磁敏感加权成像技术研究
|
摘要
近年来研究表明,磁敏感加权成像(Susceptibility Weighted Imaging, SWI)对颅内静脉损伤和疾病的诊断具有非常重要的临床价值。SWI是一种三维、高分辨率、T2*加权的梯度回波成像方式。它利用静脉和组织之间磁敏感性差异引发的相对相位变化和幅值改变来增强小静脉的可见性。为了获得接近最优的静脉对比,SWI常选择较长的回波时间,因而数据采集时间较长。回波时间长时,会引起T2*衰减,导致图像的信噪比相对较低。由于梯度回波对磁场不均匀性比较敏感,因此在构建三维SWI相位模板时,某些局部磁场严重不均匀的区域会因为非共振效应而产生伪影。由于静脉负对比度,在三维SWI数据显示时常采用最小密度投影(Minimum-Intensity Projection, mIP),在轴向投影中,由于大脑的自然形状,低密度的空气和骨组织体素会出现在脑外围区域的投影线中,导致mIP图像中脑外围区域的静脉和脑组织信号损失而产生伪影。低密度的空气和骨组织也使得常规全脑SWl数据的矢状面和冠状面投影难以进行。此外,小静脉的可见性与背景信号密切相关,抑制背景信号能增强小静脉的可见性。本文针对上述问题,对SWI快速数据采集和重建、伪影校正和静脉增强等问题做了较为系统和深入的研究,主要包括：
(1)研究了部分k-空间数据采集和基于凸集投影的图像重建方法,能有效减少数据采集时间,并表明在部分k-空间中施加三维Fermi滤波器,可提高图像的信噪比和减小空间分辨率的角度相关性。
(2)提出了双回波和多回波磁共振动静脉数据同时采集(Simultaneous Acqusition of Magnetic Resonance Angiography and Venography, MRAV)技术,在3T下采集MRA(Magnetic Resonance Angiography)数据的同时,无需增加额外的扫描时间,就可获得MRV (Magnetic Resonance Venography)数据。研究了空间分辨率对MRV图像静脉-背景对比度的影响。指出不同长度回波时间(Echo Time, TE)对MRV图像信噪比和非共振伪影的影响。表明通过拟合多个TE数据能更有效地映射R2*信号,进而量化脑区的铁离子沉积度。
(3)研究了一种基于局部磁场梯度计算的非共振伪影校正方法,无需进行相位解卷绕,通过计算局部磁场梯度来估计局部磁场的不均匀程度,进而抑制相位模板中残留的背景信息。
(4)研究了两类脑组织体分割方法：一种是基于局部相位和幅值统计属性构建多变量映射来提高空气和脑组织的分离度,从而更可靠地分割脑组织；另一种是基于改进的变分水平集算法,直接对幅值图像中的脑组织进行分割。这两种方法都能有效地抑制三维SWI数据越面显示时脑外围区域的信号损失和进行面内mIP。
(5)研究了三种小静脉图像增强算法：构建了图像域的二阶相位差高通滤波器来增强小静脉的相移,并用Fermi滤波器抑制SWI的背景信号；研究了对幅值图像的k-空间数据进行高通滤波来抑制背景组织的方法,提高小静脉的可见度；提出了基于Hessian矩阵的三维多尺度静脉增强算法,通过研究Hessian矩阵的特征值对静脉的特异性表现,来抑制背景组织和噪声,增强小静脉的可见性。
Susceptibility-weighted imaging (SWI) has recently demonstrated great clinical significance in the diagnosis of several intracranial venous lesions and diseases. SWI is a 3D, high resolution, T2*-weighted, and gradient-echo imaging technique. SWI utilizes the relative phase and magnitude change in the venous vasculature introduced by the susceptibility difference between venous blood and parenchyma. A relatively long echo time (TE) is typically used in SWI to achieve nearly optimal venous contrast, resulting in long data acqusition time. The SWI data have relatively low signal-to-noise ratio (SNR) due to the T2* decay at a long TE. Artifact can arise during the construction of the 3D phase mask in regions with severe field inhomogeneity due to the off-resonance effect. Image artifacts can also arise from minimum-intensity projection (mIP), which is commonly used for the display of venous vasculature because of the negative venous contrast. In axial projection, voxels in air and bone can be in the path of projection in peripheral regions of the brain due to the nature shape of the brain. The low intensity in air or bone results in the disappearance of signal from brain tissue, including veins, in mIP images in that region. The low intensity of air and bone also make mIP impractical for sagittal and coronal projections of whole-brain SWI data. Moreover, visualization of the venous vasculature in the magnitude data can be enhanced by background suppression. In this study, we investigated following aspects:
(1) The partial k-space acquisition was studied to reduce scan time and the magnitude images were reconstructed by the projection onto convex sets algorithm. A 3D Fermi filter in k-space was demonstrated to increase the SNR and to reduce angular dependence of spatial resolution.
(2) A dual-echo and multi-echo pulse sequence for simultaneous acquisition of magnetic resonance angiography and venography (MRAV) were developed. Using this pulse sequence, the magnetic resonance venography (MRV) data can be acquired without increasing the scan time of magnetic resonance angiography (MRA). The effect of spatial resolution on vein-to-background contrast was demonstrated. The venous contrast and off-resonance artifacts of MRV data acquired at different TEs were studied. The R2* value at each voxel was quantified using multi-TE exponential fitting. The R2* map can be used for the quantification of iron deposition.
(3) A novel postprocessing approach was studied to calculate the local field gradient (LFG) for the reduction of the off-resonance artifacts without phase-unwrapping. LFG measurements were used to assess the severity of field inhomogeneity and suppress the residual phase in the phase mask induced by the off-resonance effect.
(4) Two volume segmentation algorithms of the brain tissue were studied. A multivariate measure based on the statistics of phase and magnitude was constructed for robust tissue-air volume segmentation. An improved version of the variational level set algorithm was used for volume segmentation of brain tissue directly based on magnitude images. Both algorithms provide a feasible solution to reduce the signal loss in the peripheral regions of the brain in the through-plane mIP images and enable in-plane mIP display of MRV.
(5) Three enhancement algorithms of small vein were presented. First, the image-domain high-pass filters based on second-order phase difference were applied to the complex 3D SWI data to enhance the susceptibility phase shift of the veins and suppress background signal in SWI. Second, high-pass filter was applied to the Fourier domain of the magnitude images to suppress the background signal and enahnce the visibility of the venous vasculature in the brain. Third, a 3D multi-scale vessel enhancement algorithm based on the Hessian matrix was proposed. Eigenvalue analysis of the Hessian matrix was used to enhance the veins, suppress the background tissue, and reduce the noise in air.
(1)研究了部分k-空间数据采集和基于凸集投影的图像重建方法,能有效减少数据采集时间,并表明在部分k-空间中施加三维Fermi滤波器,可提高图像的信噪比和减小空间分辨率的角度相关性。
(2)提出了双回波和多回波磁共振动静脉数据同时采集(Simultaneous Acqusition of Magnetic Resonance Angiography and Venography, MRAV)技术,在3T下采集MRA(Magnetic Resonance Angiography)数据的同时,无需增加额外的扫描时间,就可获得MRV (Magnetic Resonance Venography)数据。研究了空间分辨率对MRV图像静脉-背景对比度的影响。指出不同长度回波时间(Echo Time, TE)对MRV图像信噪比和非共振伪影的影响。表明通过拟合多个TE数据能更有效地映射R2*信号,进而量化脑区的铁离子沉积度。
(3)研究了一种基于局部磁场梯度计算的非共振伪影校正方法,无需进行相位解卷绕,通过计算局部磁场梯度来估计局部磁场的不均匀程度,进而抑制相位模板中残留的背景信息。
(4)研究了两类脑组织体分割方法：一种是基于局部相位和幅值统计属性构建多变量映射来提高空气和脑组织的分离度,从而更可靠地分割脑组织；另一种是基于改进的变分水平集算法,直接对幅值图像中的脑组织进行分割。这两种方法都能有效地抑制三维SWI数据越面显示时脑外围区域的信号损失和进行面内mIP。
(5)研究了三种小静脉图像增强算法：构建了图像域的二阶相位差高通滤波器来增强小静脉的相移,并用Fermi滤波器抑制SWI的背景信号；研究了对幅值图像的k-空间数据进行高通滤波来抑制背景组织的方法,提高小静脉的可见度；提出了基于Hessian矩阵的三维多尺度静脉增强算法,通过研究Hessian矩阵的特征值对静脉的特异性表现,来抑制背景组织和噪声,增强小静脉的可见性。
Susceptibility-weighted imaging (SWI) has recently demonstrated great clinical significance in the diagnosis of several intracranial venous lesions and diseases. SWI is a 3D, high resolution, T2*-weighted, and gradient-echo imaging technique. SWI utilizes the relative phase and magnitude change in the venous vasculature introduced by the susceptibility difference between venous blood and parenchyma. A relatively long echo time (TE) is typically used in SWI to achieve nearly optimal venous contrast, resulting in long data acqusition time. The SWI data have relatively low signal-to-noise ratio (SNR) due to the T2* decay at a long TE. Artifact can arise during the construction of the 3D phase mask in regions with severe field inhomogeneity due to the off-resonance effect. Image artifacts can also arise from minimum-intensity projection (mIP), which is commonly used for the display of venous vasculature because of the negative venous contrast. In axial projection, voxels in air and bone can be in the path of projection in peripheral regions of the brain due to the nature shape of the brain. The low intensity in air or bone results in the disappearance of signal from brain tissue, including veins, in mIP images in that region. The low intensity of air and bone also make mIP impractical for sagittal and coronal projections of whole-brain SWI data. Moreover, visualization of the venous vasculature in the magnitude data can be enhanced by background suppression. In this study, we investigated following aspects:
(1) The partial k-space acquisition was studied to reduce scan time and the magnitude images were reconstructed by the projection onto convex sets algorithm. A 3D Fermi filter in k-space was demonstrated to increase the SNR and to reduce angular dependence of spatial resolution.
