三维多回波流动补偿定量磁化率成像(QSM)方法研究
收稿日期: 2014-08-26
修回日期: 2015-07-23
网络出版日期: 2015-09-05
基金资助
国家自然科学基金资助项目(81271533)
Three-Dimensional Multi-Echo Quantitative Susceptibility Mapping with Flow Compensation
Received date: 2014-08-26
Revised date: 2015-07-23
Online published: 2015-09-05
Supported by
国家自然科学基金资助项目(81271533)
定量磁化率成像(QSM)利用一般成像技术舍弃的相位信息得到局部磁场变化特性，通过复杂的场到源反演计算，可直接得到定量的磁化率图，它广泛应用于测量血氧饱和度、脑部微出血、铁沉积、组织钙化等方面．然而，梯度磁场中流动会引起相位错误，并且产生显著的流动伪影，最终得到错误的QSM图像．为了矫正流动的影响，该文在3 T磁共振系统上实现了三维多回波流动补偿梯度回波序列，并用该序列采集流动水模和志愿者颅脑数据．流动水模和颅脑数据均显示，流动补偿能够明显矫正相位错误，消除流动伪影．颅脑横断位QSM结果证明，流动补偿序列可以消除血液流动引起的QSM的错误，提高QSM的准确性．
关键词： 磁共振成像(MRI); 定量磁化率成像(QSM); 流动补偿; 三维多回波梯度回波; 流动伪影
翁鹏飞1 , 董芳1 , 王前锋1 , 裴孟超1 , 刘田2 , 王乙1 , 3 , 4 , 李建奇1* . 三维多回波流动补偿定量磁化率成像(QSM)方法研究[J]. 波谱学杂志, 2015 , 32(3) : 450 -461 . DOI: 10.11938/cjmr20150306
Venous blood oxygen saturation, cerebral microbleeds, iron deposition and tissue calcification can be measured by quantitative susceptibility mapping (QSM) via deconvolving the magnetic resonance (MR) phase signal. However, accurate estimation of phase signal and magnitude image in QSM may be affected by flow-induced errors in the presence of field gradient, which would ultimately lead to errors in the quantitative susceptibility map reconstructed. The aim of this study is to develop a method for correcting the flow-induced errors in phase signal and magnitude image in QSM. Flow-compensated data acquisition was achieved using a three-dimensional full flow compensated multi-echo gradient echo sequence. Phantom and in vivo experiments were carried out to validate the method proposed. The results of these experiments demonstrated that flow compensation can reduce errors in the phase signal and magnitude images. Improved quantitative susceptibility map of the superior sagittal sinus vein in human brain could be obtained with the proposed method.
[1] Haacke E M, Xu Y, Cheng Y C, et al. Susceptibility weighted imaging (SWI)[J]. Magn Reson Med, 2004, 52(1): 612－618.
[2] Liu T. Spincemaille P, de Rochefort L, et al. Calculation of susceptibility through multiple orientation sampling (COS-MOS): 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.
[3] 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.
[4] Ferdinand S, Andreas D, Berengar W L, et al. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism?[J]. Neuroimage, 2011, 54(4): 2 789－2 807.
[5] 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[J]. Magn Reson Med, 2010, 63(1): 194－206.
[6] Li J Q, Chang S X, Liu T, et al. Reducing the object orientation dependence of susceptibility effects in gradient echo MRI through quantitative susceptibility mapping[J]. Magn Reson Med, 2012, 68(5): 1 563－1 569.
[7] Wang A-li(王阿莉), Lin Jian-zhong(林建忠), Liu Wei-jun(刘伟俊), et al. Quantitative susceptibility mapping(定量磁化率成重建方法及其应用)[J]. Chinese J Magn Reson(波谱学杂志), 2014, 31(1): 133－154.
[8] Bilgic B, Pfefferbaum A, Rohlfing T W, et al. MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping[J]. Neuroimage, 2012, 59(3): 2 625－2 635.
[9] Tan H, Liu T, Wu Y, et al. Evaluation of iron content in human cerebral cavernous malformation using quantitative
susceptibility mapping[J]. Invest Radiol, 2014, 49(7): 498－504.
[10] Lim I A, Faria A V, 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[J]. Neuroimage, 2013, 82: 449－469.
[11] Deistung A, Schweser F, Wiestler B, et al. Quantitative susceptibility mapping differentiates between blood depositions and calcifications in patients with glioblastoma[J]. PLoS One, 2013, 8(3): e57924.
