Patents on Quantitative Susceptibility Mapping (QSM) of Tissue Magnetism

Author(s): Feng Lin, Martin R. Prince, Pascal Spincemaille, Yi Wang*.

Journal Name: Recent Patents on Biotechnology

Volume 13 , Issue 2 , 2019

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Graphical Abstract:


Abstract:

Background: Quantitative susceptibility mapping (QSM) depicts biodistributions of tissue magnetic susceptibility sources, including endogenous iron and calcifications, as well as exogenous paramagnetic contrast agents and probes. When comparing QSM with simple susceptibility weighted MRI, QSM eliminates blooming artifacts and shows reproducible tissue susceptibility maps independent of field strength and scanner manufacturer over a broad range of image acquisition parameters. For patient care, QSM promises to inform diagnosis, guide surgery, gauge medication, and monitor drug delivery. The Bayesian framework using MRI phase data and structural prior knowledge has made QSM sufficiently robust and accurate for routine clinical practice.

Objective: To address the lack of a summary of US patents that is valuable for QSM product development and dissemination into the MRI community.

Method: We searched the USPTO Full-Text and Image Database for patents relevant to QSM technology innovation. We analyzed the claims of each patent to characterize the main invented method and we investigated data on clinical utility.

Results: We identified 17 QSM patents; 13 were implemented clinically, covering various aspects of QSM technology, including the Bayesian framework, background field removal, numerical optimization solver, zero filling, and zero-TE phase.

Conclusion: Our patent search identified patents that enable QSM technology for imaging the brain and other tissues. QSM can be applied to study a wide range of diseases including neurological diseases, liver iron disorders, tissue ischemia, and osteoporosis. MRI manufacturers can develop QSM products for more seamless integration into existing MRI scanners to improve medical care.

Keywords: Quantitative susceptibility mapping, MRI, magnetic susceptibility, tissue magnetism, biodistribution, Bayesian framework.

[1]
de Rochefort L, Liu T, Kressler B, et al. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging. Magn Reson Med 2010; 63(1): 194-206.
[2]
Wang Y, Liu T. Quantitative susceptibility mapping (QSM): decoding MRI data for a tissue magnetic biomarker. Magn Reson Med 2015; 73(1): 82-101.
[3]
Liu C, Wei H, Gong NJ, et al. Quantitative susceptibility mapping: contrast mechanisms and clinical applications. Tomography 2015; 1(1): 3-17.
[4]
Haacke EM, Liu S, Buch S, et al. Quantitative susceptibility mapping: current status and future directions. Magn Reson Imaging 2015; 33(1): 1-25.
[5]
Reichenbach JR, Schweser F, Serres B, Deistung A. Quantitative susceptibility mapping: concepts and applications. Clin Neuroradiol 2015; 2: 225-30.
[6]
Deistung A, Schweser F, Reichenbach JR. Overview of quantitative susceptibility mapping. NMR Biomed 2017; 30(4) [Epub Ahead of Print].
[7]
Kee Y, Liu Z, Zhou L, et al. Quantitative susceptibility mapping (qsm) algorithms: mathematical rationale and computational implementations. IEEE Trans Biomed Eng 2017; 64(11): 2531-45.
[8]
Liu S, Buch S, Chen Y, et al. Susceptibility-weighted imaging: current status and future directions. NMR Biomed 2017; 30(4): e3552.
[9]
Li W, Liu C, Duong TQ, van Zijl PC, Li X. Susceptibility tensor imaging (STI) of the brain. NMR Biomed 2017; 30(4): e3540.
[10]
Ropele S, Langkammer C. Iron quantification with susceptibility. NMR Biomed 2017; 30(4): 1671-5.
[11]
Schweser F, Deistung A, Reichenbach JR. Foundations of mri phase imaging and processing for quantitative susceptibility mapping (QSM). Z Med Phys 2016; 26(1): 6-34.
[12]
Stuber C, Pitt D, Wang Y. Iron in multiple sclerosis and its noninvasive imaging with quantitative susceptibility mapping. Int J Mol Sci 2016; 17(1): 100.
[13]
Duyn JH, Schenck J. Contributions to magnetic susceptibility of brain tissue. NMR Biomed 2017; 30(4): e3546.
[14]
Yablonskiy DA, Sukstanskii AL. Effects of biological tissue structural anisotropy and anisotropy of magnetic susceptibility on the gradient echo MRI signal phase: theoretical background. NMR Biomed 2017; 30(4) [Epub Ahead of Print].
[15]
Borins S. Encouraging innovation in the public sector. J Intellect Cap 2001; 2(3): 310-9.
[16]
Wang Y. Principles of Magnetic Resonance Imaging: physics concepts, pulse sequences & biomedical applications. North Charleston, SC: CreateSpace Publishing 2012.
[17]
Bloembergen N, Purcell EM, Pound RV. Relaxation effects in nuclear magnetic resonance absorption. Phys Rev 1948; 73(7): 679-712.
[18]
Pines D, Slichter CP. Relaxation times in magnetic resonance. Phys Rev 1955; 100(4): 1014-20.
[19]
Hahn EL. Spin echoes. Phys Rev 1950; 80(4): 580.
[20]
Yablonskiy DA, Haacke EM. Theory of NMR signal behavior in magnetically inhomogeneous tissues: the static dephasing regime. Magn Reson Med 1994; 32(6): 749-63.
[21]
Wang Y. Quantitative susceptibility mapping (QSM). North Charleston, SC: CreateSpace Publishing 2013.
[22]
Lorentz HA. The theory of electrons and its applications to the phenomena of light and radiant heat. 2nd ed. Mineola, NY: Dover Publications 2003.
[23]
Li J, Chang S, Liu T, et al. Reducing the object orientation dependence of susceptibility effects in gradient echo MRI through quantitative susceptibility mapping. Magn Reson Med 2012; 68(5): 1563-9.
[24]
Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci USA 1990; 87(24): 9868-72.
[25]
Rosen BR, Belliveau JW, Vevea JM, Brady TJ. Perfusion imaging with NMR contrast agents. Magn Reson Med 1990; 14(2): 249-65.
[26]
Haacke EM, Reichenbach JR. Susceptibility weighted imaging in MRI: basic concepts and clinical applications. Hoboken, NJ: Wiley-Blackwell 2011.
[27]
de Crespigny AJ, Roberts TP, Kucharcyzk J, Moseley ME. Improved sensitivity to magnetic susceptibility contrast. Magn Reson Med 1993; 30(1): 135-7.
[28]
Haacke EM, Xu Y, Cheng YC, Reichenbach JR. Susceptibility weighted imaging (SWI). Magn Reson Med 2004; 52(3): 612-8.
[29]
Moonen CTW. Method to enhance the sensitivity of MRI for magnetic susceptibility effects. US5300886 1994.
[30]
Haacke EM. Susceptibility weighted imaging. US6658280, 2003.
[31]
Kressler B, de Rochefort L, Spincemaille P, Liu T, Wang Y. Estimation of sparse magnetic susceptibility distributions from MRI using non-linear regularization. New York, USA: International Society for Magnetic Resonance in Medicine 2008.
[32]
Kressler B, de Rochefort L, Liu T, et al. Nonlinear regularization for per voxel estimation of magnetic susceptibility distributions from MRI field maps. IEEE Trans Med Imaging 2010; 29(2): 273-81.
[33]
Sepulveda NG, Thomas IM, Wikswo JP. Magnetic susceptibility tomography for three-dimensional imaging of diamagnetic and paramagnetic objects. IEEE Trans Magn 1994; 30: 5062-9.
[34]
Li L, Leigh JS. Quantifying arbitrary magnetic susceptibility distributions with MR. Magn Reson Med 2004; 51(5): 1077-82.
