynamic imaging techniques, can also be used to produce data suitable for QSM with much shorter scan times. However, the production of high-quality QSM maps is difficult because data obtained by 2D multi-slice scans often have phase inconsistencies across adjacent slices and strong susceptibility field gradients near air-tissue interfaces. To address these challenges in 2D EPI-based QSM studies, we present a new data processing procedure that integrates 2D and 3D phase processing. First, 2D Laplacian-based phase unwrapping and 2D background phase removal are performed to reduce phase inconsistencies between slices and remove in-plane harmonic components of the background phase. This is followed by 3D background phase removal for the through-plane harmonic components. The proposed phase processing was evaluated with 2D EPI data obtained from healthy volunteers, and compared against conventional 3D phase processing using the same 2D EPI datasets. Our QSM results were also compared with QSM values from time-consuming 3D GRE data, which were taken as ground truth. The experimental results show that this new 2D EPI-based QSM technique can produce quantitative susceptibility measures that are comparable with those of 3D GRE-based QSM across different brain regions (e.g. subcortical iron-rich gray matter, cortical gray and white matter). This new 2D EPI QSM reconstruction method is implemented within STI Suite, which is a comprehensive shareware for susceptibility imaging and quantification. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Hongjiang Wei
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
| | - Yuyao Zhang
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
| | - Eric Gibbs
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
| | - Nan-Kuei Chen
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Radiology, School of Medicine, Duke University, Durham, NC, USA
| | - Nian Wang
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
| | - Chunlei Liu
- Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Radiology, School of Medicine, Duke University, Durham, NC, USA
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25
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Deistung A, Schweser F, Reichenbach JR. Overview of quantitative susceptibility mapping. NMR IN BIOMEDICINE 2017; 30:e3569. [PMID: 27434134 DOI: 10.1002/nbm.3569] [Citation(s) in RCA: 184] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 05/03/2016] [Accepted: 05/09/2016] [Indexed: 06/06/2023]
Abstract
Magnetic susceptibility describes the magnetizability of a material to an applied magnetic field and represents an important parameter in the field of MRI. With the recently introduced method of quantitative susceptibility mapping (QSM) and its conceptual extension to susceptibility tensor imaging (STI), the non-invasive assessment of this important physical quantity has become possible with MRI. Both methods solve the ill-posed inverse problem to determine the magnetic susceptibility from local magnetic fields. Whilst QSM allows the extraction of the spatial distribution of the bulk magnetic susceptibility from a single measurement, STI enables the quantification of magnetic susceptibility anisotropy, but requires multiple measurements with different orientations of the object relative to the main static magnetic field. In this review, we briefly recapitulate the fundamental theoretical foundation of QSM and STI, as well as computational strategies for the characterization of magnetic susceptibility with MRI phase data. In the second part, we provide an overview of current methodological and clinical applications of QSM with a focus on brain imaging. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Andreas Deistung
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, The State University of New York at Buffalo, NY, USA
- MRI Clinical and Translational Research Center, Jacobs School of Medicine and Biomedical Sciences, The State University of New York at Buffalo, NY, USA
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany
- Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University Jena, Jena, Germany
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26
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Ropele S, Langkammer C. Iron quantification with susceptibility. NMR IN BIOMEDICINE 2017; 30:e3534. [PMID: 27119601 DOI: 10.1002/nbm.3534] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Revised: 02/19/2016] [Accepted: 03/11/2016] [Indexed: 05/26/2023]
Abstract
Iron is an essential trace element involved in a variety of biological mechanisms in the human body. Disturbances of iron homeostasis have been observed in several inflammatory and degenerative diseases, which have raised strong interest in non-invasive iron mapping techniques. Numerous MRI techniques have been proposed so far, mostly based on the field changes induced by the magnetic properties of iron. Each of these approaches has a specific sensitivity for iron and its microstructural environment. Quantitative susceptibility mapping is the latest development and provides a direct measure of bulk susceptibility. However, field changes induced by iron are not always directly related to the concentration of iron, but rather reflect the structure of iron compounds and its cellular distribution. This review provides an overview of the most relevant iron compounds in the human body, their magnetic properties and their cellular distribution. In addition, MRI methods based on direct or indirect susceptibility changes are presented and discussed with respect to technical aspects and clinical applicability. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
