3.0T MR弥散梯度编码方向对脑组织FA值测量的影响

目的:探讨超高场强MR下弥散张量成像中弥散梯度编码方向对脑组织弥散各向异性分数(FA)的影响。方法:使用3种不同的弥散梯度编码方向(6、13和21个)在3.0T MRI上对14名健康志愿者进行头颅弥散张量成像(DTI)。在FA图上分别测量两侧半卵圆中心、胼胝体膝部、胼胝体压部、两侧内囊、丘脑及桥脑FA值,并进行统计学分析。结果:胼胝体压部FA值最高,其次为胼胝体膝部、内囊和桥脑,丘脑FA值最低。随着弥散梯度编码方向的增加,FA图质量提高,对白质纤维束细节的显示也更清楚,尤其是对脑干结构的分辨,但成像时间延长;3种不同弥散梯度编码方向的DTI扫描方案所观察到的半卵圆中心、胼胝体膝部、胼胝体压部、内囊、丘脑及桥脑的FA值不存在统计学显著性差异。结论:超高场MRI弥散梯度编码方向数目对脑组织FA值的测量无显著性影响,在临床运用中可根据患者状况选择弥散梯度编码方向,以提高DTI检查的成功率。     

磁共振弥散张量成像联合弥散张量纤维束成像在早期弥漫性轴索损伤中的应用

目的探讨磁共振弥散张量成像(DTI)与弥散张量纤维束示踪技术(DTT)对早期弥漫性轴索损伤(DAI)的诊断应用价值。方法对22例DAI早期(伤后10天内)患者(DAI组)及12例正常志愿者(对照组)分别行常规MR扫描及弥散张量成像扫描。在FA图上分别测量DAI组及对照组双侧半球白质感兴趣区(胼胝体压部、胼胝体膝部、内囊前肢、内囊后肢)部分各向异性(FA)值,将两组感兴趣区(ROI)平均FA值的差异进行比较,并对DAI组FA值与临床GCS评分进行相关性分析。运用弥散张量纤维束成像显示通过病灶的纤维束特征。结果病变组与对照组比较,DAI早期FA值(胼胝体压部0.647±0.069、胼胝体膝部0.615±0.043、内囊前肢0.541±0.065、内囊后肢0.639±0.035)较对照组(胼胝体压部0.748±0.045、胼胝体膝部0.729±0.058、内囊前肢0.622±0.038、内囊后肢0.667±0.027)FA值显著降低(P0.001)。胼胝体压部FA值的变化与GCS评分呈正相关(r=0.736,P=0.001)。DTT较好显示感兴趣区白质纤维束形态,更直观显示脑白质损伤程度。结论 DTI联合DTT是DAI患者检查的敏感方法,FA值评估是DAI程度的重要依据,DDT可观察神经纤维束受损范围。     

磁共振弥散张量成像对正常大脑白质纤维束构象的初步研究

目的利用磁共振弥散张量成像(DTI)研究正常成人脑内各部位各向异性程度及正常白质纤维束构象特征.方法对25名正常志愿者进行常规MR及DTI序列检查,重建FA图及三维彩色编码张量图.分别在半卵圆中心、基底节区和大脑脚层面测量主要白质束的FA值.结果DTI显示灰质与白质区各向异性存在显著差异,不同部位的白质纤维束各向异性程度亦不相同,且左右两侧基本对称,重建FA图和三维彩色编码张量图可显示白质内大部分主要的白质纤维束.结论DTI可清晰显示脑内白质纤维束的走行及分布,为了解脑功能与白质通路间关系提供了有力研究手段.     

梗阻性脑积水的弥散加权成像及弥散张量成像研究

目的探讨磁共振弥散加权成像(DWI)与弥散张量成像(DTI)对梗阻性脑积水的诊断价值。方法 25例梗阻性脑积水患者和30例志愿者均行3.0T磁共振常规序列以及DWI和DTI扫描,重建出ADC图、MD图、FA图、RA图、VR图及AI图,并对各参数图进行测量和记录,所得数据进行统计学分析。结果 30例志愿者及25例梗阻性脑积水患者分别测量双侧侧脑室周围脑白质、胼胝体膝部及压部区域的ADC、MD、FA、RA、VR及AI进行测量,ADC图中胼胝体膝部两组ADC值差异具有统计学意义(P<0.05),双侧侧脑室外侧脑白质两组所测得MD、FA、VR、AI值均有统计学意义(P<0.05),两组胼胝体压部RA值有统计学差异(P<0.05),两组间胼胝体膝部所有测得DTI各参数值均无统计学差异(P>0.05)。结论 DWI和DTI在脑积水的诊断中具有重要的诊断价值。     

不同数量梯度磁场方向对正常脑白质纤维束扩散张量成像的比较定量研究

目的比较扩散张量成像中施加不同数量的梯度磁场方向对扩散的各向异性（FA）和表观扩散系数（ADC）参数的影响. 资料与方法对24名正常人进行3.0 T磁共振扩散张量成像扫描,分别施加6个和25个方向的扩散敏感梯度磁场.分别测得2个梯度方向所获得FA图和ADC图的内囊前、后肢及胼胝体膝部、压部白质的FA与ADC值,进行比较. 结果在其他扫描参数不变的情况下（b=0.1000 s/mm^2）,6个方向与25个方向,相应部位的FA和ADC值的两组数据无显著差异. 结论磁共振扫描选择扩散张量成像扫描参数时,在不影响各定量值测量的情况下,应尽量减少扫描时间,6个方向是扩散张量成像的较好选择.     

磁共振弥散张量成像在脑高级别星形细胞瘤和转移瘤瘤周实质区的诊断价值

目的 探讨磁共振弥散张量成像(diffusion tensor imaging,DTI)在高级别星形细胞瘤和单发脑转移瘤诊断中的价值.方法 25例脑高级别星形细胞瘤和16例单发脑转移瘤,术前行DTI扫描,测定瘤周脑实质区及对侧正常脑实质的平均弥散系数(MD)值及各向异性分数(FA)值,并重建白质纤维示踪图,观察病灶与白质纤维束的关系.结果 高级别星形细胞瘤与脑转移瘤瘤周实质区的FA值分别为0.227±0.05、0.169±0.07,两者存在统计学差异(P＜0.05).DTI白质纤维示踪图可以较为准确地反映病灶与白质纤维束的关系.结论瘤周实质区FA值有助于高级别脑星形细胞瘤与转移瘤的鉴别.     

脑白质束扩散张量与阿尔茨海默病患者的神经心理学量表评分的多元线性回归分析

目的应用线性回归方法,通过对阿尔茨海默病(AD)的相关脑白质束扩散张量分析,找出与AD患者神经心理学评分的相关关系并建立回归方程。资料与方法对20例轻度AD患者、30例中重度AD患者以及25名年龄相匹配的正常老年自愿者进行扩散张量成像(DTI)扫描。DTI采用单次激励平面回波成像(EPI)序列,扩散敏感梯度施加在25个不同方向,生成平均扩散系数(MD)、部分各向异性(FA)参数图,结合彩色向量图及纤维追踪图分别测定各白质束的参数值,包括双侧扣带束、上纵束、下纵束、下额枕束、钩束、穹窿体、胼胝体膝部及压部。利用多元线性回归分析,建立各DTI参数与简易智能量表(MMSE)评分的回归模型。结果对照组与轻度AD组比较仅穹窿体和左侧扣带束的FA值存在明显差异;轻度AD组与中重度AD组比较穹窿体、双侧钩束、扣带束、下纵束、胼胝体膝部及压部及右侧下额枕束的FA值均存在统计学差异。对照组与轻度AD组的MD值在穹窿体区存在统计学差异,而轻度AD组与中重度AD组在穹窿体、双侧钩束、下额枕束、左侧扣带束、右侧上、下纵束、胼胝体膝部及压部均存在MD值的显著差异。回归模型提示穹窿体和左侧扣带束的FA值与MMSE评分存在正线性相关关系,...     

DTI对急性脑梗死白质纤维各向异性特征的初步研究

目的:利用磁共振扩散张量成像(DTI)研究健康成人和急性缺血性脑梗死患者大脑白质纤维束各向异性特征。方法:对16例健康志愿者和17例急性脑梗死患者进行T1WI、T2WI、DWI及DTI检查;对DTI数据离线后处理,采用Volume-one 1.64和dTVII-R1软件处理,获得部分各向异性(FA)图及方向编码彩色(DEC)图。对健康志愿者分别在内囊、胼胝体以及半卵圆中心选择兴趣区测量主要白质纤维束的FA值。测量脑梗死患者的梗死区及健侧对应白质区的FA值。结果:FA图和DEC图可显示脑内主要的白质纤维束。正常组不同部位脑白质的FA值不同;大脑半球两侧白质的FA值差异无统计学意义。超急性期脑梗死区白质FA值与对侧相比,可轻度升高或降低;急性期梗死区白质FA值显著低于健侧,差异有统计学意义(t=10.987,P<0.01);急性期梗死区白质FA值下降率与发病时间存在相关关系(相关系数r=0.841,P<0.05)。结论:DTI可显示脑内白质纤维束的走行及分布。FA图及DEC图可以显示梗死区白质纤维束的方向与各向异性的改变程度。急性期脑梗死区FA值下降率随病程延长而增大,该指标可用于反映脑梗死的病程。     

血管性轻度认知障碍患者胼胝体扩散张量成像改变与认知功能的相关性研究

目的 观察胼胝体纤维束随年龄增长的变化和血管性轻度认知障碍(vMCI)患者胼胝体异常与认知功能改变的关系.方法 通过对vMCI患者(32例)、正常年老组(25例,年龄≥60岁)和年轻组(30例,年龄20~39岁)胼胝体的扩散张量成像(DTI),获得胼胝体的平均扩散(ADC)图及部分各向异性(FA)图,分别测量压部和膝部的ADC值及FA值,并对结果进行统计分析.结果 vMCI组在胼胝体膝部、压部的ADC值较年老组高,而FA值较年老组低.年老组胼胝体膝部、压部FA值均低于年轻组相应部位的FA值(P<0.05),而胼胝体膝部、压部ADC值均大于年轻组(P<0.05).vMCI组胼胝体压部的FA值与MMSE评分呈正相关(r=0.796,P<0.05),而ADC值与MMSE评分呈负相关(r=-0.803,P<0.05).结论 vMCI病人胼胝体完整性受到破坏,故胼胝体DTI的各参数变化有助于其的早期诊断.     

采用变形矫正技术改善DTI图像质量的初步探讨

目的 探讨扩散张量成像(DTI)的变形矫正技术对各向异性分数(FA)图、纤维束追踪图的影响.资料与方法选择20名正常成人行DTI,将图像导入工作站,采用FSL软件进行变形矫正,生成FA图和纤维柬追踪图,并对FA图进行主观评分和信噪比(SNR)、对比噪声比(CN8)、FA值的测量.结果 所有被试者矫正后的FA图主观评测质量、SNR和CNR均显著提高.胼胝体膝部、压部和内囊后肢的FA值无显著改变,背侧丘脑FA值降低.矫正后图像的纤维束更加规则平滑,错误追踪纤维减少.结论 采用FSL软件可以简便、快捷地矫正图像变形,提高DTI图像质量和数据准确性.     

Biexponential diffusion tensor analysis of human brain diffusion data.

Several studies have shown that in tissues over an extended range of b-factors, the signal decay deviates significantly from the basic monoexponential model. The true nature of this departure has to date not been identified. For the current study, line scan diffusion images of brain suitable for biexponential diffusion tensor analysis were acquired in normal subjects on a clinical MR system. For each of six noncollinear directions, 32 images with b-factors ranging from 5 to 5000 s/mm2 were collected. Biexponential fits yielded parameter maps for a fast and a slow diffusion component. A subset of the diffusion data, consisting of the images obtained at the conventional range of b-factors between 5 and 972 s/mm2, was used for monoexponential diffusion tensor analysis. Fractional anisotropy (FA) of the fast-diffusion component and the monoexponential fit exhibited no significant difference. FA of the slow-diffusion biexponential component was significantly higher, particularly in areas of lower fiber density. The principal diffusion directions for the two biexponential components and the monoexponential solution were largely the same and in agreement with known fiber tracts. The second and third diffusion eigenvector directions also appeared to be aligned, but they exhibited significant deviations in localized areas.     

Comparison of the diagnostic performances of diffusion parameters in diffusion weighted imaging and diffusion tensor imaging of breast lesions

Purpose

To evaluate the diagnostic efficiency of the diffusion parameters measured by conventional diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) for discrimination of malignant breast lesions from benign lesions and the normal breast.

Materials and methods

The study included 52 women with 55 breast lesions (30 malignant, 25 benign). DTI and DWI were performed complementary to dynamic contrast MRI at 3T. Apparent diffusion coefficient (ADC) of DWI, mean diffusivity (MD) and fractional anisotropy (FA) values of DTI were measured for lesions and contralateral breast parenchyma in each patient. We used b factors of 0, 50, 850, 1000 and 1500 s/mm² for DWI and b 0 and 1000 s/mm² for DTI. ADC, MD and FA values were compared between malignant and benign lesions, and the normal parenchyma by univariate and multivariate analyses.

Results

Diffusion parameters showed no difference according to menopausal status in the normal breast. ADC and MD values of the malignant lesions were significantly lower than benign lesions and normal parenchyma (p = 0.001). The FA showed no statistical significance. With the cut-off values of ≤1.23 × 10⁻³ mm²/s (b 0–1000 s/mm²) and ≤1.12 × 10⁻³ mm²/s (b 0–1500 s/mm²), ADC showed 92.85% and 96.15% sensitivity; 72.22% and 73.52% PPV, respectively. With a cut-off value of ≤1.27 × 10⁻³ mm²/s (b 1000 s/mm²), MD was 100% sensitive with a PPV of 65.90%. Comparing the diagnostic performance of the parameters in DTI with DWI, we obtained similar efficiency of ADC with b values of 0,1000 and 0,1500 s/mm² and MD with a b value of 0, 1000 s/mm² (AUC = 0.82 ± 0.07).

Conclusion

ADC of DWI and MD of DTI values provide significant discriminative factors for benign and malignant breast lesions. FA measurement was not discriminative. Supported with clinical and dynamic contrast MRI findings, DWI and DTI findings provide significant contribution to the final radiologic decision.     

Sensitivity of diffusion weighted steady state free precession to anisotropic diffusion

Diffusion‐weighted steady‐state free precession (DW‐SSFP) accumulates signal from multiple echoes over several TRs yielding a strong sensitivity to diffusion with short gradient durations and imaging times. Although the DW‐SSFP signal is well characterized for isotropic, Gaussian diffusion, it is unclear how the DW‐SSFP signal propagates in inhomogeneous media such as brain tissue. This article presents a more general analytical expression for the DW‐SSFP signal which accommodates Gaussian and non‐Gaussian spin displacement probability density functions. This new framework for calculating the DW‐SSFP signal is used to investigate signal behavior for a single fiber, crossing fibers, and reflective barriers. DW‐SSFP measurements in the corpus callosum of a fixed brain are shown to be in good agreement with theoretical predictions. Further measurements in fixed brain tissue also demonstrate that 3D DW‐SSFP out‐performs 3D diffusion weighted spin echo in both SNR and CNR efficiency providing a compelling example of its potential to be used for high resolution diffusion tensor imaging. Magn Reson Med 60:405–413, 2008. © 2008 Wiley‐Liss, Inc.     

Characterizing non-Gaussian diffusion by using generalized diffusion tensors.

Diffusion tensor imaging (DTI) is known to have a limited capability of resolving multiple fiber orientations within one voxel. This is mainly because the probability density function (PDF) for random spin displacement is non-Gaussian in the confining environment of biological tissues and, thus, the modeling of self-diffusion by a second-order tensor breaks down. The statistical property of a non-Gaussian diffusion process is characterized via the higher-order tensor (HOT) coefficients by reconstructing the PDF of the random spin displacement. Those HOT coefficients can be determined by combining a series of complex diffusion-weighted measurements. The signal equation for an MR diffusion experiment was investigated theoretically by generalizing Fick's law to a higher-order partial differential equation (PDE) obtained via Kramers-Moyal expansion. A relationship has been derived between the HOT coefficients of the PDE and the higher-order cumulants of the random spin displacement. Monte-Carlo simulations of diffusion in a restricted environment with different geometrical shapes were performed, and the strengths and weaknesses of both HOT and established diffusion analysis techniques were investigated. The generalized diffusion tensor formalism is capable of accurately resolving the underlying spin displacement for complex geometrical structures, of which neither conventional DTI nor diffusion-weighted imaging at high angular resolution (HARD) is capable. The HOT method helps illuminate some of the restrictions that are characteristic of these other methods. Furthermore, a direct relationship between HOT and q-space is also established.     

Limitations of apparent diffusion coefficient-based models in characterizing non-gaussian diffusion.

Diffusion in complex heterogeneous structures, for example, the neural fiber system, is non-gaussian. Recently, several methods have been introduced to address the issue of non-gaussian diffusion in multifiber systems. Some are based on apparent diffusion coefficient (ADC) analysis; and some are based on q-space analysis. Here, using a simple mathematic derivation, ADC-based models are shown to be mathematically self-inconsistent in the presence of non-gaussian diffusion. Monte Carlo simulation on restricted diffusion is applied to demonstrate the poor data fitting that can result from ADC-based models. Specific comparisons are performed between two generalized diffusion tensor imaging methods: one of them is based on ADC analysis, and the other is shown to be consistent with q-space formalism. The issue of imaging asymmetric microstructures is also investigated. Signal phase and spin exchange are necessary to resolve multiple orientations of an asymmetric structure.     

More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging

With diffusion tensor imaging, the diffusion of water molecules through brain structures is quantified by parameters, which are estimated assuming monoexponential diffusion‐weighted signal attenuation. The estimated diffusion parameters, however, depend on the diffusion weighting strength, the b‐value, which hampers the interpretation and comparison of various diffusion tensor imaging studies. In this study, a likelihood ratio test is used to show that the diffusion kurtosis imaging model provides a more accurate parameterization of both the Gaussian and non‐Gaussian diffusion component compared with diffusion tensor imaging. As a result, the diffusion kurtosis imaging model provides a b‐value‐independent estimation of the widely used diffusion tensor parameters as demonstrated with diffusion‐weighted rat data, which was acquired with eight different b‐values, uniformly distributed in a range of [0,2800 sec/mm²]. In addition, the diffusion parameter values are significantly increased in comparison to the values estimated with the diffusion tensor imaging model in all major rat brain structures. As incorrectly assuming additive Gaussian noise on the diffusion‐weighted data will result in an overestimated degree of non‐Gaussian diffusion and a b‐value‐dependent underestimation of diffusivity measures, a Rician noise model was used in this study. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.     

A method for calibrating diffusion gradients in diffusion tensor imaging

OBJECTIVE: To calibrate and correct the gradient errors including gradient amplitude scaling errors, background/imaging gradients, and residual gradients in diffusion tensor imaging (DTI). METHODS: A calibration protocol using an isotropic phantom was proposed. Gradient errors were estimated by using linear regression analyses on quadratic functions of diffusion gradients along 3 orthogonal directions. A 6-element total effective scaling vector is generated from the calibration protocol to retrospectively correct gradient errors in DTI experiments. RESULTS: The accuracy of the calibration protocol was within 1% or less in estimating gradient scaling errors. On both the brain study and the computer simulations, the retrospective correction minimized undesirable estimate biases of DTI measurements due to gradient errors. CONCLUSION: The protocol and retrospective correction are shown to be effective. The method may be used for prospective correction if actual diffusion-gradient waveforms are available. The methodology is expandable to general diffusion imaging schemes.