Share：
Share this content in WeChat
X
[Chinese][PDF]56345
• Technical Article •
Effect of signal intensity inhomogeneity correction on quantitative susceptibility mapping of brain
GAN Fengling  QU Zheng  ZHAO Weiwei  ZHONG Haodong  LI Gaiying  LI Jianqi 

Cite this article as: Gan FL, Qu Z, Zhao WW, et al. Effect of signal intensity inhomogeneity correction on quantitative susceptibility mapping of brain[J]. Chin J Magn Reson Imaging, 2022, 13(4): 94-99. DOI:10.12015/issn.1674-8034.2022.04.017.


[Abstract] Objective To evaluate the effects of coil selection and signal intensity inhomogeneity on the susceptibility values of deep gray matter nuclei measured by quantitative susceptibility mapping (QSM).Materials and Methods Ten healthy subjects were scanned on a 2.89 T MRI system using 20- and 64-channel combined head-neck coils. QSM reconstructions were performed before and after correcting signal intensity inhomogeneity of the original gradient-echo images, respectively. Six bilateral deep cerebral gray matter nuclei were manually drawn on susceptibility maps, including red nucleus, substantia nigra, globus pallidus, putamen, caudate nucleus, and dentate nucleus. Paired sample t test, linear correlation analysis and Bland-Altman analysis were used to compare the differences and consistency of susceptibility values between groups with and without intensity inhomogeneity correction using different coils.Results The susceptibility maps with intensity inhomogeneity correction demonstrated improved boundaries of deep gray matter nuclei and susceptibility values increased significantly. The mean susceptibility values of deep gray matter nuclei after correction were highly correlated with those before correction (20-channel: slope K=1.06, R2=0.96; 64-channel: slope K=1.12, R2=0.95). The mean susceptibility values of deep gray matter nuclei from 20-channel coil were highly correlated with those from 64-channel coil (without correction: slope K=0.92, R2=0.96; with correction: slope K=0.98, R2=0.96). There was no statistically significant difference in the mean susceptibility values between the 20- and 64-channel coils acquisition after correcting the intensity inhomogeneity. Bland-Altman results showed no significant deviation between the 20- and 64-channel coils acquisition after the correction on the intensity inhomogeneity.Conclusions The signal intensity inhomogeneity affects the susceptibility values of the deep brain nuclei, and inhomogeneity correction can improve the accuracy of susceptibility values of deep gray matter nuclei.
[Keywords] quantitative susceptibility mapping；intensity inhomogeneity correction；coil；deep gray matter nuclei；magnetic resonance imaging

GAN Fengling1   QU Zheng2   ZHAO Weiwei1   ZHONG Haodong1   LI Gaiying1   LI Jianqi1*  

1 Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China

2 West China School of Medicine/West China Hospital, Sichuan University, Chengdu 610041, China

Li JQ, E-mail: jqli@phy.ecnu.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS Social Science Foundation of China (No. 15ZDB016).
Received  2021-11-11
Accepted  2022-03-25
DOI: 10.12015/issn.1674-8034.2022.04.017
Cite this article as: Gan FL, Qu Z, Zhao WW, et al. Effect of signal intensity inhomogeneity correction on quantitative susceptibility mapping of brain[J]. Chin J Magn Reson Imaging, 2022, 13(4): 94-99. DOI:10.12015/issn.1674-8034.2022.04.017.