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27
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Wei H, Dibb R, Decker K, Wang N, Zhang Y, Zong X, Lin W, Nissman DB, Liu C. Investigating magnetic susceptibility of human knee joint at 7 Tesla. Magn Reson Med 2017; 78:1933-1943. [PMID: 28097689 DOI: 10.1002/mrm.26596] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Revised: 12/06/2016] [Accepted: 12/12/2016] [Indexed: 11/07/2022]
Abstract
PURPOSE To evaluate the magnetic susceptibility properties of different anatomical structures within the knee joint using quantitative susceptibility mapping (QSM). METHODS A collagen tissue model was simulated and ex vivo animal cartilage experiments were conducted at 9.4 Tesla (T) to evaluate the B0 orientation-dependent magnetic susceptibility contrast observed in cartilage. Furthermore, nine volunteers (six healthy subjects without knee pain history and three patients with known knee injury, between 29 and 58 years old) were scanned using gradient-echo acquisitions on a high-field 7T MR scanner. Susceptibility values of different tissues were quantified and diseased cartilage and meniscus were compared against that of healthy volunteers. RESULTS Simulation and ex vivo animal cartilage experiments demonstrated that collagen fibrils exhibit an anisotropic susceptibility. A gradual change of magnetic susceptibility was observed in the articular cartilage from the superficial zone to the deep zone, forming a multilayer ultrastructure consistent with anisotropy of collagen fibrils. Meniscal tears caused a clear reduction of susceptibility contrast between the injured meniscus and surrounding cartilage illustrated by a loss of the sharp boundaries between the two. Moreover, QSM showed more dramatic contrast in the focal degenerated articular cartilage than R2* mapping. CONCLUSION The arrangement of the collagen fibrils is significant, and likely the most dominant source of magnetic susceptibility anisotropy. Quantitative susceptibility mapping offers a means to characterize magnetic susceptibility properties of tissues in the knee joint. It is sensitive to collagen damage or degeneration and may be useful for evaluating the status of knee diseases, such as meniscal tears and cartilage disease. Magn Reson Med 78:1933-1943, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Hongjiang Wei
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | - Russell Dibb
- Center for In Vivo Microscopy, Duke University, Durham, North Carolina, USA
| | - Kyle Decker
- Center for In Vivo Microscopy, Duke University, Durham, North Carolina, USA
| | - Nian Wang
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA
| | - Yuyao Zhang
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | - Xiaopeng Zong
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Weili Lin
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Daniel B Nissman
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Chunlei Liu
- Brain Imaging and Analysis Center, Duke University, Durham, North Carolina, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
- Department of Radiology, School of Medicine, Duke University, Durham, North Carolina, USA
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28
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Pennell DJ, Baksi AJ, Prasad SK, Mohiaddin RH, Alpendurada F, Babu-Narayan SV, Schneider JE, Firmin DN. Review of Journal of Cardiovascular Magnetic Resonance 2015. J Cardiovasc Magn Reson 2016; 18:86. [PMID: 27846914 PMCID: PMC5111217 DOI: 10.1186/s12968-016-0305-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 11/02/2016] [Indexed: 12/14/2022] Open
Abstract
There were 116 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2015, which is a 14 % increase on the 102 articles published in 2014. The quality of the submissions continues to increase. The 2015 JCMR Impact Factor (which is published in June 2016) rose to 5.75 from 4.72 for 2014 (as published in June 2015), which is the highest impact factor ever recorded for JCMR. The 2015 impact factor means that the JCMR papers that were published in 2013 and 2014 were cited on average 5.75 times in 2015. The impact factor undergoes natural variation according to citation rates of papers in the 2 years following publication, and is significantly influenced by highly cited papers such as official reports. However, the progress of the journal's impact over the last 5 years has been impressive. Our acceptance rate is <25 % and has been falling because the number of articles being submitted has been increasing. In accordance with Open-Access publishing, the JCMR articles go on-line as they are accepted with no collating of the articles into sections or special thematic issues. For this reason, the Editors have felt that it is useful once per calendar year to summarize the papers for the readership into broad areas of interest or theme, so that areas of interest can be reviewed in a single article in relation to each other and other recent JCMR articles. The papers are presented in broad themes and set in context with related literature and previously published JCMR papers to guide continuity of thought in the journal. We hope that you find the open-access system increases wider reading and citation of your papers, and that you will continue to send your quality papers to JCMR for publication.