[35]
Haacke EM, Cheng NY, House MJ, et al. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging 2005; 23(1): 1-25.
[36]
de Rochefort L, Brown R, Prince MR, Wang Y. Quantitative MR susceptibility mapping using piece-wise constant regularized inversion of the magnetic field. Magn Reson Med 2008; 60(4): 1003-9.
[37]
Liu T, Spincemaille P, de Rochefort L, Kressler B, Wang Y. Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magn Reson Med 2009; 61(1): 196-204.
[38]
Wang Y, de Rochefort L, Liu T, Kressler B. Magnetic source MRI: a new quantitative imaging of magnetic biomarkers. Conf Proc IEEE Eng Med Biol Soc 2009; 2009: 53-6.
[39]
Shmueli K, de Zwart JA, van Gelderen P, et al. Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data. Magn Reson Med 2009; 62(6): 1510-22.
[40]
Wharton S, Schafer A, Bowtell R. Susceptibility mapping in the human brain using threshold-based k-space division. Magn Reson Med 2010; 63(5): 1292-304.
[41]
de Rochefort L, Nguyen T, Brown R, et al. In vivo quantification of contrast agent concentration using the induced magnetic field for time-resolved arterial input function measurement with MRI. Med Phys 2008; 35(12): 5328-39.
[42]
Choi JK, Park HS, Wang S, Wang Y, Seo JK. Inverse problem in quantitative susceptibility mapping. SIAM J Imaging Sci 2014; 7(3): 1669-89.
[43]
Kee Y, Liu Z, Zhou L, et al. Quantitative susceptibility mapping (QSM) algorithms: mathematical rationale and computational implementations. IEEE Trans Biomed Eng 2017; 64(11): 2531-45.
[44]
Zhou L, Choi JK, Kee Y, Wang Y, Seo JK. Dipole incompatibility related artifacts in quantitative susceptibility mapping Available from: http: //adsabs. harvard.edu/abs/2017arXiv170105457Z
[45]
Wang Y, de Rochefort L, Kressler B, Liu T, Spincemaille P. P. Tool for accurate quantification in molecular MRI. US8781197, 2014.
[46]
Liu Z, Spincemaille P, Yao Y, Zhang Y, Wang Y. MEDI+0: morphology enabled dipole inversion with automatic uniform cerebrospinal fluid zero reference for quantitative susceptibility mapping. Magn Reson Med 2018; 79(5): 2795-803.
[47]
Liu Z, Yao Y, Zhang Y, Wang Y. MEDI+0: morphology enabled dipole inversion with automatic uniform cerebrospinal fluid zero reference for quantitative susceptibility mapping: QSM with automatic uniform csf zero reference. Magn Reson Med 2017; 79(5): 2795-803.
[48]
Wen Y, Nguyen TD, Liu Z, et al. Cardiac quantitative susceptibility mapping (QSM) for heart chamber oxygenation. Magn Reson Med 2018; 79(3): 1545-52.
[49]
Li J, Lin H, Liu T, et al. Quantitative susceptibility mapping (QSM) minimizes interference from cellular pathology in R2* estimation of liver iron concentration. J Magn Reson Imaging 2018; 48(4): 1069-79.
[50]
Nocedal J, Wright SJ. Numerical optimization. 2nd ed. Salmon Tower Building, NY: Springer 2006.
[51]
Liu J, Liu T, de Rochefort L, et al. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map. Neuroimage 2012; 59(3): 2560-8.
[52]
Goldstein T, Osher S. The split bregman method for L1-regularized problems. SIAM J Imaging Sci 2009; 2(2): 323-43.
[53]
Allison MJ, Ramani S, Fessler JA. Accelerated regularized estimation of MR coil sensitivities using augmented Lagrangian methods. IEEE Trans Med Imaging 2013; 32(3): 556-64.
[54]
Milovic C, Bilgic B, Zhao B, Acosta-Cabronero J, Tejos C. Fast nonlinear susceptibility inversion with variational regularization. Magn Reson Med 2018; 80(2): 814-21.
[55]
Lim IA, Faria AV, Li X, et al. Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: application to determine iron content in deep gray matter structures. Neuroimage 2013; 82: 449-69.
[56]
Acosta-Cabronero J, Williams GB, Cardenas-Blanco A, et al. In vivo quantitative susceptibility mapping (QSM) in Alzheimer’s disease. PLoS One 2013; 8(11): e81093.
[57]
Bilgic B, Pfefferbaum A, Rohlfing T, Sullivan EV, Adalsteinsson E. MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping. Neuroimage 2012; 59(3): 2625-35.
[58]
Persson N, Wu J, Zhang Q, et al. Age and sex related differences in subcortical brain iron concentrations among healthy adults. Neuroimage 2015; 122: 385-98.
[59]
Zhang Y, Wei H, Cronin MJ, et al. Longitudinal data for magnetic susceptibility of normative human brain development and aging over the lifespan. Data Brief 2018; 20: 623-31.
[60]
Tiepolt S, Schafer A, Rullmann M, et al. Quantitative susceptibility mapping of amyloid-beta aggregates in Alzheimer’s disease with 7t mr. J Alzheimers Dis 2018; 64(2): 393-404.
[61]
Langkammer C, Schweser F, Shmueli K, et al. Quantitative susceptibility mapping: report from the 2016 reconstruction challenge. Magn Reson Med 2017; 79(3): 1661-73.
[62]
Wang Y, de Rochefort L, Liu T, Khalidov I. Background field removal method for MRI using projection onto dipole fields. US9448289, 2016.
[63]
Liu Z, Kee Y, Zhou D, Wang Y, Spincemaille P. Preconditioned total field inversion (TFI) method for quantitative susceptibility mapping. Magn Reson Med 2017; 78(1): 303-15.
[64]
Sun H, Klahr AC, Kate M, et al. Quantitative susceptibility mapping for following intracranial hemorrhage. Radiology 2018; 288(3): 830-9.
[65]
Eskreis-Winkler S, Zhang Y, Zhang J, et al. The clinical utility of QSM: disease diagnosis, medical management, and surgical planning. NMR Biomed 2017; 30(4): e3668.
[66]
Wang Y, Spincemaille P, Liu Z, et al. Clinical quantitative susceptibility mapping (QSM): biometal imaging and its emerging roles in patient care. J Magn Reson Imaging 2017; 46(4): 951-71.
[67]
Chen W, Zhu W, Kovanlikaya I, et al. Intracranial calcifications and hemorrhages: characterization with quantitative susceptibility mapping. Radiology 2014; 270(2): 496-505.
[68]
Ciraci S, Gumus K, Doganay S, et al. Diagnosis of intracranial calcification and hemorrhage in pediatric patients: comparison of quantitative susceptibility mapping and phase images of susceptibility-weighted imaging. Diagn Interv Imaging 2017; 98(10): 707-14.
[69]
Deistung A, Schweser F, Wiestler B, et al. Quantitative susceptibility mapping differentiates between blood depositions and calcifications in patients with glioblastoma. PLoS One 2013; 8(3): e57924.
[70]
Schweser F, Deistung A, Lehr BW, Reichenbach JR. Differentiation between diamagnetic and paramagnetic cerebral lesions based on magnetic susceptibility mapping. Med Phys 2010; 37(10): 5165-78.
[71]
Bandt SK, de Rochefort L, Chen W, et al. Clinical integration of quantitative susceptibility mapping (QSM) MRI into neurosurgical practice. World Neurosurg 2018; 122: 10-9.
[72]
Surapaneni K, Horenstein C, Liu T, et al. Quantitative susceptibility mapping of intracranial tumors: correlation with histologic grade. Annual Meeting of ISMRM. Montreal, Canada. 2011.