[1]
Wang Y, Liu T. Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker[J]. Magn Reson Med, 2015, 73(1): 82-101. DOI: 10.1002/mrm.25358.
[2]
Zheng ZW, Cai CB, Cai SH, et al. Brief overview of principles and methods of quantitative susceptibility mapping[J]. Chin J Magn Reson Imaging, 2016, 7(6): 454-460. DOI: 10.12015/issn.1674-8034.2016.06.011.
[3]
Sun TT, Wu QL, Ni HY. Imaging method and clinical application of quantitative susceptibility mapping[J]. Chin J Radiol, 2020, 54(9): 912-916. DOI: 10.3760/cma.j.cn112149-20190924-00513.
[4]
Liu T, Surapaneni K, Lou M, et al. Cerebral microbleeds: burden assessment by using quantitative susceptibility mapping[J]. Radiology, 2012, 262(1): 269-278. DOI: 10.1148/radiol.11110251.
[5]
Li GY, Zhai GQ, Zhao XX, et al. 3D texture analyses within the substantia nigra of Parkinson's disease patients on quantitative susceptibility maps and R2 maps[J]. Neuroimage, 2019, 188: 465-472. DOI: 10.1016/j.neuroimage.2018.12.041.
[6]
Chen WW, Zhu WZ, Kovanlikaya I, et al. Intracranial calcifications and hemorrhages: characterization with quantitative susceptibility mapping[J]. Radiology, 2014, 270(2): 496-505. DOI: 10.1148/radiol.13122640.
[7]
Dimov AV, Gupta A, Kopell BH, et al. High-resolution QSM for functional and structural depiction of subthalamic nuclei in DBS presurgical mapping[J]. J Neurosurg, 2018, 131(2): 360-367. DOI: 10.3171/2018.3.JNS172145.
[8]
Hinoda T, Fushimi Y, Okada T, et al. Quantitative susceptibility mapping at 3 T and 1.5 T: evaluation of consistency and reproducibility[J]. Invest Radiol, 2015, 50(8): 522-530. DOI: 10.1097/RLI.0000000000000159.
[9]
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]. J Magn Reson Imaging, 2018, 48(5): 1410-1420. DOI: 10.1002/jmri.26045.
[10]
Isaacs BR, Mulder MJ, Groot JM, et al. 3 versus 7 Tesla magnetic resonance imaging for parcellations of subcortical brain structures in clinical settings[J]. PLoS One, 2020, 15(11): e0236208. DOI: 10.1371/journal.pone.0236208.
[11]
Spincemaille P, Anderson J, Wu GH, et al. Quantitative susceptibility mapping: MRI at 7T versus 3T[J]. J Neuroimaging, 2020, 30(1): 65-75. DOI: 10.1111/jon.12669.
[12]
Li JQ, Chang SX, Liu T, et al. Phase-corrected bipolar gradients in multi-echo gradient-echo sequences for quantitative susceptibility mapping[J]. MAGMA, 2015, 28(4): 347-355. DOI: 10.1007/s10334-014-0470-3.
[13]
Lauzon ML, McCreary CR, McLean DA, et al. Quantitative susceptibility mapping at 3 T: comparison of acquisition methodologies[J]. NMR Biomed, 2017, 30(4): e3492. DOI: 10.1002/nbm.3492.
[14]
Cong F, Liu XR, Liu CJ, et al. Improved depiction of subthalamic nucleus and globus pallidus internus with optimized high-resolution quantitative susceptibility mapping at 7 T[J]. NMR Biomed, 2020, 33(11): e4382. DOI: 10.1002/nbm.4382.
[15]
Zhou D, Cho J, Zhang JW, et al. Susceptibility underestimation in a high-susceptibility phantom: dependence on imaging resolution, magnitude contrast, and other parameters[J]. Magn Reson Med, 2017, 78(3): 1080-1086. DOI: 10.1002/mrm.26475.
[16]
Karsa A, Punwani S, Shmueli K. The effect of low resolution and coverage on the accuracy of susceptibility mapping[J]. Magn Reson Med, 2019, 81(3): 1833-1848. DOI: 10.1002/mrm.27542.
[17]
Lancione M, Cencini M, Costagli M, et al. Diagnostic accuracy of quantitative susceptibility mapping in multiple system atrophy: the impact of echo time and the potential of histogram analysis[J]. Neuroimage Clin, 2022, 34: 102989. DOI: 10.1016/j.nicl.2022.102989.
[18]
Li W, Wu B, Liu CL. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition[J]. NeuroImage, 2011, 55(4): 1645-1656. DOI: 10.1016/j.neuroimage.2010.11.088.
[19]
Wharton S, Schäfer A, Bowtell R. Susceptibility mapping in the human brain using threshold-based k-space division[J]. Magn Reson Med, 2010, 63(5): 1292-1304. DOI: 10.1002/mrm.22334.
[20]
Liu T, Spincemaille P, de Rochefort L, et al. 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[J]. Magn Reson Med, 2009, 61(1): 196-204. DOI: 10.1002/mrm.21828.
[21]