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Affiliation(s)
- D. J. Pennell
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - A. J. Baksi
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - S. K. Prasad
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - R. H. Mohiaddin
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - F. Alpendurada
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - S. V. Babu-Narayan
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - J. E. Schneider
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
| | - D. N. Firmin
- Cardiovascular Magnetic Resonance Unit, Royal Brompton & Harefield NHS Foundation Trust, Sydney Street, London, SW 3 6NP UK
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29
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Xie L, Bennett KM, Liu C, Johnson GA, Zhang JL, Lee VS. MRI tools for assessment of microstructure and nephron function of the kidney. Am J Physiol Renal Physiol 2016; 311:F1109-F1124. [PMID: 27630064 DOI: 10.1152/ajprenal.00134.2016] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 09/08/2016] [Indexed: 12/13/2022] Open
Abstract
MRI can provide excellent detail of renal structure and function. Recently, novel MR contrast mechanisms and imaging tools have been developed to evaluate microscopic kidney structures including the tubules and glomeruli. Quantitative MRI can assess local tubular function and is able to determine the concentrating mechanism of the kidney noninvasively in real time. Measuring single nephron function is now a near possibility. In parallel to advancing imaging techniques for kidney microstructure is a need to carefully understand the relationship between the local source of MRI contrast and the underlying physiological change. The development of these imaging markers can impact the accurate diagnosis and treatment of kidney disease. This study reviews the novel tools to examine kidney microstructure and local function and demonstrates the application of these methods in renal pathophysiology.
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Affiliation(s)
- Luke Xie
- Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, Utah;
| | - Kevin M Bennett
- Department of Biology, University of Hawaii at Manoa, Honolulu, Hawaii
| | - Chunlei Liu
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina; and.,Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina
| | - G Allan Johnson
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, North Carolina; and
| | - Jeff Lei Zhang
- Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, Utah
| | - Vivian S Lee
- Utah Center for Advanced Imaging Research, Department of Radiology, University of Utah, Salt Lake City, Utah
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30
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Dibb R, Liu C. Joint eigenvector estimation from mutually anisotropic tensors improves susceptibility tensor imaging of the brain, kidney, and heart. Magn Reson Med 2016; 77:2331-2346. [PMID: 27385561 DOI: 10.1002/mrm.26321] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 05/18/2016] [Accepted: 06/02/2016] [Indexed: 01/29/2023]
Abstract
PURPOSE To develop a susceptibility-based MRI technique for probing microstructure and fiber architecture of magnetically anisotropic tissues-such as central nervous system white matter, renal tubules, and myocardial fibers-in three dimensions using susceptibility tensor imaging (STI) tools. THEORY AND METHODS STI can probe tissue microstructure, but is limited by reconstruction artifacts because of absent phase information outside the tissue and noise. STI accuracy may be improved by estimating a joint eigenvector from mutually anisotropic susceptibility and relaxation tensors. Gradient-recalled echo image data were simulated using a numerical phantom and acquired from the ex vivo mouse brain, kidney, and heart. Susceptibility tensor data were reconstructed using STI, regularized STI, and the proposed algorithm of mutually anisotropic and joint eigenvector STI (MAJESTI). Fiber map and tractography results from each technique were compared with diffusion tensor data. RESULTS MAJESTI reduced the estimated susceptibility tensor orientation error by 30% in the phantom, 36% in brain white matter, 40% in the inner medulla of the kidney, and 45% in myocardium. This improved the continuity and consistency of susceptibility-based fiber tractography in each tissue. CONCLUSION MAJESTI estimation of the susceptibility tensors yields lower orientation errors for susceptibility-based fiber mapping and tractography in the intact brain, kidney, and heart. Magn Reson Med 77:2331-2346, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Russell Dibb
- Center for In Vivo Microscopy, Duke University Medical Center, Durham, North Carolina, USA.,Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Chunlei Liu
- Center for In Vivo Microscopy, Duke University Medical Center, Durham, North Carolina, USA.,Biomedical Engineering, Duke University, Durham, North Carolina, USA.,Brain Imaging & Analysis Center, Duke University Medical Center, Durham, North Carolina, USA.,Radiology, Duke University Medical Center, Durham, North Carolina, USA
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31