[73]
Rasouli J, Ramdhani R, Panov FE, et al. Utilization of quantitative susceptibility mapping for direct targeting of the subthalamic nucleus during deep brain stimulation surgery. Oper Neurosurg 2017; 14(1): 412-9.
[74]
Dimov AV, Gupta A, Kopell BH, Wang Y. High-resolution QSM for functional and structural depiction of subthalamic nuclei in DBS presurgical mapping. J Neurosurg 2018; 1: 1-8.
[75]
Deistung A, Schafer A, Schweser F, et al. Toward in vivo histology: a comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2*-imaging at ultra-high magnetic field strength. Neuroimage 2013; 65: 299-314.
[76]
Schafer A, Forstmann BU, Neumann J, et al. Direct visualization of the subthalamic nucleus and its iron distribution using high-resolution susceptibility mapping. Hum Brain Mapp 2012; 33(12): 2831-42.
[77]
Liu T, Eskreis-Winkler S, Schweitzer AD, et al. Improved subthalamic nucleus depiction with quantitative susceptibility mapping. Radiology 2013; 269(1): 216-23.
[78]
Chandran AS, Bynevelt M, Lind CR. Magnetic resonance imaging of the subthalamic nucleus for deep brain stimulation. J Neurosurg 2016; 124(1): 96-105.
[79]
Alkemade A, de Hollander G, Keuken MC, et al. Comparison of T2*-weighted and QSM contrasts in Parkinson’s disease to visualize the STN with MRI. PLoS One 2017; 12(4): e0176130.
[80]
Mehta V, Pei W, Yang G, et al. Iron is a sensitive biomarker for inflammation in multiple sclerosis lesions. PLoS One 2013; 8(3): e57573.
[81]
Hametner S, Wimmer I, Haider L, et al. Iron and neurodegeneration in the multiple sclerosis brain. Ann Neurol 2013; 74(6): 848-61.
[82]
Wisnieff C, Ramanan S, Olesik J, et al. Quantitative susceptibility mapping (QSM) of white matter multiple sclerosis lesions: Interpreting positive susceptibility and the presence of iron. Magn Reson Med 2015; 74(2): 564-70.
[83]
Dal-Bianco A, Grabner G, Kronnerwetter C, et al. Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging. Acta Neuropathol 2017; 133(1): 25-42.
[84]
Harrison DM, Li X, Liu H, et al. Lesion Heterogeneity on High-Field Susceptibility MRI Is Associated with Multiple Sclerosis Severity. AJNR Am J Neuroradiol 2016; 37(8): 1447-53.
[85]
Eskreis-Winkler S, Deh K, Gupta A, et al. Multiple sclerosis lesion geometry in quantitative susceptibility mapping (QSM) and phase imaging. J Magn Reson Imaging 2015; 42(1): 224-9.
[86]
Cronin MJ, Wharton S, Al-Radaideh A, et al. A comparison of phase imaging and quantitative susceptibility mapping in the imaging of multiple sclerosis lesions at ultrahigh field. MAGMA 2016; 29(3): 543-57.
[87]
Yao Y, Nguyen TD, Pandya S, et al. Combining quantitative susceptibility mapping with automatic zero reference (QSM0) and myelin water fraction imaging to quantify iron-related myelin damage in chronic active MS lesions. AJNR Am J Neuroradiol 2018; 39(2): 303-10.
[88]
Zivadinov R, Tavazzi E, Bergsland N, et al. Brain iron at quantitative MRI is associated with disability in multiple sclerosis. Radiology 2018; 289(2): 487-96.
[89]
Deh K, Ponath GD, Molvi Z, et al. Magnetic susceptibility increases as diamagnetic molecules breakdown: myelin digestion during multiple sclerosis lesion formation contributes to increase on QSM. J Magn Reson Imaging 2018; 48(5): 1281-7.
[90]
Chen W, Gauthier SA, Gupta A, et al. Quantitative susceptibility mapping of multiple sclerosis lesions at various ages. Radiology 2014; 271(1): 183-92.
[91]
Zhang Y, Gauthier SA, Gupta A, et al. Longitudinal change in magnetic susceptibility of new enhanced multiple sclerosis (MS) lesions measured on serial quantitative susceptibility mapping (QSM). J Magn Reson Imaging 2016; 44(2): 426-32.
[92]
Zhang Y, Gauthier SA, Gupta A, et al. Magnetic susceptibility from quantitative susceptibility mapping can differentiate new enhancing from nonenhancing multiple sclerosis lesions without gadolinium injection. AJNR Am J Neuroradiol 2016; 37(10): 1794-9.
[93]
Zhang S, Nguyen TD, Zhao Y, et al. Diagnostic accuracy of semiautomatic lesion detection plus quantitative susceptibility mapping in the identification of new and enhancing multiple sclerosis lesions. Neuroimage Clin 2018; 18: 143-8.
[94]
McDonald RJ, McDonald JS, Kallmes DF, et al. Intracranial gadolinium deposition after contrast-enhanced MRI imaging. Radiology 2015; 275(3): 772-82.
[95]
Radbruch A, Weberling LD, Kieslich PJ, et al. High-signal intensity in the dentate nucleus and globus pallidus on unenhanced T1-weighted images: evaluation of the macrocyclic Gadolinium-based contrast agent gadobutrol. Invest Radiol 2015; 50(12): 805-10.
[96]
Ramalho J, Castillo M, AlObaidy M, et al. High signal intensity in globus pallidus and dentate nucleus on unenhanced T1-weighted mr images: evaluation of two linear gadolinium-based contrast agents. Radiology 2015; 276(3): 836-44.
[97]
Kanda T, Matsuda M, Oba H, Toyoda K, Furui S. Gadolinium deposition after contrast-enhanced mr imaging. Radiology 2015; 277(3): 924-5.
[98]
Zhang Y, Cao Y, Shih GL, Hecht EM, Prince MR. Extent of signal hyperintensity on unenhanced t1-weighted brain MR images after more than 35 administrations of linear gadolinium-based contrast agents. Radiology 2016; 282(2): 516-25.
[99]
Tanaka M, Nakahara K, Kinoshita M. Increased signal intensity in the dentate nucleus of patients with multiple sclerosis in comparison with neuromyelitis optica spectrum disorder after multiple doses of gadolinium contrast. Eur Neurol 2016; 75(3-4): 195-8.
[100]
Tedeschi E, Palma G, Canna A, et al. In vivo dentate nucleus MRI relaxometry correlates with previous administration of Gadolinium-based contrast agents. Eur Radiol 2016; 26(12): 4577-84.
[101]
Roccatagliata L, Vuolo L, Bonzano L, Pichiecchio A, Mancardi GL. Multiple sclerosis: hyperintense dentate nucleus on unenhanced T1-weighted MR images is associated with the secondary progressive subtype. Radiology 2009; 251(2): 503-10.
[102]
Lassmann H, van Horssen J, Mahad D. Progressive multiple sclerosis: pathology and pathogenesis. Nat Rev Neurol 2012; 8(11): 647-56.
[103]
Desai RA, Davies AL, Tachrount M, et al. Cause and prevention of demyelination in a model multiple sclerosis lesion. Ann Neurol 2016; 79(4): 591-604.
[104]
El-Hammadi MM, Arias JL. Iron oxide-based multifunctional nanoparticulate systems for biomedical applications: a patent review (2008 - present). Expert Opin Ther Pat 2015; 25(6): 691-709.
[105]
Gupta AK, Gupta M. Synthesis and surface engineering of iron oxide nanoparticles for biomedical applications. Biomaterials 2005; 26(18): 3995-4021.
[106]
Laurent S, Forge D, Port M, et al. Magnetic iron oxide nanoparticles: synthesis, stabilization, vectorization, physicochemical characterizations, and biological applications. Chem Rev 2008; 108(6): 2064-110.
[107]
Marshall D, Johnell O, Wedel H. Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ 1996; 312(7041): 1254-9.
[108]
Sharma SD, Hernando D, Horng DE, Reeder SB. Quantitative susceptibility mapping in the abdomen as an imaging biomarker of hepatic iron overload. Magn Reson Med 2015; 74(3): 673-83.
[109]
Olivieri NF, Brittenham GM. Iron-chelating therapy and the treatment of thalassemia. Blood 1997; 89(3): 739-61.
[110]
Belaidi AA, Bush AI. Iron neurochemistry in Alzheimer’s disease and Parkinson’s disease: targets for therapeutics. J Neurochem 2016; 139(1): 179-97.
[111]
Ayton S, Fazlollahi A, Bourgeat P, et al. Cerebral quantitative susceptibility mapping predicts amyloid-beta-related cognitive decline. Brain 2017; 140(8): 2112-9.
[112]
Devos D, Moreau C, Devedjian JC, et al. Targeting chelatable iron as a therapeutic modality in Parkinson’s disease. Antioxid Redox Signal 2014; 21(2): 195-210.
[113]
Brittenham GM. Iron-chelating therapy for transfusional iron overload. N Engl J Med 2011; 364(2): 146-56.
[114]
Deh K, Nguyen TD, Eskreis-Winkler S, et al. Reproducibility of quantitative susceptibility mapping in the brain at two field strengths from two vendors. J Magn Reson Imaging 2015; 42(6): 1592-600.
[115]
Hinoda T, Fushimi Y, Okada T, et al. Quantitative susceptibility mapping at 3 T and 1.5 T: Evaluation of consistency and reproducibility. Invest Radiol 2015; 50(8): 522-30.
[116]
Lin PY, Chao TC, Wu ML. Quantitative susceptibility mapping of human brain at 3T: a multisite reproducibility study. AJNR Am J Neuroradiol 2015; 36(3): 467-74.
[117]
Santin MD, Didier M, Valabregue R, et al. Reproducibility of R2* and quantitative susceptibility mapping (QSM) reconstruction methods in the basal ganglia of healthy subjects. NMR Biomed 2017; 30(4): e3491.
[118]
Robinson SD, Bredies K, Khabipova D, et al. An illustrated comparison of processing methods for MR phase imaging and QSM: combining array coil signals and phase unwrapping. NMR Biomed 2017; 30(4) [Epub Ahead of Print].
[119]
Grossman GM, Helpman E. Innovation and growth in the global economy. Reprint ed. Cambridge, MA:MIT Press. 1993.
[120]
Colaianni A, Cook-Deegan R. Columbia University’s axel patents: technology transfer and implications for the Bayh-Dole Act. Milbank Q 2009; 87(3): 683-715.
[121]
Mills RL. Magnetic susceptibility imaging (MSI). US5073858, 1991.
[122]
Mills RL. . Resonant magnetic susceptibility imaging(ReMSI). US6477398, 2002.
[123]
Liu C. Systems and methods for susceptibility tensor imaging. US8447089, 2013.
[124]
Liu C. . Systems and methods for susceptibility tensor imaging. US9383423 2016.
[125]
Liu C, Li W, Wu B, Jiang Y, Johnson GA. 3D fiber tractography with susceptibility tensor imaging. Neuroimage 2012; 59(2): 1290-8.
[126]
Li X, Vikram DS, Lim IA, et al. Mapping magnetic susceptibility anisotropies of white matter in vivo in the human brain at 7 T. Neuroimage 2012; 62(1): 314-30.
[127]
Wisnieff C, Liu T, Spincemaille P, et al. Magnetic susceptibility anisotropy: cylindrical symmetry from macroscopically ordered anisotropic molecules and accuracy of MRI measurements using few orientations. Neuroimage 2013; 70: 363-76.
[128]
Wang S, Liu T, Chen W, et al. Noise Effects in various quantitative susceptibility mapping methods. IEEE Trans Biomed Eng 2013; 60(12): 3441-8.
[129]
Liu T. System, process and computer-accessible medium for providing quantitative susceptibility mapping.US9213076, 2015.
[130]
Sato R, Shirai T, Taniguchi Y, Ochi H, Bito Y. Magnetic resonance imaging apparatus, image processing apparatus, and susceptibility map calculation method.US9709641, 2017.
[131]
Sharma SD, Artz NS, Reeder SB. System and method for object-based initialization of magnetic field inhomogeneity in magnetic resonance imaging.US9612300, 2017.
[132]
Dimov AV, Liu T, Spincemaille P, et al. Joint estimation of chemical shift and quantitative susceptibility mapping (chemical QSM). Magn Reson Med 2015; 73(6): 2100-10.
[133]
Dimov AV, Liu Z, Spincemaille P, et al. Bone quantitative susceptibility mapping using a chemical species-specific R2* signal model with ultrashort and conventional echo data. Magn Reson Med 2018; 79(1): 121-8.
[134]
Bauer F, Gutting M, Lukas MA. Evaluation of parameter choice methods for regularization of ill-posed problems in geomathematics. In: Freeden W, Nashed M, Sonar T, Eds Handbook of geomathematics Berlin, Heidelberg: Springer. 2013; pp 1-55.
[135]
Liu T, Khalidov I, de Rochefort L, et al. A novel background field removal method for MRI using projection onto dipole fields (PDF). NMR Biomed 2011; 24(9): 1129-36.
[136]
Schweser F, Deistung A, Lehr BW, Reichenbach JR. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism? Neuroimage 2011; 54(4): 2789-807.
[137]
Liu C, Li W, Wu B. Systems and methods for imaging and quantifying tissue magnetism with magnetic resonance imaging. US9285449 2016.
[138]
Zhou D, Liu T, Spincemaille P, Wang Y. Background field removal by solving the laplacian boundary value problem. NMR Biomed 2014; 27(3): 312-9.
[139]
Schweser F, Robinson SD, de Rochefort L, Li W, Bredies K. An illustrated comparison of processing methods for phase MRI and QSM: removal of background field contributions from sources outside the region of interest. NMR Biomed 2017; 30(4): e3604.
[140]
Fortier V, Levesque IR. Phase processing for quantitative susceptibility mapping of regions with large susceptibility and lack of signal. Magn Reson Med 2018; 79(6): 3103-13.
[141]
Chen Z, Calhoun VD. 3D and 4D magnetic susceptibility tomography based on complex MR images.US8886283, 2014.
[142]
Bilgic B, Setsompop K. Systems and methods for fast reconstruction for quantitative susceptibility mapping using magnetic resonance imaging. US9542763, 2017.
[143]
Parker DL, Du YP, Davis WL. The voxel sensitivity function in Fourier transform imaging: applications to magnetic resonance angiography. Magn Reson Med 1995; 33(2): 156-62.
[144]
Eskreis-Winkler S, Zhou D, Liu T, et al. On the influence of zero-padding on the nonlinear operations in Quantitative Susceptibility Mapping. Magn Reson Imaging 2016; 35: 154-9.
[145]
Katscher U, Voigt T, Findeklee C, et al. Determination of electric conductivity and local SAR via B1 mapping. IEEE Trans Med Imaging 2009; 28(9): 1365-74.
[146]
Seo JK, Woo EJ, Katscher U, Wang Y. Electro-magnetic tissue properties MRI IEEE T Bio-Med Eng 2014; 61(5): 1390-9.
[147]
Kim D-H, Choi N, Gho S-M, Ghim M, Lee J. Apparatus and method for conductivity and susceptibility reconstruction. US9632155, 2017.
[148]
Krauss JB, Kuttenkeuler D. Intellectual property rights derived from academic research and their role in the modern bioeconomy-A guide for scientists. N Biotechno 2018. 40(Pt A): 133-9
[149]
Liu C, Li W, Tong KA, Yeom KW, Kuzminski S. Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging 2015; 42(1): 23-41.