(2) A dual-echo and multi-echo pulse sequence for simultaneous acquisition of magnetic resonance angiography and venography (MRAV) were developed. Using this pulse sequence, the magnetic resonance venography (MRV) data can be acquired without increasing the scan time of magnetic resonance angiography (MRA). The effect of spatial resolution on vein-to-background contrast was demonstrated. The venous contrast and off-resonance artifacts of MRV data acquired at different TEs were studied. The R2* value at each voxel was quantified using multi-TE exponential fitting. The R2* map can be used for the quantification of iron deposition.
(3) A novel postprocessing approach was studied to calculate the local field gradient (LFG) for the reduction of the off-resonance artifacts without phase-unwrapping. LFG measurements were used to assess the severity of field inhomogeneity and suppress the residual phase in the phase mask induced by the off-resonance effect.
(4) Two volume segmentation algorithms of the brain tissue were studied. A multivariate measure based on the statistics of phase and magnitude was constructed for robust tissue-air volume segmentation. An improved version of the variational level set algorithm was used for volume segmentation of brain tissue directly based on magnitude images. Both algorithms provide a feasible solution to reduce the signal loss in the peripheral regions of the brain in the through-plane mIP images and enable in-plane mIP display of MRV.
(5) Three enhancement algorithms of small vein were presented. First, the image-domain high-pass filters based on second-order phase difference were applied to the complex 3D SWI data to enhance the susceptibility phase shift of the veins and suppress background signal in SWI. Second, high-pass filter was applied to the Fourier domain of the magnitude images to suppress the background signal and enahnce the visibility of the venous vasculature in the brain. Third, a 3D multi-scale vessel enhancement algorithm based on the Hessian matrix was proposed. Eigenvalue analysis of the Hessian matrix was used to enhance the veins, suppress the background tissue, and reduce the noise in air.
引文
[1]Z.P. Liang, P.C. Lauterbur. Principles of magnetic resonance imaging:a signal processing perspective. The Institute of Electrical and Electronics Engineers. New York.2000.
[2]E.M. Haacke, R.W. Brown, M.R. Thompson, R. Venktesan. Magnetic resonance imaging:physical principles and sequence design. Wiley, New York.1999.
[3]杨正汉,冯逢,王霄英.磁共振成像技术指南.人民军医出版社.2007.
[4]J.R. Reichenbach, R. Venkatesan, D.J. Schillinger, D.K. Kido, E.M. Haacke. Small vessels in the human brain:MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology.1997; 204:272-277.
[5]J.R. Reichenbach, M. Essig, E.M. Haacke, B.C. Lee, C. Przetak, W.A. Kaiser, L.R. Schad. High-resolution venography of the brain using magnetic resonance imaging. MAGMA.1998; 6:62-69.
[6]E.M. Haacke, Y. Xu, Y.C. Cheng, J.R. Reichenbach. Susceptibility weighted imaging (SWI). Magn Reson Med.2004;52:612-618.
[7]K.R. Thulborn, J.C. Waterton, P.M. Matthews, C.K. Radda. Oxygenation dependence of the transverse relaxation time of water protons in whole blood at high field. Biochern Biophys Acta.1982; 714:265-270.
[8]R.M. Weisskoff, S. Kiihne. MRI susceptometry:image-based measurement of absolute susceptibility of MR contrast agents and human blood. Magn Reson Med 1992;24:375-383.
[9]E.M. Haacke, S. Mittal, Z. Wu, J. Nelavalli, Y. N. Cheng. Susceptibility-weighted imaging:technical aspects and clinical applications, part1. AJNR.2009; 30:1-12.
[10]S. Mittal, Z. Wu, J. Nelavalli, E.M. Haacke. Susceptibility-weighted imaging:technical aspects and clinical applications, part2. AJNR.2009; 30:232-252.
[11]B. C Ong and S. L Stuckey. Susceptibility weighted imaging:a pictorial review. Journal of Medical imaging and radiation oncology.2010;54:435-449.
[12]K.T. Baudendistel, J.R. Reichenbach, R. Metzner, J. Schroder, L.R. Schad. Comparison of functional venography and EPI-BOLD-fMRI at 1.5T. Magn Reson Imaging.1998; 16:989-991.
[13]B.C.P. Lee, K.D. Vo, D.K. Kido, P. Mukherjee, J.R. Reichenbach, W. Lin, M.S. Yoon, E.M. Haacke. MR high-resolution blood oxygenation level-dependent venography of occult (low-flow) vascular lesions. AJNR.1999; 20:1239-1242.
[14]I.L. Tan, R.A. van Schijndel, P.J.W. Pouwels, M.A.A. van Walderveen, J.R. Reichenbach, R. A. Manoliu and F. Barkhof. MR venography of multiple sclerosis. AJNR.2000; 21:1039-1042.
[15]W. Lin, P. Mukherjee, H. An, Y. Yu, Y. Wang, K. Vo, B. Lee, D. Kido, E.M. Haacke. Improving high-resolution MR bold venographic imaging using a T1 reducing contrast agent. J Magn Reson Imaging.1999; 10:118-123.
[16]J.R. Reichenbach, L. Jonetz-Mentzel, C. Fitzek, E.M. Haacke, D.K. Kido, B.C.P. Lee, W.A. Kaiser. High-resolution blood oxygen-level dependent MR venography (HRBV):A new technique. Neuroradiology.2001; 43:364-369.
[17]J. R. Reichenbach, E. M. Haacke. High-resolution BOLD venographic imaging:A window into brain function. NMR in Biomedicine.2001; 14:453-467.
[18]L. Schad. Improved target volume characterization in stereotactic treatment planning of brain lesions by using high-resolution BOLD MR-venography. NMR in Biomedicine.2001;14:478-483.
[19]J.R. Reichenbach, M. Barth, E.M. Haacke, M.Klarhofer, W.A. Kaiser, E. Moser. High-resolution MR venography at 3 Tesla. JCAT.2000; 24:949-957.
[20]M. Barth, I.M. Nobauer-Huhmann, J.R. Reichenbach, V. Mlynarik, A. Schoggl, C. Matula, S. Trattnig. Enhanced BOLD MR-venography (CE-MRV) of brain tumors at 3 Tesla:first clinical experience. Investigative Radiology.2003; 38:409-414.
[21]R.A. Dashner, D.W. Chakeres, A. Kangarlu, P. Schmalbrock, G. A. Christoforidis, R.M. DePhilip. MR imaging visualization of the cerebral microvasculature:a comparison of live and postmortem studies at 8 T. AJNR.2003; 24:1881-1884.
[22]K.A. Tong, S. Ashwal, B.A. Holshouser, L. Shutter, G. Herigault, E.M. Haacke, D.K. Kido. Improved detection of hemorrhagic shearing lesions in children with post-traumatic diffuse axonal injury-initial results. Radiology.2003; 227:332-339.
[23]A. Rauscher, M. Barth, J.R. Reichenbach, R. Stollberger, E. Moser. Automated unwrapping of MR phase images applied to BOLD MR-venography at 3 Tesla. J Magn Reson Imaging.2003; 18:175-180.
[24]M. Essig, J.R.Reichenbach, L.R. Schad, S.O. Schonberg, J. Debus, W.A. Kaiser. High-resolution MR venography of cerebral arteriovenous malformations. Magn Reson Imaging.1999; 17:1417-1425.
[25]Y. Wang, Y. Yu, D. Li, et al. Artery and vein separation using susceptibility dependent phase in contrast-enhanced MRA. J Magn Reson Imaging.2000; 12:661-670.
[26]A. Rauscher, J. Sedlacik, M. Barth, et al. Noninvasive assessment of vascular architecture and function during modulated blood oxygenation using susceptibility weighted magnetic resonance imaging. Magn Reson Med.2005;54:87-95.
[27]A. Rauscher, J. Sedlacik, M. Barth, et al. Magnetic susceptibility-weighted MR phase imaging of the human brain. AJNR.2005; 26:736-42.
[28]V. Sehgal, Z. Delproposto, D. Haddar, et al. Susceptibility-weighted imaging to visualize blood products and improve tumor contrast in the study of brain masses. J Magn Reson Imaging.2006; 24:41-51.
[29]B.C. Hamans, M. Barth, W.P. Leenders, et al. Contrast enhanced susceptibility weighted imaging (CE-SWI) of the mouse brain using ultrasmall superparamagnetic iron oxide particles (USPIO). Z Med Phys.2006; 16:269-274.
[30]S. Ashwal, T. Babikian, J. Gardner-Nichols, et al. Susceptibility-weighted imaging and proton magnetic resonance spectroscopy in assessment of outcome after pediatric traumatic brain injury. Arch Phys Med Rehabil.2006; 87:850-858.
[31]Y. Yoshida, S. Terae, K. Kudo, et al. Capillary telangiectasia of the brain stem diagnosed by susceptibility-weighted imaging. J Comput Assist Tomogr.2006; 30:980-982.
[32]I.M. Noebauer-Huhmann, K. Pinker, M. Barth, et al. Contrast-enhanced, highresolution, susceptibility-weighted magnetic resonance imaging of the brain:dose-dependent optimization at 3 Tesla and 1.5 Tesla in healthy volunteers. Invest Radiol.2006; 41:249-255.
[33]Z. Wu, S. Mitta, et al. Identification of calcification with MRI using susceptibility-weighted imaging:a case study. J Magn Reson Imaging.2009;29:177-182.
[34]E.M. Haacke, N.Y.C. Cheng, Q. Liu, J. Neelavalli, Robert Ogg, A. Khan, M. Ayaz, W. Kirsch, A. Obenaus. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging. 2005; 23:1-25.
[35]E.M Haacke, M. Ayaz, A. Khan, et al. Establishing a baseline phase behavior in magnetic resonance imaging to determine normal vs. abnormal iron content in the brain. J Magn Reson Imaging.2007; 26:256-264.
[36]E.M Haacke, Z.S. DelProposto, S. Chaturvedi, et al. Imaging cerebral amyloid angiopathy with susceptibility-weighted imaging. AJNR.2007;28:316-317.
[37]E.M. Haacke, et al. Characterizing iron deposition in multiple sclerosis lesions using susceptibility weighted imaging. J Magn Reson Imaging.2009;29:537-544.
[38]G. McAuley, M. Schrag, P. Sipos, S. Sun, A. Obenaus, J. Neelavalli, E. M. Haacke, B. Holschouser, R. Madacsi, and W. Kirsch. Quantification of punctate iron sources using magnetic resonance phase. Magn Reson Med.2010;63:106-115.
[39]L. d. Rochefort, T. Liu, B. Kressler, J. Liu, P. Sincemaille, V. Lebon, J. Wu, and Y. Wang. Quantitative susceptibility map reconstruction from MR phase data using Bayesian regularization:validation and application to brain imaging. Magn Reson Med.2010;63:194-206.
[40]E.M. Haacke, J. Tang, J. Neelavalli, and Y. Cheng. Susceptibility mapping as a means to visualize veins and quantify oxygen saturation. J. Magn. Reson. Imaging.2010:32:663-676
[41]S. Wharton, A. Schafer, and R. Bowtell. Susceptibility mapping in the human brain using threshold-based k-space division. Magn Reson Med.2010;63:1292-1304.
[42]J. Sedlacik and J. Reichenbach. Validation of quantitative estimation of tissue oxygen extraction fraction and deoxygenated blood volume fraction in phantom and in vivo experiments by using MRI. Magn Reson Med.2010;63:910-921.
[43]J. Sedlacik, A. Rauscher, J.R. Reichenbach. Obtaining blood oxygenation levels from MR signal behavior in the presence of single venous vessels. Magn Reson Med.2007;58:1035-1044.
[44]R.M. Henkelman. Measurement of signal intensities in the presence of noise in MR images. Med.Phys.1985;12:232-233.
[45]Y. Xu, E.M. Haacke. The role of voxel aspect ratio in determining apparent vascular phase behavior in susceptibility weighted imaging. Magn Reson Imaging.2006; 24:155-160.
[46]V. Sehgal, Z. Delproposto, E.M Haacke, K. Tong, N. Wycliffe, D.K. Kido, Y. Xu, J. Neelavalli, D. Haddar, J.R. Reichenbach. Clinical applications of neuroimaging with susceptibility weighted imaging. J Magn Reson Imaging.2005; 22:439-450.
[47]D. S. Vinod, P.S. Bindu, S. Ravishankar, et al. Relaxation and susceptibility MRI characteristics in Hallervorden-Spatz syndrome. J Magn Reson Imaging.2007;25:715-720.
[48]M. Akter, T. Hirai, Y. Hiai, et al. Detection of hemorrhagic hypointense foci in the brain on susceptibility-weighted imaging clinical and phantom studies. Acad Radiol.2007;14:1011-1019.
[49]J.P. Larsen, W.I. Britt, D. Kido, et al. Susceptibility weighted magnetic resonance imaging in the evaluation of dementia. Radiology Case Reports.2007; 2:102.
[50]K. Pinker, I.M. Noebauer-Huhmann, I. Stavrou, et al. High-resolution contrastenhanced, susceptibility-weighted MR imaging at 3T in patients with brain tumors:correlation with positron-emission tomography and histopathologic findings. AJNR.2007;28:1280-1286.
[51]Y. Shen, Z. Kou, C.W. Kreipke, et al. In vivo measurement of tissue damage, oxygen saturation changes and blood flow changes after experimental traumatic brain injury in rats using susceptibility weighted imaging. Magn Reson Imaging.2007;25:219-227.
[52]H.J. Mentzel, A. Dieckmann, C. Fitzek, et al. Early diagnosis of cerebral involvement in Sturge-Weber syndrome using high-resolution BOLD MR venography. Pediatr Radiol.2005;35:85-90.
[53]T. Babikian, M.C. Freier, K.A. Tong, et al. Susceptibility weighted imaging:neuropsychologic outcome and pediatric head injury. Pediatr Neurol.2005;33:184-194.
[54]J. Hu, Y. Yu, C. Juhasz, et al. MR susceptibility weighted imaging (SWI) complements conventional contrast enhanced T1 weighted MRI in characterizing brain abnormalities of Sturge-Weber syndrome. J Magn Reson Imaging.2008;28:300-307.
[55]B. Thomas, S. Somasundaram, K. Thamburaj, et al. Clinical applications of susceptibility weighted MR imaging of the brain:a pictorial review. Neuroradiology.2008;50:105-116.
[56]J.M. de Souza, R.C. Domingues, L.C. Cruz, et al. Susceptibility-weighted imaging for the evaluation of patients with familial cerebral cavernous malformations; a comparison with T2-weighted fast spin-echo and gradient-echo sequences. AJNR.2008;29:154-158.
[57]S.L. Harder, K.M. Hopp, H. Ward, et al. Mineralization of the deep gray matter with age:a retrospective review with susceptibility-weighted MR imaging. AJNR.2008;29:176-183.
[58]J. Sedlacik, K. Helm, A. Rauscher, et al. Investigations on the effect of caffeine on cerebral venous vessel contrast by using susceptibility-weighted imaging (SWI) at 1.5,3 and 7 T. Neuroimage.2008;40:11-18.
[59]S. Somasundaram, C. Kesavadas, B. Thomas. Susceptibility weighted imaging in holohemispheric venous angioma with cerebral hemiatrophy. Neurol India.2008;56:104-105.
[60]H.L. Liu, Y.Y. Wai, W.S. Chen, et al. Hemorrhage detection during focused-ultrasound induced blood-brain-barrier opening by using susceptibility-weighted magnetic resonance imaging. Ultrasound Med Biol.2008;34:598-606.
[61]Y. Fushimi, Y. Miki, K. Togashi, et al. A developmental venous anomaly presenting atypical findings on susceptibility-weighted imaging. AJNR.2008;29:E56.
[62]K. Zhong, J. Leupold, D. von Elverfeldt, et al. The molecular basis for gray and white matter contrast in phase imaging. Neuroimage.2008;40:1561-1566.
[63]A. Deistung, A. Rauscher, J. Sedlacik, et al. Susceptibility weighted imaging at ultra high magnetic field strengths:theoretical considerations and experimental results. Magnetic Resonce in Medicine.2008; 60:1155-1168.
[64]P.J. Koopmans, R. Manniesing, W.J. Niessen, et al. MR venography of the human brain using susceptibility weighted imaging at very high field strength. MAGMA.2008;21:149-158.
[65]K.E. Hammond, J.M. Lupo, D. Xu, et al. Development of a robust method for generating 7.0 T multichannel phase images of the brain with application to normal volunteers and patients with neurological diseases. Neuroimage.2008;39:1682-1692.
[66]K. T. Bae, S. Park, C. Moon, J. Kim, D. Kaya, and T. Zhao. Dual-echo arteriovenography imaging with 7TMRI. J. Magn. Reson. Imaging.2010:31:225-261.
[67]J. Budde, G. Shajan, J. Hoffmann, K. Ugurbil, and R. Pohmann. Human imaging at 9.4 T using T2*-, phase-, and susceptibility-weighted contrast. Magn Reson Med.2010;24:1-5.
[68]Y.P. Du, Z. Jin. Simultaneous acquisition of MR angiography and venography (MRAV). Magn Reson Med.2008;59:954-958.
[69]Y.P. Du, Z. Jin, Y. Hu, J. Tanabe. Multi-echo acquisition of MR angiography and venography of the brain at 3T. J Magn Reson Imaging.2009;30:449-454.
[70]S. Park, H. Shim, C. Moon, J. Kim, and K. T. Bae. Quantitative evaluation of k-space reordering schemes for compatible dual-echo arteriovenography (CODEA). Magn Reson Med.2010;63:1404-1410.
[71]J. Neelavalli, Y.N.Cheng, J. Jiang and E.M. Haacke. Removing background phase variations in susceptibility-weighted imaging using a fast, forward-field calculation. J Magn Reson Imaging. 2009;29:937-948.
[72]F. Eibofer, G. Steidle, R. Kehlbach, R. Bantleon, and F. Schick. Positive contrast imaging of iron oxide nanoparticles with susceptibility-weighted imaging. Magn Reson Med.2010;64:1027-1038.
[73]D. L. Parker and G.T. Gullberg. Signal-to-noise efficiency in magnetic resonance imaging. Med.Phys.1990;17:250-257.
[74]Z. Jin, L. Xia, Y.P. Du. Reduction of artifacts in susceptibility weighted MR venography of the brain. J Magn Reson Imaging.2008;28:327-333.
[75]E.M. Haacke, Z.P. Liang, S.H. lzen. Constrained reconstruction:a superresolution, optimal signal-to-noise alternative to the Fourier transform in magnetic resonance imaging. Med.Phys. 1989;16:388-397.
[76]E. M. Haacke, Z.P. Liang, F. Boada. Image reconstruction using projection onto convex sets,model constraints, and linear prediction theory for the removal of phase, motion, and Gibbs artifacts in magnetic resonance and ultrasound imaging. Optical Engineering.1990;29:555-566.
[77]D.C. Noll, D.G Nishimura and A. Macovski. Homodyne detection in magnetic resonance imaging. IEEE Transactions on Medical imaging.1991;10:154-163.
[78]E.M. Haacke, E.D. Lindskog, W. Lin. A fast, iterative, partial-fourier technique capable of local phase recovery. Journal of Magnetic Resonance.1991;92:124-145.
[79]Z.P Liang et al, Constrained Reconstruction Methods in MR Imaging, Reviews of Mag. Reson. in Med. 1992; 4:67-185.
[80]T.K.F. Foo and K.F. King. A computationally efficient method for tracking reference position displacements for motion compensation in magnetic resonance imaging. Magn Reson Med. 1999;42:548-553.
[81]J.A. Derbyshire, Y.P. Du and M. Saranathan. The Fourier conjugate of the Ahn algorithm with applications for real-time signal processing for MRI. Proc.Intl.Soc.Magn.Reson.Med.1999; p7.
[82]Y. Xu and E.M. Haacke. Partial fourier imaging in multi-dimensions:A means to save a full factor of two in time. Journal of Magnetic Resonance.2001; 14:628-635.
[83]G. McGibney, M.R. Smith, S.T. Nichols, A. Crawley. Quantitative evaluation of several partial fourier reconstruction algorithms used in MRI. Magn Reson Med.2005;30:51-59.
[84]A.J. Madhuranthakam, H.H. Hu, A.V. Barger, et. al. Undersampled elliptical centric view-order for improved spatial resolution in contrast-enhanced MR angiography. Magn Reson Med.2006;55:50-58.
[85]H. Peng, M. Sabati, L. Lauzon, R. Frayne. MR image reconstruction of sparsely sampled 3D k-space data by projection-onto-convex sets. Magnetic Resonance Imaging.2006; 24:761-773.
[86]Y. P. Du, M. Dalwani, K. Wylie, E. Claus, and J.R. Tregellas. Reducing susceptibility artifacts in fMRI using volume-selective z-shim compensation.2007;57:396-404.
[87]V.A. Stenger, F.E. Boada, and D.C. Noll. Three-dimensional tailored RF pulses for the reduction of susceptibility artifacts in T2*-weighted functional MRI. Magn Reson Med.2000;44:525-531.
[88]D.G Norris, S. Zysset, T. Mildner, and C.J. Wiggins. An investigation of the value of spin-echo-based fMRI using a Stroop color-word matching task and EPI at 3 T. Neuroimage.2002;15:719-726.
[89]GH. Glover, C.S. Law. Spiral-in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts. Magn Reson Med.2001;46:515-522.
[90]Y. Yang, H. Gu, W. Zhan, S. Xu, D.A. Silbersweig, and E. Stern. Simultaneous perfusion and BOLD imaging using reverse spiral scanning at 3T:characterization of functional contrast and susceptibility artifacts. Magn Reson Med.2002;48:278-289.
[91]GH. Glover, M.E. Thomason. Improved combination of spiral-in/out images for BOLD fMRI. Magn Reson Med.2004;51:863-868.
[92]J.L. Wilson, M. Jenkinson, P. Jezzard. Optimization of static field homogeneity in human brain using diamagnetic passive shims. Magn Reson Med.2002;48:906-914.
[93]J.L. Wilson, P. Jezzard. Utilization of an intra-oral diamagnetic passive shim in functional MRI of the inferior frontal cortex. Magn Reson Med.2003;50:1089-1094.
[94]J.J. Hsu, GH. Glover. Mitigation of susceptibility-induced signal loss in neuroimaging using localized shim coils. Magn Reson Med.2005;53:243-248.
[95]A. Rauscher, M. Barth, K.H. Herrmann, S. Witoszynskyja, A. Deistunga, J.R. Reichenbacha. Improved elimination of phase effects from background field inhomogeneities for susceptibility weighted imaging at high magnetic field strengths. Magnetic Resonance Imaging.2008;26:1145-51.
[96]Y.P. Du, Z. Jin. Robust tissue-air volume segmentation of MRI based on the statistics of phase and magnitude:its applications in the display of susceptibility weighted imaging of the brain. J Magn Reson Imaging.2009;30:722-731.
[97]Z. Jin, L. Xia, Y.P. Du. Semi-automated volume segmentation of the brain based on a level set approach. Proc.Intl.Soc.Magn.Reson.Med.2009;p2870, Honolulu, Hawaii.
[98]R. Adams, L. Bischof. Seeded region growing. IEEE Trans. on Pattern Anal Mach. Intelligence.1994; 16:641-647.
[99]M. Kass, A. Kitkin and D. Terzopoulos. Snakes:active contour models. Int. J. Comput. Vis. 1987;1:321-331.
[100]S. Osher, J. A. Sethian. Fronts propagating with curvaturedependent speed:algorithms based on Hamilton-Jacobi formulations. J. Comp. Phys.1988:79:12-49.
[101]S. Osher and R. Fedkiw. Level Set Methods and Dynamic Implicit Surfaces. Springer-Verlag, New York,2002.
[102]J.A. Sethian. Level set and fast marching methods.Cambridge University Press, Cambridge (1999).
[103]V. Caselles, R. Kimmel and G. Sapiro. Geodesic active contours. Int. J. Comput. Vis.1997;22: 61-79.
[104]Z. Liang and S. Wang. An EM approach to MAP solution of segmenting tissue mixtures:a numerical analysis. IEEE Transactions on Medical Imaging.2009; 28:297-310.
[105]T. Chan and L. Vese. Active contours without edges. IEEE Tans. Image Processing.2001; 10:266-277.
[106]L. Vese and T. Chan. A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis.2002; 50:271-293.
[107]C. Li, C. Xu, C. Gui and M. D. Fox. Level set evolution without re-initialization:a new variational formulation. CVPR.2005; 1:430-436.
[108]C. Li, C. Xu, K. Konwar and M. Fox. Fast distance preserving level set evolution for medical image segmentation. ICARCV.2006; p1-7.
[109]C. Li, C. Kao, J. Gore and Z. Ding. Implicit active contours driven by local binary fitting energy. CVPR.2007; 3:1-7.
[110]C. Li, C. Kao, J. Gore and Z. Ding. Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Processing.2008; 17:1940-1949.
[111]C. Li, R. Huang, Z. Ding, C. Gatenby, D. Metaxas and J. Gore. A variational level set approach to segmentation and bias correction of medical images with intensity inhomogeneity. MICCAI, 2008;p1083-1091.
[112]Y.P. Du, D.L. Parker, and W.L. Davis. Vessel enhancement filtering in three-dimensional MR angiography. J Magn Reson Imaging.1995; 5:353-59.
[113]Y.P. Du, D.L. Parker. Vessel enhancement filtering in three-dimensional MR angiograms using long-range signal correction. J Magn Reson Imaging.1997;7:447-450.
[114]M.M. Orkisz, C. Bresson, I.E. Magnin, O. Champin, and P.C. Douek. Improved vessel visualization in MR angiography by nonlinear anisotropic filtering. Magn Reson Med.1997;37:914-919.
[115]D.L. Parker, B.E. Chapman, J.A. Roberts, A.L. Alexander, and J.S. Tsuruda. Enhanced image detail using continuity in the MIP Z-buffer:application to magnetic resonance angiography. J Magn. Reson Imaging.2000; 11:378-388.
[116]B.E. Chapman, J.O. Stapelton, and D.L. Parker. Intracranial vessel segmentation from time-of-flight MRA using pre-processing of the MIP Z-buffer:accuracy of the ZBS algorithm. Medical Image Analysis. 2004;8:113-126.
[117]B.E. Chapman, D.L. Parker, J.O. Stapelton, J.S. Tsuruda, C. Mello-Thoms, B. Hamilton, G.L. Katzman, and K. Moore. Diagnostic fidelity of the Z-buffer segmentation algorithm:preliminary assessment based on intracranical aneurysm detection. Journal of Biomedical Informatics. 2004;37:19-29.
[118]T.M. Koller, G. Gerig, G. Szekely and D. Dettwiler. Multiscale detection of curvilinear structures in 2-D and 3-D image data. In:Proceedings of the Fifth International Conference on Computer Vision. Cambridge, MA, USA.1995;p864-869.
[119]C. Lorenz, L.C. Carlsen, T.M. Buzug, C. Fassnacht and J. Weese. Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images. In: Computer Vision, Virtual Reality and Robotics in Medicine and Medical Robotics and Computer-Assisted Surgery. Berlin, Germany:Springer Verlag.1997; p233-242.
[120]K. Karissian, G. Malandain, N. Ayache. Model based multiscale detection of 3D vessels. In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 98). California, USA.1998;p722-727.
[121]Y. Sato, S. Nakajima, N. Shiraga, et al. Three dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis.1998;2:143-168.
[122]A.F. Frangi, W.J. Niessen, K.L. Vincken, M.A. Viergever. Multiscale vessel enhancement filtering. In:Medical Image Computing and Computer-Assisted Intervention, Berlin, Germany:Springer Verlag, 1998, pp.130-137.
[123]B.E. Chapman, D.L. Parker.3D multi-scale vessel enhancement filtering based on curvature measurements:application to time-of-flight MRA. Medical Image Analysis.2005;9:191-208.
[124]T. Lindeberg. On scale selection for differential operators. In:Proceedings of 8th Scandinavian Conference on Image Analysis. Tromso, Norway.1993;p857-866.
[125]D.L. Parker, Y.P. Du, and W.L. Davis. The voxel sensitivity function in Fourier transform imaging: applications to magnetic resonance angiography. Magn Reson Med.1995;33:156-62.
[126]F.W. Wehrli, A. Shimakawa, G.T. Gullberg, J.R. MacFall. Time-of-flight MR flow imaging: selective saturation recovery with gradient refocusing. Radiology.1986;160:781-5.
[127]D.L. Parker, C. Yuan, D.D. Blatter. MR angiography by multiple thin slab 3D acquisition. Magn Reson Med.1991; 17:434-51.
[128]M.A. Bernstein, J. Huston, C. Lin, G.F. Gibbs, J.P. Felmlee. High-resolution intracranial and cervical MRA at 3.0T:technical considerations and initial experience. Magn Reson Med. 2001:46:955-62.
[129]T.C. Lauenstein, K. Salman, R. Morreira, S. Tata, D. Tudorascu, G. Baramidze, S. Singh-Parker, D.R. Martin. Nephrogenic systemic fibrosis:center case review. J Magn Reson Imaging. 2007;26:1198-1203.
[130]D. Atkinson, M. Brant-Zawadzki, G Gillan, D. Purdy, G Laub. Improved MR angiography: magnetization transfer suppression with variable flip angle excitation and increased resolution. Radiology. 1994;190:890-4.
[131]Y.P. Du. Three-dimensional mapping of local field gradient in fMRI. In:Proceedings of the 11th Annual Meeting of ISMRM, Toronto, Canada.2003; p1714.
[132]M.A. Bernstein, S.B. Fain, S.J. Riederer. Effect of windowing and zero-filled reconstruction of MRI data on spatial resolution and acquisition strategy. J Magn Reson Imaging.2001;14:270-280.
[133]GA. Baxes. Digital image processing:principles and applications. New York:John Wiley and Sons Inc.1994. p129-135.
[134]S. Chavez, Q.S. Xiang, L. An. Understanding phase maps in MRI:a new cutline phase unwrapping method. IEEE Trans Med Imaging.2002;21:966-977.
[135]J. Frahm, K.D. Merboldt, W. Hanicke. Direct FLASH MR imaging of magnetic field inhomogeneities by gradient compensation. Magn Reson Med.1988;6:474-480.
[136]D.S Pandian, C. Ciulla, E.M Haacke, J. Jiang, M. Ayaz. Complex threshold method for identifying pixels that contain predominantly noise in magnetic resonance images. J Magn Reson Imaging. 2008;28:727-735.
[137]G Gilbert. Measurement of signal-to-noise ratios in sum-of-squares MR images. J Magn Reson Imaging.2007;26:1678;
[138]N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybernet. 1979;9:62-66.
[139]S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi. Optimization by simulated annealing. Science, 1983;220:671-680.
[140]H. Chen, J. Hale. An algorithm for angiography image enhancement. Magn Reson Med. 1995;33:534-540.
[141]E.G. Kholmovski, D.L. Parker. High resolution magnetic resonance venography at 3 Tesla: optimized acquisition, reconstruction, and post-processing. In:Proceedings of the 14th Annual Meeting of ISMRM, Seattle, Washington, USA.2006;p810.
[142]P.B. Roemer, W.A. Edelstein, C.E. Hayes, S.P. Souza, and O.M. Mueller. The NMR phased array. Magn Reson Med 1990;16:192-225.
[143]Y.P. Du, D.L. Parker, W.L. Davis, G. Cao. Reduction of partial-volume artifacts using zero-filled interpolation in three-dimensional MR angiography. J Magn Reson Imaging.1994;4:733-741.
[144]M.A. Griswold, P.M. Jakob, R.M. Heidemann, M. Nittka, V. Jellus, J. Wang, B. Kiefer, A. Haase. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med, 2002;47:1202-1210.
[145]C. Denk, BSc, A. Rauscher, Susceptibility weighted imaging with multiple echoes. J Magn Reson Imaging.2010;31:185-191.
[146]M.J. Lowe, J.A. Sorenson. Spatially filtering functional magnetic resonance imaging data. Magn Reson Med.1997;37:723-729.
[147]M.W. Woolrich, S. Jbabdi, B. Patenaude, M. Chappell, S. Makni, T. Behrens, C. Beckmann, M. Jenkinson, S.M. Smith. Bayesian analysis of neuroimaging data in FSL. NeuroImage.2009;45:S173-186.
[148]S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, L.M. De, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N.D. Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage.2004;23(S1):208-19.
[149]S.D. Wolff, J. Eng, R.S. Balaban. Magnetization transfer contrast:method for improving contrast in gradient-recalled-echo images. Radiology.1991;179:133-137.
[150]A.L. Alexander, B.E. Chapman, J.S. Tsuruda, D.L. Parker. A median filter for 3D FAST spin echo black blood images of cerebral vessels. Magn Reson Med.2000;43:310-313.
[151]K.P. Pruessmann, M. Weiger, M.B. Scheidegger, P. Boesiger. SENSE:sensitivity encoding for fast MRI. Magn Reson Med.1999;42:952-962.
[152]D.K. Sodickson, W.J. Manning. Simultaneous acquisition of spatial harmonics (SMASH):ultra-fast imaging with radiofrequency coil arrays. Magn Reson Med.1997;38:591-603.
[153]W.E. Kyriakos, L.P. Panych, D.F. Kacher, C.F. Westin, S.M. Bao, R.V. Mulkern and F.A. Jolesz. Sensitivity profiles from an array of coils for encoding and reconstruction in parallel (SPACE RIP). Magn Reson Med.2000;44:301-308.
[154]J. Tsao, P. Boesiger, and K.P. Pruessmann. k-t BLAST and k-t SENSE:dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn Reson Med.2003; 50:1031-1042.
[155]Y. Qian, Z. Zhang, V.A. Stenger, and Y. Wang. Self-Calibrated spiral SENSE. Magn Reson Med. 2003;52:688-692.
[156]N, Seiberlich, F. Breuer, M. Blaimer, P. Jakob, M. Griswold. Self-calibrating GRAPPA operator gridding for radial and spiral trajectories.2008;59:930-935.
[157]T. Kubo, M. Horii, Y. Harada, et al. Radial-sequence magnetic resonance imaging in evaluation of acetabular labrum. Journal of Orthopaedic Science.1999;4:328-332.
[158]E.J. Candes, J. Romberg, and T. Tao. Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory.2006;52:489-509.
[159]D.L. Donoho. Compressed sensing. IEEE Trans. Inform. Theory.2006:52:1289-1306.
[160]M. Lustig, D.L. Donoho, and J. M. Pauly. Sparse MRI:The application of compressed sensing for rapid MR imaging. Magn Reson Med.2007;58:1182-1195.
[161]U. Gamper, P. Boesiger, and S. Kozerke. Compressed sensing in dynamic MRI. Magn Reson Med. 2008; 59:365-373.
[2]E.M. Haacke, R.W. Brown, M.R. Thompson, R. Venktesan. Magnetic resonance imaging:physical principles and sequence design. Wiley, New York.1999.
[3]杨正汉,冯逢,王霄英.磁共振成像技术指南.人民军医出版社.2007.
[4]J.R. Reichenbach, R. Venkatesan, D.J. Schillinger, D.K. Kido, E.M. Haacke. Small vessels in the human brain:MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology.1997; 204:272-277.
[5]J.R. Reichenbach, M. Essig, E.M. Haacke, B.C. Lee, C. Przetak, W.A. Kaiser, L.R. Schad. High-resolution venography of the brain using magnetic resonance imaging. MAGMA.1998; 6:62-69.
[6]E.M. Haacke, Y. Xu, Y.C. Cheng, J.R. Reichenbach. Susceptibility weighted imaging (SWI). Magn Reson Med.2004;52:612-618.
[7]K.R. Thulborn, J.C. Waterton, P.M. Matthews, C.K. Radda. Oxygenation dependence of the transverse relaxation time of water protons in whole blood at high field. Biochern Biophys Acta.1982; 714:265-270.
[8]R.M. Weisskoff, S. Kiihne. MRI susceptometry:image-based measurement of absolute susceptibility of MR contrast agents and human blood. Magn Reson Med 1992;24:375-383.
[9]E.M. Haacke, S. Mittal, Z. Wu, J. Nelavalli, Y. N. Cheng. Susceptibility-weighted imaging:technical aspects and clinical applications, part1. AJNR.2009; 30:1-12.
[10]S. Mittal, Z. Wu, J. Nelavalli, E.M. Haacke. Susceptibility-weighted imaging:technical aspects and clinical applications, part2. AJNR.2009; 30:232-252.
[11]B. C Ong and S. L Stuckey. Susceptibility weighted imaging:a pictorial review. Journal of Medical imaging and radiation oncology.2010;54:435-449.
[12]K.T. Baudendistel, J.R. Reichenbach, R. Metzner, J. Schroder, L.R. Schad. Comparison of functional venography and EPI-BOLD-fMRI at 1.5T. Magn Reson Imaging.1998; 16:989-991.
[13]B.C.P. Lee, K.D. Vo, D.K. Kido, P. Mukherjee, J.R. Reichenbach, W. Lin, M.S. Yoon, E.M. Haacke. MR high-resolution blood oxygenation level-dependent venography of occult (low-flow) vascular lesions. AJNR.1999; 20:1239-1242.
[14]I.L. Tan, R.A. van Schijndel, P.J.W. Pouwels, M.A.A. van Walderveen, J.R. Reichenbach, R. A. Manoliu and F. Barkhof. MR venography of multiple sclerosis. AJNR.2000; 21:1039-1042.
[15]W. Lin, P. Mukherjee, H. An, Y. Yu, Y. Wang, K. Vo, B. Lee, D. Kido, E.M. Haacke. Improving high-resolution MR bold venographic imaging using a T1 reducing contrast agent. J Magn Reson Imaging.1999; 10:118-123.
[16]J.R. Reichenbach, L. Jonetz-Mentzel, C. Fitzek, E.M. Haacke, D.K. Kido, B.C.P. Lee, W.A. Kaiser. High-resolution blood oxygen-level dependent MR venography (HRBV):A new technique. Neuroradiology.2001; 43:364-369.
[17]J. R. Reichenbach, E. M. Haacke. High-resolution BOLD venographic imaging:A window into brain function. NMR in Biomedicine.2001; 14:453-467.
[18]L. Schad. Improved target volume characterization in stereotactic treatment planning of brain lesions by using high-resolution BOLD MR-venography. NMR in Biomedicine.2001;14:478-483.
[19]J.R. Reichenbach, M. Barth, E.M. Haacke, M.Klarhofer, W.A. Kaiser, E. Moser. High-resolution MR venography at 3 Tesla. JCAT.2000; 24:949-957.
[20]M. Barth, I.M. Nobauer-Huhmann, J.R. Reichenbach, V. Mlynarik, A. Schoggl, C. Matula, S. Trattnig. Enhanced BOLD MR-venography (CE-MRV) of brain tumors at 3 Tesla:first clinical experience. Investigative Radiology.2003; 38:409-414.
[21]R.A. Dashner, D.W. Chakeres, A. Kangarlu, P. Schmalbrock, G. A. Christoforidis, R.M. DePhilip. MR imaging visualization of the cerebral microvasculature:a comparison of live and postmortem studies at 8 T. AJNR.2003; 24:1881-1884.
[22]K.A. Tong, S. Ashwal, B.A. Holshouser, L. Shutter, G. Herigault, E.M. Haacke, D.K. Kido. Improved detection of hemorrhagic shearing lesions in children with post-traumatic diffuse axonal injury-initial results. Radiology.2003; 227:332-339.
[23]A. Rauscher, M. Barth, J.R. Reichenbach, R. Stollberger, E. Moser. Automated unwrapping of MR phase images applied to BOLD MR-venography at 3 Tesla. J Magn Reson Imaging.2003; 18:175-180.
[24]M. Essig, J.R.Reichenbach, L.R. Schad, S.O. Schonberg, J. Debus, W.A. Kaiser. High-resolution MR venography of cerebral arteriovenous malformations. Magn Reson Imaging.1999; 17:1417-1425.
[25]Y. Wang, Y. Yu, D. Li, et al. Artery and vein separation using susceptibility dependent phase in contrast-enhanced MRA. J Magn Reson Imaging.2000; 12:661-670.
[26]A. Rauscher, J. Sedlacik, M. Barth, et al. Noninvasive assessment of vascular architecture and function during modulated blood oxygenation using susceptibility weighted magnetic resonance imaging. Magn Reson Med.2005;54:87-95.
[27]A. Rauscher, J. Sedlacik, M. Barth, et al. Magnetic susceptibility-weighted MR phase imaging of the human brain. AJNR.2005; 26:736-42.
[28]V. Sehgal, Z. Delproposto, D. Haddar, et al. Susceptibility-weighted imaging to visualize blood products and improve tumor contrast in the study of brain masses. J Magn Reson Imaging.2006; 24:41-51.
[29]B.C. Hamans, M. Barth, W.P. Leenders, et al. Contrast enhanced susceptibility weighted imaging (CE-SWI) of the mouse brain using ultrasmall superparamagnetic iron oxide particles (USPIO). Z Med Phys.2006; 16:269-274.
[30]S. Ashwal, T. Babikian, J. Gardner-Nichols, et al. Susceptibility-weighted imaging and proton magnetic resonance spectroscopy in assessment of outcome after pediatric traumatic brain injury. Arch Phys Med Rehabil.2006; 87:850-858.
[31]Y. Yoshida, S. Terae, K. Kudo, et al. Capillary telangiectasia of the brain stem diagnosed by susceptibility-weighted imaging. J Comput Assist Tomogr.2006; 30:980-982.
[32]I.M. Noebauer-Huhmann, K. Pinker, M. Barth, et al. Contrast-enhanced, highresolution, susceptibility-weighted magnetic resonance imaging of the brain:dose-dependent optimization at 3 Tesla and 1.5 Tesla in healthy volunteers. Invest Radiol.2006; 41:249-255.
[33]Z. Wu, S. Mitta, et al. Identification of calcification with MRI using susceptibility-weighted imaging:a case study. J Magn Reson Imaging.2009;29:177-182.
[34]E.M. Haacke, N.Y.C. Cheng, Q. Liu, J. Neelavalli, Robert Ogg, A. Khan, M. Ayaz, W. Kirsch, A. Obenaus. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging. 2005; 23:1-25.
[35]E.M Haacke, M. Ayaz, A. Khan, et al. Establishing a baseline phase behavior in magnetic resonance imaging to determine normal vs. abnormal iron content in the brain. J Magn Reson Imaging.2007; 26:256-264.
[36]E.M Haacke, Z.S. DelProposto, S. Chaturvedi, et al. Imaging cerebral amyloid angiopathy with susceptibility-weighted imaging. AJNR.2007;28:316-317.
[37]E.M. Haacke, et al. Characterizing iron deposition in multiple sclerosis lesions using susceptibility weighted imaging. J Magn Reson Imaging.2009;29:537-544.
[38]G. McAuley, M. Schrag, P. Sipos, S. Sun, A. Obenaus, J. Neelavalli, E. M. Haacke, B. Holschouser, R. Madacsi, and W. Kirsch. Quantification of punctate iron sources using magnetic resonance phase. Magn Reson Med.2010;63:106-115.
[39]L. d. Rochefort, T. Liu, B. Kressler, J. Liu, P. Sincemaille, V. Lebon, J. Wu, and Y. Wang. Quantitative susceptibility map reconstruction from MR phase data using Bayesian regularization:validation and application to brain imaging. Magn Reson Med.2010;63:194-206.
[40]E.M. Haacke, J. Tang, J. Neelavalli, and Y. Cheng. Susceptibility mapping as a means to visualize veins and quantify oxygen saturation. J. Magn. Reson. Imaging.2010:32:663-676
[41]S. Wharton, A. Schafer, and R. Bowtell. Susceptibility mapping in the human brain using threshold-based k-space division. Magn Reson Med.2010;63:1292-1304.
[42]J. Sedlacik and J. Reichenbach. Validation of quantitative estimation of tissue oxygen extraction fraction and deoxygenated blood volume fraction in phantom and in vivo experiments by using MRI. Magn Reson Med.2010;63:910-921.
[43]J. Sedlacik, A. Rauscher, J.R. Reichenbach. Obtaining blood oxygenation levels from MR signal behavior in the presence of single venous vessels. Magn Reson Med.2007;58:1035-1044.
[44]R.M. Henkelman. Measurement of signal intensities in the presence of noise in MR images. Med.Phys.1985;12:232-233.
[45]Y. Xu, E.M. Haacke. The role of voxel aspect ratio in determining apparent vascular phase behavior in susceptibility weighted imaging. Magn Reson Imaging.2006; 24:155-160.
[46]V. Sehgal, Z. Delproposto, E.M Haacke, K. Tong, N. Wycliffe, D.K. Kido, Y. Xu, J. Neelavalli, D. Haddar, J.R. Reichenbach. Clinical applications of neuroimaging with susceptibility weighted imaging. J Magn Reson Imaging.2005; 22:439-450.
[47]D. S. Vinod, P.S. Bindu, S. Ravishankar, et al. Relaxation and susceptibility MRI characteristics in Hallervorden-Spatz syndrome. J Magn Reson Imaging.2007;25:715-720.
[48]M. Akter, T. Hirai, Y. Hiai, et al. Detection of hemorrhagic hypointense foci in the brain on susceptibility-weighted imaging clinical and phantom studies. Acad Radiol.2007;14:1011-1019.
[49]J.P. Larsen, W.I. Britt, D. Kido, et al. Susceptibility weighted magnetic resonance imaging in the evaluation of dementia. Radiology Case Reports.2007; 2:102.
[50]K. Pinker, I.M. Noebauer-Huhmann, I. Stavrou, et al. High-resolution contrastenhanced, susceptibility-weighted MR imaging at 3T in patients with brain tumors:correlation with positron-emission tomography and histopathologic findings. AJNR.2007;28:1280-1286.
[51]Y. Shen, Z. Kou, C.W. Kreipke, et al. In vivo measurement of tissue damage, oxygen saturation changes and blood flow changes after experimental traumatic brain injury in rats using susceptibility weighted imaging. Magn Reson Imaging.2007;25:219-227.
[52]H.J. Mentzel, A. Dieckmann, C. Fitzek, et al. Early diagnosis of cerebral involvement in Sturge-Weber syndrome using high-resolution BOLD MR venography. Pediatr Radiol.2005;35:85-90.
[53]T. Babikian, M.C. Freier, K.A. Tong, et al. Susceptibility weighted imaging:neuropsychologic outcome and pediatric head injury. Pediatr Neurol.2005;33:184-194.
[54]J. Hu, Y. Yu, C. Juhasz, et al. MR susceptibility weighted imaging (SWI) complements conventional contrast enhanced T1 weighted MRI in characterizing brain abnormalities of Sturge-Weber syndrome. J Magn Reson Imaging.2008;28:300-307.
[55]B. Thomas, S. Somasundaram, K. Thamburaj, et al. Clinical applications of susceptibility weighted MR imaging of the brain:a pictorial review. Neuroradiology.2008;50:105-116.
[56]J.M. de Souza, R.C. Domingues, L.C. Cruz, et al. Susceptibility-weighted imaging for the evaluation of patients with familial cerebral cavernous malformations; a comparison with T2-weighted fast spin-echo and gradient-echo sequences. AJNR.2008;29:154-158.
[57]S.L. Harder, K.M. Hopp, H. Ward, et al. Mineralization of the deep gray matter with age:a retrospective review with susceptibility-weighted MR imaging. AJNR.2008;29:176-183.
[58]J. Sedlacik, K. Helm, A. Rauscher, et al. Investigations on the effect of caffeine on cerebral venous vessel contrast by using susceptibility-weighted imaging (SWI) at 1.5,3 and 7 T. Neuroimage.2008;40:11-18.
[59]S. Somasundaram, C. Kesavadas, B. Thomas. Susceptibility weighted imaging in holohemispheric venous angioma with cerebral hemiatrophy. Neurol India.2008;56:104-105.
[60]H.L. Liu, Y.Y. Wai, W.S. Chen, et al. Hemorrhage detection during focused-ultrasound induced blood-brain-barrier opening by using susceptibility-weighted magnetic resonance imaging. Ultrasound Med Biol.2008;34:598-606.
[61]Y. Fushimi, Y. Miki, K. Togashi, et al. A developmental venous anomaly presenting atypical findings on susceptibility-weighted imaging. AJNR.2008;29:E56.
[62]K. Zhong, J. Leupold, D. von Elverfeldt, et al. The molecular basis for gray and white matter contrast in phase imaging. Neuroimage.2008;40:1561-1566.
[63]A. Deistung, A. Rauscher, J. Sedlacik, et al. Susceptibility weighted imaging at ultra high magnetic field strengths:theoretical considerations and experimental results. Magnetic Resonce in Medicine.2008; 60:1155-1168.
[64]P.J. Koopmans, R. Manniesing, W.J. Niessen, et al. MR venography of the human brain using susceptibility weighted imaging at very high field strength. MAGMA.2008;21:149-158.
[65]K.E. Hammond, J.M. Lupo, D. Xu, et al. Development of a robust method for generating 7.0 T multichannel phase images of the brain with application to normal volunteers and patients with neurological diseases. Neuroimage.2008;39:1682-1692.
[66]K. T. Bae, S. Park, C. Moon, J. Kim, D. Kaya, and T. Zhao. Dual-echo arteriovenography imaging with 7TMRI. J. Magn. Reson. Imaging.2010:31:225-261.
[67]J. Budde, G. Shajan, J. Hoffmann, K. Ugurbil, and R. Pohmann. Human imaging at 9.4 T using T2*-, phase-, and susceptibility-weighted contrast. Magn Reson Med.2010;24:1-5.
[68]Y.P. Du, Z. Jin. Simultaneous acquisition of MR angiography and venography (MRAV). Magn Reson Med.2008;59:954-958.
[69]Y.P. Du, Z. Jin, Y. Hu, J. Tanabe. Multi-echo acquisition of MR angiography and venography of the brain at 3T. J Magn Reson Imaging.2009;30:449-454.
[70]S. Park, H. Shim, C. Moon, J. Kim, and K. T. Bae. Quantitative evaluation of k-space reordering schemes for compatible dual-echo arteriovenography (CODEA). Magn Reson Med.2010;63:1404-1410.
[71]J. Neelavalli, Y.N.Cheng, J. Jiang and E.M. Haacke. Removing background phase variations in susceptibility-weighted imaging using a fast, forward-field calculation. J Magn Reson Imaging. 2009;29:937-948.
[72]F. Eibofer, G. Steidle, R. Kehlbach, R. Bantleon, and F. Schick. Positive contrast imaging of iron oxide nanoparticles with susceptibility-weighted imaging. Magn Reson Med.2010;64:1027-1038.
[73]D. L. Parker and G.T. Gullberg. Signal-to-noise efficiency in magnetic resonance imaging. Med.Phys.1990;17:250-257.
[74]Z. Jin, L. Xia, Y.P. Du. Reduction of artifacts in susceptibility weighted MR venography of the brain. J Magn Reson Imaging.2008;28:327-333.
[75]E.M. Haacke, Z.P. Liang, S.H. lzen. Constrained reconstruction:a superresolution, optimal signal-to-noise alternative to the Fourier transform in magnetic resonance imaging. Med.Phys. 1989;16:388-397.
[76]E. M. Haacke, Z.P. Liang, F. Boada. Image reconstruction using projection onto convex sets,model constraints, and linear prediction theory for the removal of phase, motion, and Gibbs artifacts in magnetic resonance and ultrasound imaging. Optical Engineering.1990;29:555-566.
[77]D.C. Noll, D.G Nishimura and A. Macovski. Homodyne detection in magnetic resonance imaging. IEEE Transactions on Medical imaging.1991;10:154-163.
[78]E.M. Haacke, E.D. Lindskog, W. Lin. A fast, iterative, partial-fourier technique capable of local phase recovery. Journal of Magnetic Resonance.1991;92:124-145.
[79]Z.P Liang et al, Constrained Reconstruction Methods in MR Imaging, Reviews of Mag. Reson. in Med. 1992; 4:67-185.
[80]T.K.F. Foo and K.F. King. A computationally efficient method for tracking reference position displacements for motion compensation in magnetic resonance imaging. Magn Reson Med. 1999;42:548-553.
[81]J.A. Derbyshire, Y.P. Du and M. Saranathan. The Fourier conjugate of the Ahn algorithm with applications for real-time signal processing for MRI. Proc.Intl.Soc.Magn.Reson.Med.1999; p7.
[82]Y. Xu and E.M. Haacke. Partial fourier imaging in multi-dimensions:A means to save a full factor of two in time. Journal of Magnetic Resonance.2001; 14:628-635.
[83]G. McGibney, M.R. Smith, S.T. Nichols, A. Crawley. Quantitative evaluation of several partial fourier reconstruction algorithms used in MRI. Magn Reson Med.2005;30:51-59.
[84]A.J. Madhuranthakam, H.H. Hu, A.V. Barger, et. al. Undersampled elliptical centric view-order for improved spatial resolution in contrast-enhanced MR angiography. Magn Reson Med.2006;55:50-58.
[85]H. Peng, M. Sabati, L. Lauzon, R. Frayne. MR image reconstruction of sparsely sampled 3D k-space data by projection-onto-convex sets. Magnetic Resonance Imaging.2006; 24:761-773.
[86]Y. P. Du, M. Dalwani, K. Wylie, E. Claus, and J.R. Tregellas. Reducing susceptibility artifacts in fMRI using volume-selective z-shim compensation.2007;57:396-404.
[87]V.A. Stenger, F.E. Boada, and D.C. Noll. Three-dimensional tailored RF pulses for the reduction of susceptibility artifacts in T2*-weighted functional MRI. Magn Reson Med.2000;44:525-531.
[88]D.G Norris, S. Zysset, T. Mildner, and C.J. Wiggins. An investigation of the value of spin-echo-based fMRI using a Stroop color-word matching task and EPI at 3 T. Neuroimage.2002;15:719-726.
[89]GH. Glover, C.S. Law. Spiral-in/out BOLD fMRI for increased SNR and reduced susceptibility artifacts. Magn Reson Med.2001;46:515-522.
[90]Y. Yang, H. Gu, W. Zhan, S. Xu, D.A. Silbersweig, and E. Stern. Simultaneous perfusion and BOLD imaging using reverse spiral scanning at 3T:characterization of functional contrast and susceptibility artifacts. Magn Reson Med.2002;48:278-289.
[91]GH. Glover, M.E. Thomason. Improved combination of spiral-in/out images for BOLD fMRI. Magn Reson Med.2004;51:863-868.
[92]J.L. Wilson, M. Jenkinson, P. Jezzard. Optimization of static field homogeneity in human brain using diamagnetic passive shims. Magn Reson Med.2002;48:906-914.
[93]J.L. Wilson, P. Jezzard. Utilization of an intra-oral diamagnetic passive shim in functional MRI of the inferior frontal cortex. Magn Reson Med.2003;50:1089-1094.
[94]J.J. Hsu, GH. Glover. Mitigation of susceptibility-induced signal loss in neuroimaging using localized shim coils. Magn Reson Med.2005;53:243-248.
[95]A. Rauscher, M. Barth, K.H. Herrmann, S. Witoszynskyja, A. Deistunga, J.R. Reichenbacha. Improved elimination of phase effects from background field inhomogeneities for susceptibility weighted imaging at high magnetic field strengths. Magnetic Resonance Imaging.2008;26:1145-51.
[96]Y.P. Du, Z. Jin. Robust tissue-air volume segmentation of MRI based on the statistics of phase and magnitude:its applications in the display of susceptibility weighted imaging of the brain. J Magn Reson Imaging.2009;30:722-731.
[97]Z. Jin, L. Xia, Y.P. Du. Semi-automated volume segmentation of the brain based on a level set approach. Proc.Intl.Soc.Magn.Reson.Med.2009;p2870, Honolulu, Hawaii.
[98]R. Adams, L. Bischof. Seeded region growing. IEEE Trans. on Pattern Anal Mach. Intelligence.1994; 16:641-647.
[99]M. Kass, A. Kitkin and D. Terzopoulos. Snakes:active contour models. Int. J. Comput. Vis. 1987;1:321-331.
[100]S. Osher, J. A. Sethian. Fronts propagating with curvaturedependent speed:algorithms based on Hamilton-Jacobi formulations. J. Comp. Phys.1988:79:12-49.
[101]S. Osher and R. Fedkiw. Level Set Methods and Dynamic Implicit Surfaces. Springer-Verlag, New York,2002.
[102]J.A. Sethian. Level set and fast marching methods.Cambridge University Press, Cambridge (1999).
[103]V. Caselles, R. Kimmel and G. Sapiro. Geodesic active contours. Int. J. Comput. Vis.1997;22: 61-79.
[104]Z. Liang and S. Wang. An EM approach to MAP solution of segmenting tissue mixtures:a numerical analysis. IEEE Transactions on Medical Imaging.2009; 28:297-310.
[105]T. Chan and L. Vese. Active contours without edges. IEEE Tans. Image Processing.2001; 10:266-277.
[106]L. Vese and T. Chan. A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vis.2002; 50:271-293.
[107]C. Li, C. Xu, C. Gui and M. D. Fox. Level set evolution without re-initialization:a new variational formulation. CVPR.2005; 1:430-436.
[108]C. Li, C. Xu, K. Konwar and M. Fox. Fast distance preserving level set evolution for medical image segmentation. ICARCV.2006; p1-7.
[109]C. Li, C. Kao, J. Gore and Z. Ding. Implicit active contours driven by local binary fitting energy. CVPR.2007; 3:1-7.
[110]C. Li, C. Kao, J. Gore and Z. Ding. Minimization of region-scalable fitting energy for image segmentation. IEEE Trans. Image Processing.2008; 17:1940-1949.
[111]C. Li, R. Huang, Z. Ding, C. Gatenby, D. Metaxas and J. Gore. A variational level set approach to segmentation and bias correction of medical images with intensity inhomogeneity. MICCAI, 2008;p1083-1091.
[112]Y.P. Du, D.L. Parker, and W.L. Davis. Vessel enhancement filtering in three-dimensional MR angiography. J Magn Reson Imaging.1995; 5:353-59.
[113]Y.P. Du, D.L. Parker. Vessel enhancement filtering in three-dimensional MR angiograms using long-range signal correction. J Magn Reson Imaging.1997;7:447-450.
[114]M.M. Orkisz, C. Bresson, I.E. Magnin, O. Champin, and P.C. Douek. Improved vessel visualization in MR angiography by nonlinear anisotropic filtering. Magn Reson Med.1997;37:914-919.
[115]D.L. Parker, B.E. Chapman, J.A. Roberts, A.L. Alexander, and J.S. Tsuruda. Enhanced image detail using continuity in the MIP Z-buffer:application to magnetic resonance angiography. J Magn. Reson Imaging.2000; 11:378-388.
[116]B.E. Chapman, J.O. Stapelton, and D.L. Parker. Intracranial vessel segmentation from time-of-flight MRA using pre-processing of the MIP Z-buffer:accuracy of the ZBS algorithm. Medical Image Analysis. 2004;8:113-126.
[117]B.E. Chapman, D.L. Parker, J.O. Stapelton, J.S. Tsuruda, C. Mello-Thoms, B. Hamilton, G.L. Katzman, and K. Moore. Diagnostic fidelity of the Z-buffer segmentation algorithm:preliminary assessment based on intracranical aneurysm detection. Journal of Biomedical Informatics. 2004;37:19-29.
[118]T.M. Koller, G. Gerig, G. Szekely and D. Dettwiler. Multiscale detection of curvilinear structures in 2-D and 3-D image data. In:Proceedings of the Fifth International Conference on Computer Vision. Cambridge, MA, USA.1995;p864-869.
[119]C. Lorenz, L.C. Carlsen, T.M. Buzug, C. Fassnacht and J. Weese. Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images. In: Computer Vision, Virtual Reality and Robotics in Medicine and Medical Robotics and Computer-Assisted Surgery. Berlin, Germany:Springer Verlag.1997; p233-242.
[120]K. Karissian, G. Malandain, N. Ayache. Model based multiscale detection of 3D vessels. In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 98). California, USA.1998;p722-727.
[121]Y. Sato, S. Nakajima, N. Shiraga, et al. Three dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis.1998;2:143-168.
[122]A.F. Frangi, W.J. Niessen, K.L. Vincken, M.A. Viergever. Multiscale vessel enhancement filtering. In:Medical Image Computing and Computer-Assisted Intervention, Berlin, Germany:Springer Verlag, 1998, pp.130-137.
[123]B.E. Chapman, D.L. Parker.3D multi-scale vessel enhancement filtering based on curvature measurements:application to time-of-flight MRA. Medical Image Analysis.2005;9:191-208.
[124]T. Lindeberg. On scale selection for differential operators. In:Proceedings of 8th Scandinavian Conference on Image Analysis. Tromso, Norway.1993;p857-866.
[125]D.L. Parker, Y.P. Du, and W.L. Davis. The voxel sensitivity function in Fourier transform imaging: applications to magnetic resonance angiography. Magn Reson Med.1995;33:156-62.
[126]F.W. Wehrli, A. Shimakawa, G.T. Gullberg, J.R. MacFall. Time-of-flight MR flow imaging: selective saturation recovery with gradient refocusing. Radiology.1986;160:781-5.
[127]D.L. Parker, C. Yuan, D.D. Blatter. MR angiography by multiple thin slab 3D acquisition. Magn Reson Med.1991; 17:434-51.
[128]M.A. Bernstein, J. Huston, C. Lin, G.F. Gibbs, J.P. Felmlee. High-resolution intracranial and cervical MRA at 3.0T:technical considerations and initial experience. Magn Reson Med. 2001:46:955-62.
[129]T.C. Lauenstein, K. Salman, R. Morreira, S. Tata, D. Tudorascu, G. Baramidze, S. Singh-Parker, D.R. Martin. Nephrogenic systemic fibrosis:center case review. J Magn Reson Imaging. 2007;26:1198-1203.
[130]D. Atkinson, M. Brant-Zawadzki, G Gillan, D. Purdy, G Laub. Improved MR angiography: magnetization transfer suppression with variable flip angle excitation and increased resolution. Radiology. 1994;190:890-4.
[131]Y.P. Du. Three-dimensional mapping of local field gradient in fMRI. In:Proceedings of the 11th Annual Meeting of ISMRM, Toronto, Canada.2003; p1714.
[132]M.A. Bernstein, S.B. Fain, S.J. Riederer. Effect of windowing and zero-filled reconstruction of MRI data on spatial resolution and acquisition strategy. J Magn Reson Imaging.2001;14:270-280.
[133]GA. Baxes. Digital image processing:principles and applications. New York:John Wiley and Sons Inc.1994. p129-135.
[134]S. Chavez, Q.S. Xiang, L. An. Understanding phase maps in MRI:a new cutline phase unwrapping method. IEEE Trans Med Imaging.2002;21:966-977.
[135]J. Frahm, K.D. Merboldt, W. Hanicke. Direct FLASH MR imaging of magnetic field inhomogeneities by gradient compensation. Magn Reson Med.1988;6:474-480.
[136]D.S Pandian, C. Ciulla, E.M Haacke, J. Jiang, M. Ayaz. Complex threshold method for identifying pixels that contain predominantly noise in magnetic resonance images. J Magn Reson Imaging. 2008;28:727-735.
[137]G Gilbert. Measurement of signal-to-noise ratios in sum-of-squares MR images. J Magn Reson Imaging.2007;26:1678;
[138]N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybernet. 1979;9:62-66.
[139]S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi. Optimization by simulated annealing. Science, 1983;220:671-680.
[140]H. Chen, J. Hale. An algorithm for angiography image enhancement. Magn Reson Med. 1995;33:534-540.
[141]E.G. Kholmovski, D.L. Parker. High resolution magnetic resonance venography at 3 Tesla: optimized acquisition, reconstruction, and post-processing. In:Proceedings of the 14th Annual Meeting of ISMRM, Seattle, Washington, USA.2006;p810.
[142]P.B. Roemer, W.A. Edelstein, C.E. Hayes, S.P. Souza, and O.M. Mueller. The NMR phased array. Magn Reson Med 1990;16:192-225.
[143]Y.P. Du, D.L. Parker, W.L. Davis, G. Cao. Reduction of partial-volume artifacts using zero-filled interpolation in three-dimensional MR angiography. J Magn Reson Imaging.1994;4:733-741.
[144]M.A. Griswold, P.M. Jakob, R.M. Heidemann, M. Nittka, V. Jellus, J. Wang, B. Kiefer, A. Haase. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med, 2002;47:1202-1210.
[145]C. Denk, BSc, A. Rauscher, Susceptibility weighted imaging with multiple echoes. J Magn Reson Imaging.2010;31:185-191.
[146]M.J. Lowe, J.A. Sorenson. Spatially filtering functional magnetic resonance imaging data. Magn Reson Med.1997;37:723-729.
[147]M.W. Woolrich, S. Jbabdi, B. Patenaude, M. Chappell, S. Makni, T. Behrens, C. Beckmann, M. Jenkinson, S.M. Smith. Bayesian analysis of neuroimaging data in FSL. NeuroImage.2009;45:S173-186.
[148]S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, L.M. De, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N.D. Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage.2004;23(S1):208-19.
[149]S.D. Wolff, J. Eng, R.S. Balaban. Magnetization transfer contrast:method for improving contrast in gradient-recalled-echo images. Radiology.1991;179:133-137.
[150]A.L. Alexander, B.E. Chapman, J.S. Tsuruda, D.L. Parker. A median filter for 3D FAST spin echo black blood images of cerebral vessels. Magn Reson Med.2000;43:310-313.
[151]K.P. Pruessmann, M. Weiger, M.B. Scheidegger, P. Boesiger. SENSE:sensitivity encoding for fast MRI. Magn Reson Med.1999;42:952-962.
[152]D.K. Sodickson, W.J. Manning. Simultaneous acquisition of spatial harmonics (SMASH):ultra-fast imaging with radiofrequency coil arrays. Magn Reson Med.1997;38:591-603.
[153]W.E. Kyriakos, L.P. Panych, D.F. Kacher, C.F. Westin, S.M. Bao, R.V. Mulkern and F.A. Jolesz. Sensitivity profiles from an array of coils for encoding and reconstruction in parallel (SPACE RIP). Magn Reson Med.2000;44:301-308.
[154]J. Tsao, P. Boesiger, and K.P. Pruessmann. k-t BLAST and k-t SENSE:dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn Reson Med.2003; 50:1031-1042.
[155]Y. Qian, Z. Zhang, V.A. Stenger, and Y. Wang. Self-Calibrated spiral SENSE. Magn Reson Med. 2003;52:688-692.
[156]N, Seiberlich, F. Breuer, M. Blaimer, P. Jakob, M. Griswold. Self-calibrating GRAPPA operator gridding for radial and spiral trajectories.2008;59:930-935.
[157]T. Kubo, M. Horii, Y. Harada, et al. Radial-sequence magnetic resonance imaging in evaluation of acetabular labrum. Journal of Orthopaedic Science.1999;4:328-332.
[158]E.J. Candes, J. Romberg, and T. Tao. Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory.2006;52:489-509.
[159]D.L. Donoho. Compressed sensing. IEEE Trans. Inform. Theory.2006:52:1289-1306.
[160]M. Lustig, D.L. Donoho, and J. M. Pauly. Sparse MRI:The application of compressed sensing for rapid MR imaging. Magn Reson Med.2007;58:1182-1195.
[161]U. Gamper, P. Boesiger, and S. Kozerke. Compressed sensing in dynamic MRI. Magn Reson Med. 2008; 59:365-373.