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Huelnhagen T, Hezel F, Serradas Duarte T, Pohlmann A, Oezerdem C, Flemming B, Seeliger E, Prothmann M, Schulz-Menger J, Niendorf T. Myocardial effective transverse relaxation time T2* Correlates with left ventricular wall thickness: A 7.0 T MRI study. Magn Reson Med 2016; 77:2381-2389. [PMID: 27342430 DOI: 10.1002/mrm.26312] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 05/23/2016] [Accepted: 05/25/2016] [Indexed: 12/13/2022]
Abstract
PURPOSE Myocardial effective relaxation time T2* is commonly regarded as a surrogate for myocardial tissue oxygenation. However, it is legitimate to assume that there are multiple factors that influence T2*. To this end, this study investigates the relationship between T2* and cardiac macromorphology given by left ventricular (LV) wall thickness and left ventricular radius, and provides interpretation of the results in the physiological context. METHODS High spatio-temporally resolved myocardial CINE T2* mapping was performed in 10 healthy volunteers using a 7.0 Tesla (T) full-body MRI system. Ventricular septal wall thickness, left ventricular inner radius, and T2* were analyzed. Macroscopic magnetic field changes were elucidated using cardiac phase-resolved magnetic field maps. RESULTS Ventricular septal T2* changes periodically over the cardiac cycle, increasing in systole and decreasing in diastole. Ventricular septal wall thickness and T2* showed a significant positive correlation, whereas the inner LV radius and T2* were negatively correlated. The effect of macroscopic magnetic field gradients on T2* can be considered minor in the ventricular septum. CONCLUSION Our findings suggest that myocardial T2* is related to tissue blood volume fraction. Temporally resolved T2* mapping could be beneficial for myocardial tissue characterization and for understanding cardiac (patho)physiology in vivo. Magn Reson Med 77:2381-2389, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Till Huelnhagen
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Fabian Hezel
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Teresa Serradas Duarte
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Andreas Pohlmann
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Celal Oezerdem
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Bert Flemming
- Institute of Physiology, Charité University Medicine, Berlin, Germany
| | - Erdmann Seeliger
- Institute of Physiology, Charité University Medicine, Berlin, Germany
| | - Marcel Prothmann
- Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine.,DZHK (German Centre for Cardiovascular Research), partner site Berlin, Germany
| | - Jeanette Schulz-Menger
- Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine.,DZHK (German Centre for Cardiovascular Research), partner site Berlin, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine, Berlin, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Berlin, Germany
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32
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Liu C, Wei H, Gong NJ, Cronin M, Dibb R, Decker K. Quantitative Susceptibility Mapping: Contrast Mechanisms and Clinical Applications. ACTA ACUST UNITED AC 2015; 1:3-17. [PMID: 26844301 PMCID: PMC4734903 DOI: 10.18383/j.tom.2015.00136] [Citation(s) in RCA: 122] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Quantitative susceptibility mapping (QSM) is a recently developed magnetic resonance imaging (MRI) technique for quantifying the spatial distribution of magnetic susceptibility within biological tissues. It first uses the frequency shift in the MRI signal to map the magnetic field profile within the tissue. The resulting field map is then used to determine the spatial distribution of the underlying magnetic susceptibility by solving an inverse problem. The solution is achieved by deconvolving the field map with a dipole field, under the assumption that the magnetic field results from a superposition of the dipole fields generated by all voxels and that each voxel has its own unique magnetic susceptibility. QSM provides an improved contrast-to-noise ratio for certain tissues and structures compared with its magnitude counterpart. More importantly, magnetic susceptibility directly reflects the molecular composition and cellular architecture of the tissue. Consequently, by quantifying magnetic susceptibility, QSM is becoming a quantitative imaging approach for characterizing normal and pathological tissue properties. This article reviews the mechanism that generates susceptibility contrast within tissues and some associated applications.
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Affiliation(s)
- Chunlei Liu
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710; Department of Radiology, Duke University School of Medicine, Durham, NC 27710; Center for In Vivo Microscopy, Duke University School of Medicine, Durham, NC 27710
| | - Hongjiang Wei
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710
| | - Nan-Jie Gong
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710
| | - Matthew Cronin
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC 27710
| | - Russel Dibb
- Center for In Vivo Microscopy, Duke University School of Medicine, Durham, NC 27710
| | - Kyle Decker
- Center for In Vivo Microscopy, Duke University School of Medicine, Durham, NC 27710
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