[150]
Benabid AL, Chabardes S, Mitrofanis J, Pollak P. Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson’s disease. Lancet Neurol 2009; 8(1): 67-81.
[151]
de Hollander G, Keuken MC, Bazin PL, et al. A gradual increase of iron toward the medial-inferior tip of the subthalamic nucleus. Hum Brain Mapp 2014; 35(9): 4440-9.
[152]
Rasouli J, Ramdhani R, Panov FE, et al. Utilization of Quantitative Susceptibility Mapping for direct targeting of the subthalamic nucleus during deep brain stimulation surgery. Oper Neurosurg (Hagerstown) 2018; 14(4): 412-9.
[153]
Nam Y, Gho SM, Kim DH, Kim EY, Lee J. Imaging of nigrosome 1 in substantia nigra at 3T using multiecho susceptibility map-weighted imaging (SMWI). J Magn Reson Imaging 2017; 46(2): 528-36.
[154]
Guan X, Xuan M, Gu Q, et al. Influence of regional iron on the motor impairments of Parkinson’s disease: A quantitative susceptibility mapping study. J Magn Reson Imaging 2017; 45(5): 1335-42.
[155]
He N, Huang P, Ling H, et al. Dentate nucleus iron deposition is a potential biomarker for tremor-dominant Parkinson’s disease. NMR Biomed 2017; 30(4): e3554.
[156]
Guan X, Xuan M, Gu Q, et al. Regionally progressive accumulation of iron in Parkinson’s disease as measured by quantitative susceptibility mapping. NMR Biomed 2017; 30(4): e3489.
[157]
Schweser F, Raffaini Duarte Martins AL, Hagemeier J, et al. Mapping of thalamic magnetic susceptibility in multiple sclerosis indicates decreasing iron with disease duration: a proposed mechanistic relationship between inflammation and oligodendrocyte vitality. Neuroimage 2018; 167: 438-52.
[158]
Hagemeier J, Zivadinov R, Dwyer MG, et al. Changes of deep gray matter magnetic susceptibility over 2 years in multiple sclerosis and healthy control brain. Neuroimage Clin 2018; 18: 1007-16.
[159]
Hagemeier J, Ramanathan M, Schweser F, et al. Iron-related gene variants and brain iron in multiple sclerosis and healthy individuals. Neuroimage Clin 2018; 17: 530-40.
[160]
Gillen KM, Mubarak M, Nguyen TD, Pitt D. Significance and in vivo detection of iron-laden Microglia in white matter multiple sclerosis lesions. Front Immunol 2018; 9: 255.
[161]
Chawla S, Kister I, Sinnecker T, et al. Longitudinal study of multiple sclerosis lesions using ultra-high field (7T) multiparametric MR imaging. PLoS One 2018; 13(9): e0202918.
[162]
Wiggermann V, Hametner S, Hernandez-Torres E, et al. Susceptibility-sensitive MRI of multiple sclerosis lesions and the impact of normal-appearing white matter changes. NMR Biomed 2017; 30(8)
[163]
Pontillo G, Cocozza S, Lanzillo R, et al. Brain susceptibility changes in a patient with natalizumab-related progressive multifocal Leukoencephalopathy: a longitudinal quantitative susceptibility mapping and relaxometry study. Front Neurol 2017; 8: 294.
[164]
Li X, Harrison DM, Liu H, et al. Magnetic susceptibility contrast variations in multiple sclerosis lesions. J Magn Reson Imaging 2016; 43(2): 463-73.
[165]
Carra-Dalliere C, Menjot de Champfleur N, Deverdun J, et al. Use of quantitative susceptibility mapping (QSM) in progressive multifocal leukoencephalopathy. J Neuroradiol 2016; 43(1): 6-10.
[166]
Sun H, Walsh AJ, Lebel RM, et al. Validation of quantitative susceptibility mapping with Perls’ iron staining for subcortical gray matter. Neuroimage 2015; 105: 486-92.
[167]
Kakeda S, Futatsuya K, Ide S, et al. Improved Detection of Cortical Gray Matter Involvement in Multiple Sclerosis with Quantitative Susceptibility Mapping. Acad Radiol 2015; 22(11): 1427-32.
[168]
Cobzas D, Sun H, Walsh AJ, et al. Subcortical gray matter segmentation and voxel-based analysis using transverse relaxation and quantitative susceptibility mapping with application to multiple sclerosis. J Magn Reson Imaging 2015; 42(6): 1601-10.
[169]
Langkammer C, Liu T, Khalil M, et al. Quantitative susceptibility mapping in multiple sclerosis. Radiology 2013; 267(2): 551-9.
[170]
Stuber C, Dimov A, Deh K, et al. Combining QSM and MWF in multiple sclerosis: a marker for the inflammatory state of MS lesions? ISMRM 25th Annual Meeting & Exhibition. Honolulu, HI 2017
[171]
Khaled W, Piraquive J, Leporq B, et al. In vitro distinction between proinflammatory and antiinflammatory macrophages with gadolinium-liposomes and ultrasmall superparamagnetic iron oxide particles at 3.0T. J Magn Reson Imaging 2018; 3. [Epub ahead of print].
[172]
Yao S, Zhong Y, Xu Y, et al. Quantitative susceptibility mapping reveals an association between brain iron load and depression severity. Front Hum Neurosci 2017; 11: 442.
[173]
Absinta M, Sati P, Schindler M, et al. Persistent 7-tesla phase rim predicts poor outcome in new multiple sclerosis patient lesions. J Clin Invest 2016; 126(7): 2597-609.
[174]
Gupta A, Al-Dasuqi K, Xia F, et al. The use of noncontrast quantitative MRI to detect gadolinium-enhancing multiple sclerosis brain lesions: a systematic review and meta-analysis. AJNR Am J Neuroradiol 2017; 38(7): 1317-22.
[175]
Zecca L, Youdim MB, Riederer P, Connor JR, Crichton RR. Iron, brain ageing and neurodegenerative disorders. Nat Rev Neurosci 2004; 5(11): 863-73.
[176]
Rouault TA. Iron metabolism in the CNS: implications for neurodegenerative diseases. Nat Rev Neurosci 2013; 14(8): 551-64.
[177]
Ayton S, Lei P. Nigral iron elevation is an invariable feature of Parkinson’s disease and is a sufficient cause of neurodegeneration. BioMed Res Int 2014; 2014: 581256.
[178]
Abbruzzese G, Cossu G, Balocco M, et al. A pilot trial of deferiprone for neurodegeneration with brain iron accumulation. Haematologica 2011; 96(11): 1708-11.
[179]
Ward RJ, Dexter DT, Crichton RR. Neurodegenerative diseases and therapeutic strategies using iron chelators. J Trace Elem Med Biol 2015; 31: 267-73.
[180]
Meineke J, Wenzel F, De Marco M, et al. Motion artifacts in standard clinical setting obscure disease-specific differences in quantitative susceptibility mapping. Phys Med Biol 2018; 63(14): 14NT01.
[181]
Du L, Zhao Z, Cui A, et al. Increased iron deposition on brain quantitative susceptibility mapping correlates with decreased cognitive function in Alzheimer’s disease. ACS Chem Neurosci 2018; 9(7): 1849-57.
[182]
Van Bergen JMG, Li X, Quevenco FC, et al. Simultaneous quantitative susceptibility mapping and Flutemetamol-PET suggests local correlation of iron and beta-amyloid as an indicator of cognitive performance at high age. Neuroimage 2018; 174: 308-16.
[183]
Kim HG, Park S, Rhee HY, et al. Quantitative susceptibility mapping to evaluate the early stage of Alzheimer’s disease. Neuroimage Clin 2017; 16: 429-38.
[184]
O’Callaghan J, Holmes H, Powell N, et al. Tissue magnetic susceptibility mapping as a marker of tau pathology in Alzheimer’s disease. Neuroimage 2017; 159: 334-45.
[185]
Van Bergen JM, Li X, Hua J, et al. Colocalization of cerebral iron with Amyloid beta in mild cognitive impairment. Sci Rep 2016; 6: 35514.
[186]
Hwang EJ, Kim HG, Kim D, et al. Texture analyses of quantitative susceptibility maps to differentiate Alzheimer’s disease from cognitive normal and mild cognitive impairment. Med Phys 2016; 43(8): 4718.
[187]
Moon Y, Han SH, Moon WJ. Patterns of brain iron accumulation in vascular dementia and alzheimer’s dementia using quantitative susceptibility mapping imaging. J Alzheimers Dis 2016; 51(3): 737-45.
[188]
Poynton CB, Jenkinson M, Adalsteinsson E, et al. Quantitative susceptibility mapping by inversion of a perturbation field model: correlation with brain iron in normal aging. IEEE Trans Med Imaging 2015; 34(1): 339-53.
[189]
Klohs J, Politano IW, Deistung A, et al. Longitudinal assessment of amyloid pathology in transgenic arcabeta mice using multi-parametric magnetic resonance imaging. PLoS One 2013; 8(6): e66097.
[190]
Klohs J, Deistung A, Schweser F, et al. Detection of cerebral microbleeds with quantitative susceptibility mapping in the ArcAbeta mouse model of cerebral amyloidosis. J Cereb Blood Flow Metab 2011; 31(12): 2282-92.
[191]
Sethi SK, Kisch SJ, Ghassaban K, et al. Iron quantification in Parkinson’s disease using an age-based threshold on susceptibility maps: the advantage of local versus entire structure iron content measurements. Magn Reson Imaging 2019; 55: 145-52.
[192]
Li DTH, Hui ES, Chan Q, et al. Quantitative susceptibility mapping as an indicator of subcortical and limbic iron abnormality in Parkinson’s disease with dementia. Neuroimage Clin 2018; 20: 365-73.
[193]
Guan JJ, Feng YQ. Quantitative magnetic resonance imaging of brain iron deposition: comparison between quantitative susceptibility mapping and transverse relaxation rate (R2*) mapping. Nan Fang Yi Ke Da Xue Xue Bao 2018; 38(3): 305-11.
[194]
Kim EY, Sung YH, Shin HG, et al. Diagnosis of early-stage idiopathic parkinson’s disease using high-resolution quantitative susceptibility mapping combined with histogram analysis in the substantia nigra at 3 T. J Clin Neurol 2018; 14(1): 90-7.
[195]
Guo T, Song Y, Li J, et al. Seed point discontinuity-based segmentation method for the substantia nigra and the red nucleus in quantitative susceptibility maps. J Magn Reson Imaging 2018; 48(4): 1112-9.
[196]
Shin C, Lee S, Lee JY, Rhim JH, Park SW. Non-motor symptom burdens are not associated with iron accumulation in early parkinson’s disease: a quantitative susceptibility mapping study. J Korean Med Sci 2018; 33(13): e96.
[197]
Takahashi H, Watanabe Y, Tanaka H, et al. Quantifying changes in nigrosomes using quantitative susceptibility mapping and neuromelanin imaging for the diagnosis of early-stage Parkinson’s disease. Br J Radiol 2018; 91(1086): 20180037.
[198]
Guan X, Huang P, Zeng Q, et al. Quantitative susceptibility mapping as a biomarker for evaluating white matter alterations in Parkinson’s disease. Brain Imaging Behav 2018; 7: 1-12.
[199]
An H, Zeng X, Niu T, et al. Quantifying iron deposition within the substantia nigra of Parkinson’s disease by quantitative susceptibility mapping. J Neurol Sci 2018; 386: 46-52.
[200]
Lee H, Baek SY, Chun SY, Lee JH, Cho H. Specific visualization of neuromelanin-iron complex and ferric iron in the human post-mortem substantia nigra using MR relaxometry at 7T. Neuroimage 2018; 172: 874-85.
[201]
Sjostrom H, Granberg T, Westman E, Svenningsson P. Quantitative susceptibility mapping differentiates between parkinsonian disorders. Parkinsonism Relat Disord 2017; 44: 51-7.
[202]
Ito K, Ohtsuka C, Yoshioka K, et al. Differential diagnosis of parkinsonism by a combined use of diffusion kurtosis imaging and quantitative susceptibility mapping. Neuroradiology 2017; 59(8): 759-69.
[203]
Langkammer C, Pirpamer L, Seiler S, et al. Quantitative susceptibility mapping in parkinson’s disease. PLoS One 2016; 11(9): e0162460.
[204]
Santin MD, Didier M, Valabregue R, et al. Reproducibility of R2* and quantitative susceptibility mapping (QSM) reconstruction methods in the basal ganglia of healthy subjects. NMR Biomed 2017; 30(4): e3491.
[205]
He N, Ling H, Ding B, et al. Region-specific disturbed iron distribution in early idiopathic Parkinson’s disease measured by quantitative susceptibility mapping. Hum Brain Mapp 2015; 36(11): 4407-20.
[206]
Barbosa JH, Santos AC, Tumas V, et al. Quantifying brain iron deposition in patients with Parkinson’s disease using quantitative susceptibility mapping, R2 and R2. Magn Reson Imaging 2015; 33(5): 559-65.
[207]
Ide S, Kakeda S, Ueda I, et al. Internal structures of the globus pallidus in patients with Parkinson’s disease: evaluation with quantitative susceptibility mapping (QSM). Eur Radiol 2015; 25(3): 710-8.
[208]
Dominguez JF, Ng AC, Poudel G, et al. Iron accumulation in the basal ganglia in Huntington’s disease: cross-sectional data from the IMAGE-HD study. J Neurol Neurosurg Psychiatry 2016; 87(5): 545-9.
[209]
Chen L, Hua J, Ross CA, et al. Altered brain iron content and deposition rate in Huntington’s disease as indicated by quantitative susceptibility MRI. J Neurosci Res 2018; 97(4): 467-79.
[210]
Schweitzer AD, Liu T, Gupta A, et al. Quantitative susceptibility mapping of the motor cortex in amyotrophic lateral sclerosis and primary lateral sclerosis. AJR Am J Roentgenol 2015; 204(5): 1086-92.
[211]
Costagli M, Donatelli G, Biagi L, et al. Magnetic susceptibility in the deep layers of the primary motor cortex in Amyotrophic Lateral Sclerosis. Neuroimage Clin 2016; 12: 965-9.
[212]
Lee JY, Lee YJ, Park DW, et al. Quantitative susceptibility mapping of the motor cortex: a comparison of susceptibility among patients with amyotrophic lateral sclerosis, cerebrovascular disease, and healthy controls. Neuroradiology 2017; 59(12): 1213-22.
[213]
Acosta-Cabronero J, Machts J, Schreiber S, et al. Quantitative susceptibility MRI to detect brain iron in amyotrophic lateral sclerosis. Radiology 2018; 289(1): 195-203.
[214]
Weidman EK, Schweitzer AD, Niogi SN, et al. Diffusion tensor imaging and quantitative susceptibility mapping as diagnostic tools for motor neuron disorders. Clin Imaging 2018; 53: 6-11.
[215]
Fritzsch D, Reiss-Zimmermann M, Trampel R, et al. Seven-tesla magnetic resonance imaging in Wilson disease using quantitative susceptibility mapping for measurement of copper accumulation. Invest Radiol 2014; 49(5): 299-306.
[216]
Doganay S, Gumus K, Koc G, et al. Magnetic susceptibility changes in the basal ganglia and brain stem of patients with Wilson’s disease: evaluation with quantitative susceptibility mapping. Magn Reson Med Sci 2018; 17(1): 73-9.
[217]
Saracoglu S, Gumus K, Doganay S, et al. Brain susceptibility changes in neurologically asymptomatic pediatric patients with Wilson’s disease: evaluation with quantitative susceptibility mapping. Acta Radiol 2018; 59(11): 1380-5.
[218]
Zaino D, Chiarotti I, Battisti C, et al. Six-year clinical and MRI quantitative susceptibility mapping (QSM) follow-up in neurological Wilson’s disease under zinc therapy: a case report. Neurol Sci 2018; 40(1): 199-201.
[219]
Hu S, Zhu WZ, Kovanlikaya I, et al. The application value of quantitative susceptibility mapping in grading meningiomas. Annual Meeting of ISMRM. Milan, Italy. 2014.
[220]
Zhang J, Cho J, Zhou D, et al. Quantitative susceptibility mapping-based cerebral metabolic rate of oxygen mapping with minimum local variance. Magn Reson Med 2018; 79(1): 172-9.
[221]
Miyata M, Kakeda S, Kudo K, et al. Evaluation of oxygen extraction fraction in systemic lupus erythematosus patients using quantitative susceptibility mapping. J Cereb Blood Flow Metab 2018; 49. [Epub Ahead of Print].
[222]
Leatherday C, Dehkharghani S, Nahab F, et al. cerebral mr oximetry during acetazolamide augmentation: beyond cerebrovascular reactivity in hemodynamic failure. J Magn Reson Imaging 2018; 49. [Epub Ahead of Print].
[223]
Cho J, Kee Y, Spincemaille P, et al. Cerebral metabolic rate of oxygen (CMRO2) mapping by combining quantitative susceptibility mapping (QSM) and quantitative blood oxygenation level-dependent imaging (qBOLD). Magn Reson Med 2018; 80(4): 1595-604.
[224]
Chai C, Liu S, Fan L, et al. Reduced deep regional cerebral venous oxygen saturation in hemodialysis patients using quantitative susceptibility mapping. Metab Brain Dis 2018; 33(1): 313-23.
[225]
Zhang J, Zhou D, Nguyen TD, et al. Cerebral metabolic rate of oxygen (CMRO2) mapping with hyperventilation challenge using quantitative susceptibility mapping (QSM). Magn Reson Med 2017; 77(5): 1762-73.
[226]
Ward PG, Fan AP, Raniga P, et al. Improved quantification of cerebral vein oxygenation using partial volume correction. Front Neurosci 2017; 11: 89.
[227]
Uwano I, Kudo K, Sato R, et al. Noninvasive assessment of oxygen extraction fraction in chronic ischemia using quantitative susceptibility mapping at 7 tesla. Stroke 2017; 48(8): 2136-41.
[228]
Hsieh MC, Kuo LW, Huang YA, Chen JH. Investigating hyperoxic effects in the rat brain using quantitative susceptibility mapping based on MRI phase. Magn Reson Med 2017; 77(2): 592-602.
[229]
Chai C, Guo R, Zuo C, et al. Decreased susceptibility of major veins in mild traumatic brain injury is correlated with post-concussive symptoms: a quantitative susceptibility mapping study. Neuroimage Clin 2017; 15: 625-32.
[230]
Ozbay PS, Warnock G, Rossi C, et al. Probing neuronal activation by functional quantitative susceptibility mapping under a visual paradigm: a group level comparison with BOLD fMRI and PET. Neuroimage 2016; 137: 52-60.
[231]
Kudo K, Liu T, Murakami T, et al. Oxygen extraction fraction measurement using quantitative susceptibility mapping: Comparison with positron emission tomography. J Cereb Blood Flow Metab 2016; 36(8): 1424-33.
[232]
Hsieh MC, Tsai CY, Liao MC, et al. Quantitative susceptibility mapping-based microscopy of magnetic resonance venography (qsm-mmrv) for in vivo morphologically and functionally assessing cerebromicrovasculature in rat stroke model. PLoS One 2016; 11(3): e0149602.
[233]
Fan AP, Schafer A, Huber L, et al. Baseline oxygenation in the brain: correlation between respiratory-calibration and susceptibility methods. Neuroimage 2016; 125: 920-31.
[234]
Zhang Z, Liu J, Zhou S, Kou Z. [Advances in clinical application of quantitative susceptibility mapping in central nervous system]. Zhong Nan Da Xue Xue Bao Yi Xue Ban 2015; 40(7): 816-9.
[235]
Zhang J, Liu T, Gupta A, et al. Quantitative mapping of cerebral metabolic rate of oxygen (CMRO2) using quantitative susceptibility mapping (QSM). Magn Reson Med 2015; 74(4): 945-52.
[236]
Ozbay PS, Rossi C, Kocian R, et al. Effect of respiratory hyperoxic challenge on magnetic susceptibility in human brain assessed by quantitative susceptibility mapping (QSM). NMR Biomed 2015; 28(12): 1688-96.
[237]
Xu B, Liu T, Spincemaille P, Prince M, Wang Y. Flow compensated quantitative susceptibility mapping for venous oxygenation imaging. Magn Reson Med 2014; 72(2): 438-45.
[238]
Xia S, Utriainen D, Tang J, et al. Decreased oxygen saturation in asymmetrically prominent cortical veins in patients with cerebral ischemic stroke. Magn Reson Imaging 2014; 32(10): 1272-6.
[239]
Fan AP, Bilgic B, Gagnon L, et al. Quantitative oxygenation venography from MRI phase. Magn Reson Med 2014; 72(1): 149-59.
[240]
Fan AP, Evans KC, Stout JN, Rosen BR, Adalsteinsson E. Regional quantification of cerebral venous oxygenation from MRI susceptibility during hypercapnia. Neuroimage 2015; 104: 146-55.
[241]
Buch S, Ye Y, Haacke EM. Quantifying the changes in oxygen extraction fraction and cerebral activity caused by caffeine and acetazolamide. J Cereb Blood Flow Metab 2017; 37(3): 825-36.
[242]
Wehrli FW, Fan AP, Rodgers ZB, Englund EK, Langham MC. Susceptibility-based time-resolved whole-organ and regional tissue oximetry. NMR Biomed 2017; 30(4): e3495.
[243]
Sun H, Seres P, Wilman AH. Structural and functional quantitative susceptibility mapping from standard fMRI studies. NMR Biomed 2017; 30(4): e3619.
[244]
Balla DZ, Sanchez-Panchuelo RM, Wharton SJ, et al. Functional quantitative susceptibility mapping (fQSM). Neuroimage 2014; 100: 112-24.
[245]
Liu T, Wisnieff C, Lou M, et al. Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping. Magn Reson Med 2013; 69(2): 467-76.
[246]
Wang S, Lou M, Liu T, et al. Hematoma volume measurement in gradient echo MRI using quantitative susceptibility mapping. Stroke 2013; 44(8): 2315-7.
[247]
Wei H, Dibb R, Zhou Y, et al. Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range. NMR Biomed 2015; 28(10): 1294-303.
[248]
Chang S, Zhang J, Liu T, et al. Quantitative susceptibility mapping of intracerebral hemorrhages at various stages. J Magn Reson Imaging 2016; 44(2): 420-5.
[249]
Sun H, Kate M, Gioia LC, et al. Quantitative susceptibility mapping using a superposed dipole inversion method: Application to intracranial hemorrhage. Magn Reson Med 2016; 76(3): 781-91.
[250]
Liu T, Surapaneni K, Lou M, et al. Cerebral microbleeds: burden assessment by using quantitative susceptibility mapping. Radiology 2012; 262(1): 269-78.
[251]
Liu S, Neelavalli J, Cheng YC, Tang J, Mark Haacke E. Quantitative susceptibility mapping of small objects using volume constraints. Magn Reson Med 2013; 69(3): 716-23.
[252]
Charidimou A, Krishnan A, Werring DJ, Rolf Jager H. Cerebral microbleeds: a guide to detection and clinical relevance in different disease settings. Neuroradiology 2013; 55(6): 655-74.
[253]
Gho SM, Liu C, Li W, et al. Susceptibility map-weighted imaging (SMWI) for neuroimaging. Magn Reson Med 2014; 72(2): 337-46.
[254]
Kaaouana T, de Rochefort L, Samaille T, et al. 2D harmonic filtering of MR phase images in multicenter clinical setting: toward a magnetic signature of cerebral microbleeds. Neuroimage 2015; 104: 287-300.
[255]
Liu W, Soderlund K, Senseney JS, et al. Imaging cerebral microhemorrhages in military service members with chronic traumatic brain injury. Radiology 2016; 278(2): 536-45.
[256]
Nakagawa D, Cushing C, Nagahama Y, Allan L, Hasan D. Quantitative susceptibility mapping as a possible tool to radiographically diagnose sentinel headache associated with intracranial aneurysm: case report World Neurosurg 2017. 103: 954 1- 4
[257]
Mikati AG, Tan H, Shenkar R, et al. Dynamic permeability and quantitative susceptibility: related imaging biomarkers in cerebral cavernous malformations. Stroke 2014; 45(2): 598-601.
[258]
Tan H, Liu T, Wu Y, et al. Evaluation of iron content in human cerebral cavernous malformation using quantitative susceptibility mapping. Invest Radiol 2014; 49(7): 498-504.
[259]
Awad AJ, Bederson JB, Mocco J, Raj T. 154 Expression quantitative trait locus analysis from primary immune cells identifies novel regulatory effects underlying intracranial aneurysms susceptibility. Neurosurgery 2016; 63: 162.
[260]
Tan H, Zhang L, Mikati AG, et al. Quantitative susceptibility mapping in cerebral cavernous malformations: clinical correlations. AJNR Am J Neuroradiol 2016; 37(7): 1209-15.
[261]
Girard R, Fam MD, Zeineddine HA, et al. Vascular permeability and iron deposition biomarkers in longitudinal follow-up of cerebral cavernous malformations. J Neurosurg 2017; 127(1): 102-10.
[262]
Zeineddine HA, Girard R, Cao Y, et al. Quantitative susceptibility mapping as a monitoring biomarker in cerebral cavernous malformations with recent hemorrhage. J Magn Reson Imaging 2017; 47(4): 1133-8.
[263]
Simchick G, Liu Z, Nagy T, Xiong M, Zhao Q. Assessment of MR-based R2* and quantitative susceptibility mapping for the quantification of liver iron concentration in a mouse model at 7T. Magn Reson Med 2018; 80(5): 2081-93.
[264]
Liu S, Wang C, Zhang X, et al. Quantification of liver iron concentration using the apparent susceptibility of hepatic vessels. Quant Imaging Med Surg 2018; 8(2): 123-34.
[265]
Lin H, Wei H, He N, et al. Quantitative susceptibility mapping in combination with water-fat separation for simultaneous liver iron and fat fraction quantification. Eur Radiol 2018; 28(8): 3494-504.
[266]
Li J, Lin H, Liu T, et al. Quantitative susceptibility mapping (QSM) minimizes interference from cellular pathology in R2* estimation of liver iron concentration. J Magn Reson Imaging 2018; 48(4): 1069-79.
[267]
Finnerty E, Ramasawmy R, O’Callaghan J, et al. Noninvasive quantification of oxygen saturation in the portal and hepatic veins in healthy mice and those with colorectal liver metastases using QSM MRI. Magn Reson Med 2018; 81(4): 2666-75.
[268]
Sharma SD, Fischer R, Schoennagel BP, et al. MRI-based quantitative susceptibility mapping (QSM) and R2* mapping of liver iron overload: comparison with SQUID-based biomagnetic liver susceptometry. Magn Reson Med 2017; 78(1): 264-70.
[269]
Dong J, Liu T, Chen F, et al. Simultaneous phase unwrapping and removal of chemical shift (SPURS) using graph cuts: application in quantitative susceptibility mapping. IEEE Trans Med Imaging 2015; 34(2): 531-40.
[270]
Wong R, Chen X, Wang Y, Hu X, Jin MM. Visualizing and quantifying acute inflammation using ICAM-1 specific nanoparticles and MRI quantitative susceptibility mapping. Ann Biomed Eng 2012; 40(6): 1328-38.
[271]
Hussain S, Gollan JL, Semelka RC. Liver MRI: correlation with other imaging modalities and histopathology. Berlin, Heidelberg: Springer 2007. [E book]
[272]
Sharma SD, Fischer R, Schoennagel BP, et al. MRI-based quantitative susceptibility mapping (QSM) and R2* mapping of liver iron overload: comparison with SQUID-based biomagnetic liver susceptometry. Magn Reson Med 2017; 78(1): 264-70.
[273]
Li J, Song Q, Liu T, et al. Quantitative susceptibility mapping (QSM) overcomes R2* confounding factors for measuring liver iron. Annual Meeting of ISMRM. Honolulu, Hawaii. 2017.
[274]
St Pierre TG, Clark PR, Chua-anusorn W, et al. Noninvasive measurement and imaging of liver iron concentrations using proton magnetic resonance. Blood 2005; 105(2): 855-61.
[275]
St Pierre TG, El-Beshlawy A, Elalfy M, et al. Multicenter validation of spin-density projection-assisted R2-MRI for the noninvasive measurement of liver iron concentration. Magn Reson Med 2014; 71(6): 2215-23.
[276]
Tacke F, Zimmermann HW. Macrophage heterogeneity in liver injury and fibrosis. J Hepatol 2014; 60(5): 1090-6.
[277]
Straub S, Laun FB, Emmerich J, et al. Potential of quantitative susceptibility mapping for detection of prostatic calcifications. J Magn Reson Imaging 2017; 45(3): 889-98.
[278]
Paige CC, Saunders MA. LSQR, an algorithm for sparse linear equations and sparse least squares. ACM Trans Math Softw 1982; 8(1): 43-71.
[279]
Bilgic B, Fan AP, Polimeni JR, et al. Fast quantitative susceptibility mapping with L1-regularization and automatic parameter selection. Magn Reson Med 2014; 72(5): 1444-59.
[280]
Li W, Wu B, Liu C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. Neuroimage 2011; 55(4): 1645-56.
[281]
Ghiglia DC, Pritt MD. Two-dimensional phase unwrapping: theory, algorithms, and software. New York: Wiley 1998.
[282]
Ippoliti M, Adams LC, Winfried B, et al. Quantitative susceptibility mapping across two clinical field strengths: contrast-to-noise ratio enhancement at 1.5T. J Magn Reson Imaging 2018; 48(5): 1410-20.
[283]
Rasmussen KGB, Kristensen MJ, Blendal RG, et al. DeepQSM-using deep learning to solve the dipole inversion for MRI susceptibility mapping. bioRxiv 2018; 278036.
[284]
Schlemper J, Caballero J, Hajnal JV, Price AN, Rueckert D. A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans Med Imaging 2018; 37(2): 491-503.
[285]
Yoon J, Gong E, Chatnuntawech I, et al. Quantitative susceptibility mapping using deep neural network: QSMnet. Neuroimage 2018; 179: 199-206.


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VOLUME: 13
ISSUE: 2
Year: 2019
Page: [90 - 113]
Pages: 24
DOI: 10.2174/1872208313666181217112745

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