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[J]. Neuroimage, 2012, 59(3): 2560-2568. DOI: 10.1016/j.neuroimage.2011.08.082.
[22]
Smith SM. Fast robust automated brain extraction[J]. Hum Brain Mapp, 2002, 17(3): 143-155. DOI: 10.1002/hbm.10062.
[23]
Liu T, Wisnieff C, Lou M, et al. Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping[J]. Magn Reson Med, 2013, 69(2): 467-476. DOI: 10.1002/mrm.24272.
[24]
Abdul-Rahman HS, Gdeisat MA, Burton DR, et al. Fast and robust three-dimensional best path phase unwrapping algorithm[J]. Appl Opt, 2007, 46(26): 6623-6635. DOI: 10.1364/ao.46.006623.
[25]
Zhou D, Liu T, Spincemaille P, et al. Background field removal by solving the Laplacian boundary value problem[J]. NMR Biomed, 2014, 27(3): 312-319. DOI: 10.1002/nbm.3064.
[26]
Liu Z, Spincemaille P, Yao YH, et al. MEDI+0: Morphology enabled dipole inversion with automatic uniform cerebrospinal fluid zero reference for quantitative susceptibility mapping[J]. Magn Reson Med, 2018, 79(5): 2795-2803. DOI: 10.1002/mrm.26946.
[27]
Liu T, Xu WY, Spincemaille P, et al. Accuracy of the morphology enabled dipole inversion (MEDI) algorithm for quantitative susceptibility mapping in MRI[J]. IEEE Trans Med Imaging, 2012, 31(3): 816-824. DOI: 10.1109/TMI.2011.2182523.
[28]
Liu T, Liu J, de Rochefort L, et al. Morphology enabled dipole inversion (MEDI) from a single-angle acquisition: comparison with COSMOS in human brain imaging[J]. Magn Reson Med, 2011, 66(3): 777-783. DOI: 10.1002/mrm.22816.
[29]
Li KR, Avecillas-Chasin J, Nguyen TD, et al. Quantitative evaluation of brain iron accumulation in different stages of Parkinson's disease[J]. J Neuroimaging, 2022, 32(2): 363-371. DOI: 10.1111/jon.12957.
[30]
He NY, Ghassaban K, Huang P, et al. Imaging iron and neuromelanin simultaneously using a single 3D gradient echo magnetization transfer sequence: combining neuromelanin, iron and the nigrosome-1 sign as complementary imaging biomarkers in early stage Parkinson's disease[J]. Neuroimage, 2021, 230: 117810. DOI: 10.1016/j.neuroimage.2021.117810.
[31]
Uchida Y, Kan H, Sakurai K, et al. Magnetic susceptibility associates with dopaminergic deficits and cognition in Parkinson's disease[J]. Mov Disord, 2020, 35(8): 1396-1405. DOI: 10.1002/mds.28077.
[32]
Au CKF, Abrigo J, Liu CL, et al. Quantitative susceptibility mapping of the hippocampal Fimbria in Alzheimer's disease[J]. J Magn Reson Imaging, 2021, 53(6): 1823-1832. DOI: 10.1002/jmri.27464.
[33]
Gong NJ, Dibb R, Bulk M, et al. Imaging beta amyloid aggregation and iron accumulation in Alzheimer's disease using quantitative susceptibility mapping MRI[J]. Neuroimage, 2019, 191: 176-185. DOI: 10.1016/j.neuroimage.2019.02.019.
[34]
Bulk M, Abdelmoula WM, Geut H, et al. Quantitative MRI and laser ablation-inductively coupled plasma-mass spectrometry imaging of iron in the frontal cortex of healthy controls and Alzheimer's disease patients[J]. Neuroimage, 2020, 215: 116808. DOI: 10.1016/j.neuroimage.2020.116808.
[35]
Huang WY, Sweeney EM, Kaunzner UW, et al. Quantitative susceptibility mapping versus phase imaging to identify multiple sclerosis iron rim lesions with demyelination[J]. J Neuroimaging, 2022. DOI: 10.1111/jon.12987.
[36]
Li GY, Wu R, Tong R, et al. Quantitative measurement of metal accumulation in brain of patients with Wilson's disease[J]. Mov Disord, 2020, 35(10): 1787-1795. DOI: 10.1002/mds.28141.
[37]
Song S, Zheng YJ, He YL. A review of methods for bias correction in medical images[J]. Biomed Eng Rev, 2017, 3(1). DOI: 10.18103/bme.v3i1.1550.
[38]
Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI[J]. IEEE Trans Med Imaging, 2007, 26(3): 405-421. DOI: 10.1109/TMI.2006.891486.

PREV A comparative study on phantom verification of T1 and T2 relaxation values determined by synthetic MRI and conventional mapping methods
NEXT Comparative study of 3D-FLAIR sequence based on compressed sensing and the conventional 2D-FLAIR sequence in white matter lesion imaging
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn