磁共振表观弥散系数图在脑梗死演变中的诊断价值

目的:探讨磁共振弥散加权成像(DWI)表观扩散系数图(ADC图)在脑梗死分期中的价值。方法:对75例脑梗死患者共作了81例次(超急性期15例次、急性期15例次、亚急性期31例次、稳定期12例次及慢性期8例次)DWI及常规MRI检查。测定各期梗死灶ADC值及对侧ADC值,计算相对ADC(rADC),分析判断rADC在各期脑梗死中的演变情况。结果:超急性期、急性期及亚急性期梗死灶ADC值为(4.96±0.98)×10-4mm2/s,低于对侧相应区域(8.18±1.07)×10-4mm2/s。(t=2.22,P<0.05);rADC以超急性期最低,随时间延长由低到高,于8~14天出现假性正常化,于慢性期高于正常水平,rADC与时间具有相关性(rs=1.00,P<0.05)。结论:各期脑梗死灶ADC值具有特征性演变规律,结合DWI、T2WI对早期脑梗死的检出和准确分期具重要价值。     

脑梗死rADC值变化与临床分期相关性的初步研究

目的:分析脑梗死各临床分期的相对表现扩散系数(rADC)值变化,探讨rADC值的大小与临床分期的相关性。方法:收集22例脑梗死患者,分别于超急性期(<12h)、急性期(13～72h)、亚急性期(4～14d)、慢性期(15d)行常规序列和磁共振扩散加权成像(DWI)检查。测量各期病灶的ADC值,并计算rADC值,统计不同分期rADC值有无差异。按rADC值大小对临床各期病例进行统计,分析rADC值的大小与临床分期的相关性。结果:脑梗死rADC值在超急性期下降,急性期进一步下降;于亚急性期、慢性期逐渐上升。脑梗死病灶各期的rADC值有明显差异;脑梗死不同分期与rADC值大小有相关性。结论:根据脑梗死rADC值大小可判断其临床发病时间。     

不同期相脑梗死在扩散加权成像中的信号特征及临床应用

目的:探讨在不同期相脑梗死中DWI(核磁共振扩散加权成像)及ADC图(表观弥散系数图像)的信号特点及其成像技术对脑梗死的应用价值。方法:对54例脑梗死患者,应用SE-T1WI、FSE-T2WI、T2-FLAIR、DWI序列进行横断位扫描,及ADC图像重建,并按发病时间分为超急性期、急性期、亚急性期和慢性期,观察并测量DWI图像及ADC图在不同期相脑梗死中的信号强度和ADC值的大小。结果:对于超急性期和急性期的脑梗死病灶,其DWI信号明显高于正常脑组织(P<0.05),其ADC值则低于正常脑组织(P<0.05),二图对其均有很高的显示度;对于亚急性期病灶,其DWI信号高于正常脑组织(P<0.05),其ADC值与正常脑组织信号差别不大(P>0.05),DWI可以显示病灶;对于慢性期病灶,DWI信号等或稍低于正常脑组织,对病灶显示意义不大(P>0.05),而ADC值高于正常脑组织(P<0.05),可以显示病灶区域。结论:DWI和ADC图对超急性期、急性期脑梗死都有很大的诊断意义,DWI对亚急性期病灶有一定显示度,ADC图对慢性期梗死灶也有一定诊断意义。     

DWI及FLAIR技术在急性脑梗塞中的临床应用探讨

目的　探讨弥散加权成像 (DWI)技术及FLAIR技术在急性脑梗塞中的临床应用价值。方法　收集 60例急性脑梗塞患者 ,其中超急性期 ( <6h) 7例 ,急性期 ( 6～ 2 4h) 2 4例 ,亚急性期 ( 2 4～ 72h) 2 9例。均行常规T1WI ,T2 WI及FLAIR ,DWI扫描 ,比较病灶显示范围、边界及对比度 ,并计算病灶的ADC与rADC值。结果　①DWI及FLAIR序列对病灶的显示范围、对比均优于常规T1WI、T2 WI ,而且DWI对病灶显示较FLAIR更清晰。②DWI的b值越高 ,弥散效果越好 ,显示病灶越清晰。同时 ,弥散全方向比单方向显示病灶更清晰。③所有病例病灶的ADC与rADC值均下降。结论　DWI技术对急性脑梗塞病变敏感性最高 ,结合FLAIR技术可准确、可靠地诊断急性脑梗塞     

低场强磁共振弥散成像在脑梗死诊断中的应用价值

目的 评价低场磁共振弥散序列对各期脑梗死的诊断作用。方法 应用磁共振T1W1，T2WI和水抑制反转恢复成像(T2 FLAIR)和DWI对70例各期脑梗死病人进行86人次MRI检查，并作ADC图，测定ADC值。结果在超急性脑梗死病例中，DWI和ADC图均表现缺血，但T2及T2FLAIR成像表现正常，病灶的ADC值随梗死时间延长，呈由低向高变化的趋势。结论低场磁共振DWI对急性期及超急性期脑缺血病变高度敏感，显示率及敏感性是100％．常规T2WI敏感性37％；ADC图可进一步量化缺血程度，二者结合应用对脑梗死的早期诊断和病灶的转归评估有重要意义。     

MR DWI及ADC值在急性脑梗死诊断中的应用价值

目的评价磁共振扩散加权成像（DWI）和表观扩散系数（ADC）在急性脑梗死诊断中的临床价值。方法选取临床上拟诊为脑梗死患者186例进行常规MR扫描及DWI检查,根据发病时间分成超急性期（〈6h）、急性期（6～72h）、亚急性期（3～7d）3个组。测定各组梗死灶ADC值及健侧ADC值,并计算相对ADC（rADC）。结果超急性期、急性期、亚急性期梗死灶ADC值均低于健侧相应区域（配对t检验,P〈0．01）;超急性期、急性期及亚急性期病例之间rADC有统计学差异（单因素方差分析,F=7．663,P=0．001）。结论DWI在脑梗死超急性期、急性期、亚急性期均具有很高的敏感性,各期梗死灶rADC值有时间相关性,对脑梗死的准确分期有重要价值。     

ADC值变化对溶栓治疗前后脑缺血性半暗带的评估

目的 通过分析溶栓治疗前后脑缺血区ADC值的变化,探讨其在脑缺血半暗带(IP)中的价值.方法 对38例超急性期及急性期脑梗死患者分别于就诊当日及经溶栓治疗20天后进行DWI、T2 WI、T2-FLAIR扫描,将就诊时DWI高信号大小定义为初始病灶(L0),治疗后病灶在T2WI或T2-FLAIR大小定义为最终梗死面积(P),前者大于后者的面积为组织存活区(ST),后者大于前者的面积为扩大区(LG).根据患者就诊时间不同分为4组,分别测量P、ST、LG及对侧正常区的ADC值,计算相对ADC值(rADC),进行统计学分析.结果 病变侧DWI高信号区ADC值均较对侧减低,病变从中心到周边ADC值由低到高不同程度增加.相同时间点P、ST及LG的ADC值由低到高不同程度增加.随着脑梗死时间延长组织存活区所占比例逐渐减小.结论 (1)缺血半暗带可能在脑梗死24 h内都存在.(2)计算rADC值可以辅助判断脑损伤程度及评估溶栓预后.     

DWI对脑出血的诊断及鉴别诊断

目的 探讨脑出血(ICH)不同时期磁共振扩散加权成像(DWI)信号特点及表观扩散系数(ADC)值变化规律.方法 回顾性分析56例经CT证实的ICH患者MR图像,结合出血时间进行分期,分析各期MR信号特点,并测量血肿内部及健侧对应部位ADC值,对各期ADC值做统计学分析.结果 超急性期、亚急性晚期、慢性期血肿DWI大部为高信号,其中超急性期血肿边缘见低信号环;急性期、亚急性早期DWI以低信号为主,边缘见高信号环;各期患侧ADC值与健侧比较均明显下降,其中急性期和亚急性晚期下降幅度最大;除慢性期外双侧ADC值比较均有显著性差异(P＜0.05).结论 脑出血各期DWI信号有一定特征性,血肿中心ADC值较健侧降低,DWI可以鉴别急性脑梗死和脑出血,为临床早期治疗提供帮助.     

DWI及ADC图对脑梗死病情进展的评估作用研究

目的:研究核磁共振弥散加权成像及表观弥散系数图像对脑梗死的病情进展和恢复状态的评估作用。方法:对一组超急性期(8例)和急性期(10例)脑梗死患者连续追踪行T_1-FLAIR、FSE-T_2WI、T_2-FLAIR、DWI序列扫描检查,及ADC图像重建,观察不同期相脑梗死灶的图像特征,计算其固定层面病灶区域残余弥散积(MSAFSS):面积×(3.0-1 000×ADC),并使其与相应的脑功能损害评分相对应,进行相关性分析。结果:①超急性期病灶,T_1-FLAIR、T_2WI均无明显信号改变,T_2-FLAIR偶可见等或稍高信号,DWI则可见明显的高信号,ADC图为低信号;急性期梗死灶,T_1-FLAIR基本都呈低信号改变,T_2WI表现为高信号,T_2-FLAIR为高信号,DWI仍为高信号,ADC图为低信号;亚急性期病灶,T_1-FLAIR信号较前都明显降低,T_2WI、T_2-FLAIR、DWI均为高信号表现,ADC图为稍低或等信号表现;慢性期病灶,T_1-FLAIR为低信号,T_2WI为高信号,T_2-FLAIR、DWI呈低或等信号,而ADC图为高信号。②对12例全程追踪的观察结果显示,病灶全过程的MSAFSS与评分分值之间无相关(P0.05);而亚急性早期前10例的MSAFSS与分值间具有正相关性(P0.05)。结论:各期脑梗死在DWI及ADC图上都有其不同的图像特征,并可据此大体判断脑梗死所处期相;通过计算MSAFSS可以对亚急性早期以前的脑梗死病情进展与功能恢复状态给予一定程度的评估。     

DWI及ADC图在脑梗死不同期相中的诊断价值

目的：研究磁共振弥散加权成像（diffusion weighted imaging,DWI）及表观弥散系数（apparent diffusion coefficient,ADC）图像对不同期相脑梗死的图像特征及诊断价值。方法：对54例脑梗死患者按发病就诊时间分为超急性期（8例）、急性期（10例）、亚急性期（22例）和慢性期（14例）4组,对每例进行MRI的T1-FLAIR、FSE-T2WI、T2-FLAIR、DWI序列扫描检查及ADC图像重建,分别测量病灶中心和病灶对称点正常脑组织的信号强度,计算出两者的信号差作为本序列对病灶显示的对比度,并对在各序列图像上的信号差进行对比,对急性期患者在各序列图像上同一层面所显示面积进行比较。结果：对超急性期、急性期脑梗死病灶,DWI序列的信号差（对比度）明显高于其它序列（P＆lt;0.05）;亚急性期的病灶,T2-FLAIR的信号差低于DWI的病灶（P＆lt;0.05）,T2WI、DWI中病灶信号差类同（P＆gt;0.05）,不存在序列间的相互差别;对于慢性期的病灶,T2WI序列的病灶信号差明显高于其它序列（P＆lt;0.05）;对于急性期的相同层面,T2WI、T2-FLAIR、DWI显示的病灶面积类同,不存在序列间的相互差别（P〉O．05）。结论：DWI能对超急性期、急性期、亚急性期的脑梗死病灶做出明确诊断,并对超急性期、急性期病灶的诊断敏感性明显高于其它序列。     

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.     

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.     

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.     

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.     

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。     

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.