[12] Chen W, Zhu W, Kovanlikaya I, et al. Intracranial calcifications and hemorrhages: Characterization with quantitative susceptibility mapping[J]. Radiology, 2014, 270(2): 496－505.
[13] Liu T, Surapaneni K, Lou M, et al. Cerebral microbleeds: burden assessment by using quantitative susceptibility mapping[J]. Radiology, 2012, 262(1): 269－278.
[14] Ge Y, Zhang Z, Lu H, et al. Characterizing brain oxygen metabolism in patients with multiple sclerosis with T2-relaxation-under-spin-tagging MRI[J]. J Cereb Blood Flow Metab, 2012, 32(3): 403－412.
[15] Nordsmark M, Bentzen S M, Rudat V, et al. Prognostic value of tumor oxygenation in 397 head and neck tumors after primary radiation therapy. An international multi-center study[J]. Radiother Oncol, 2005, 77(1): 18－24.
[16] Leenders K L, Beaney R P, Brooks D J, et al. Dexamethasone treatment of brain tumor patients: effects on regional cerebral blood flow, blood volume, and oxygen utilization[J]. Neurology, 1985, 35(11): 1 610－1 616.
[17] Baron J C, Bousser M G, Rey A, et al. Reversal of focal “misery-perfusion syndrome” by extra-intracranial arterial bypass in hemodynamic cerebral ischemia. A case study with 15O positron emission tomography[J]. Stroke, 1981, 12(4): 454－459.
[18] Sobesky J, Zaro W O, Lehnhardt F G, et al. Does the mismatch match the penumbra? Magnetic resonance imaging and positron emission tomography in early ischemic stroke[J]. Stroke, 2005, 36(5): 980－985.
[19] Heiss W D, Kracht L, Grond M, et al. Early [(11)C] Flumazenil/H(2)O positron emission tomography predicts irreversible ischemic cortical damage in stroke patients receiving acute thrombolytic therapy[J]. Stroke, 2000, 31(2): 366－369.
[20] Haacke E M, Tang J, Neelavalli J, et al. Susceptibility mapping as a means to visualize veins and quantify oxygen saturation[J]. J Magn Reson Imaging, 2010, 32(3): 663－676.
[21] Ferdinand S，Karsten S, Andreas D, et al. Quantitative susceptibility mapping for investigating subtle susceptibility variations in the human brain[J]. Neuroimage, 2012, 62(3): 2 083－2 100.
[22] Xu B, Liu T, Spincemaille P, et al. Flow compensated quantitative susceptibility mapping for venous oxygenation imaging[J]. Magn Reson Med, 2013, 72(2): 438－445.
[23] Haacke E M. Magnetic Resonance Imaging: Physical Principle and Sequence Design (2nd ed.)[M]. America: John Wiley &Sons, 1999.
[24] Yamada K, Naruse S, Nakajima K, et al. Flow velocity of the cortical vein and its effect on functional brain MRI at 1.5 T: preliminary results by cine-MR venography [J]. J Magn Reson Imaging, 1997, 7(2): 347－352.
[25] Dong Fang(董芳), Pei Meng-chao(裴梦超), Wang Qian-feng(王前锋), et al. Gradient echo imaging of the human brain: respiratory induced artifacts and navigator echo correction(颅脑梯度回波成像: 呼吸伪影和导航回波矫正)[J]. Chinese J Magn Reson(波谱学杂志), 2014, 31(3): 321－330.
[26] Chavhan G B, Babyn P S, Thomas B, et al. Principles, techniques, and applications of T2*-based MR imaging and its special applications[J]. Radiographics, 2009, 29(5): 1 433－1 449.
[27] Liu Z, Liao H, Yin J, et al. Using R2* values to evaluate brain tumours on magnetic resonance imaging: preliminary results[J]. Eur Radiol, 2014, 24(3): 693－702.
[28] Liu T, Xu W, 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.
[29] 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): 2 560－ 2 568.
[30] 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.
[31] Comroe J H Jr. Textbook the Lung: Clinical Physiology and Pulmonary Function Tests (2nd ed.)[M]. Chicago: Year Book Medical Publishers, 1955.
[32] Wang X F, Zhao W J. Measurement of multi-wavelength pulse oxygen saturation based on dynamic spectroscopy[J]. Spectrosc Spect Anal, 2014, 34(5): 1 323－1 326.
/
| 〈 |
|
〉 |