MR扩散加权成像与表观扩散系数在前列腺肿瘤中的应用比较

目的探讨磁共振弥散加权成像(DWI)与表观扩散系数(ADC)值在前列腺癌诊断中的应用价值。方法对54例前列腺病病变患者采用磁共振弥散加权成像检查,比较DWI与ADC值诊断前列腺癌的敏感性、特异性、准确性。结果在b=50s/mm2、b=800s/mm2,前列腺癌组患者的DWI的信号强度值明显低于前列腺增生组,且差异具有统计学意义。在b=800s/mm2,前列腺癌组患者的ADC值明显低于前列腺增生组,且差异具有统计学意义。当b取800s/mm2时,以前列腺癌组和前列腺增生组患者的平均ADC值的95%可信区间,将ADC值的诊断阈值放在≤0.87×10-3mm2/s,其诊断敏感性为89.47%,特异性为85.71%,准确性为87.04%;而DWI(b=50s/mm2)的其诊断敏感性为63.16%,特异性为71.43%,准确性为68.52%,DWI(b=800s/mm2)的其诊断敏感性为73.68%,特异性为74.29%,准确性为74.07%。经统计学分析发现ADC值对前列腺癌的诊断敏感性、特异性、准确性明显高于DWI,且差异具有统计学意义。结论当b=800s/mm~2时,DWI和ADC图对前列腺癌均有较高的诊断价值,ADC对于前列腺增生、前列腺癌可提供定量诊断信息,其诊断敏感性、特异性、准确性高于DWI;当b=50s/mm2时,DWI对前列腺癌的诊断敏感性较差,所以ADC值是鉴别前列腺增生与前列腺癌的一个很有价值的参数。     

磁共振弥散加权成像和动态增强诊断前列腺疾病临床价值分析

目的：探讨磁共振弥散加权成像和动态增强扫描在前列腺疾病临床诊断中的应用价值。方法对64例患者的前列腺病灶行磁共振常规扫描、磁共振弥散加权成像和动态扫描,选取感兴趣区记录动态增强定量参数Ktrans、Vc及Kep的值以及DWI的信号强度和表面扩散系数ADC值,并对得到的数据进行方差分析。结果前列腺癌组、前列腺增生组及正常前列腺组患者在b＝50s/mm^2、b＝800s/mm^2信号强度值及ADC值比较有显著性差异（ F ＝52．34、14．35、198．64,P＜0．05）。前列腺癌组、前列腺增生组及正常前列腺组患者在Ktrans、Vc及Kep等DCE-MRI参数方面比较有显著性差异（ F＝15．30、21．06、37．95,P ＜0．05）。前列腺癌组、前列腺增生组及正常前列腺组等三组患者进行两两比较发现正常前列腺组与前列腺增生组在Ktrans、Vc及Kep等方面比较无明显差异,而其它各组之间两两比较有显著性差异（t ＝4．66、5．65、3．81、4．15、3．01、3．24,P ＜0．05）。结论磁共振弥散加权成像联合动态增强扫描应用提高了M RI诊断前列腺癌的诊断和分期准确率,有助于对前列腺病变的鉴别诊断。     

MR扩散加权成像对前列腺癌的诊断价值

目的 探讨磁共振扩散加权成像(DWI)在前列腺癌的诊断及鉴别诊断中的应用价值.资料与方法 40例前列腺疾病中17例前列腺癌及23例前列腺增生.所有病例行MR DWI扫描,b值为800 s/mm2.分析各病例的DWI和表观扩散系数(ADC)图表现,并分别测量癌区、前列腺增生组织以及膀胱内尿液的ADC值,统计分析组间是否存在差异.结果 17例前列腺癌中15例在DWI上呈明显高信号,ADC图呈低信号,能直观显示肿瘤的范围.前列腺癌组织的平均ADC值为(1.03±0.32)×10-3 mm2/s,前列腺增生组织的平均ADC值为(1.62±0.16)×10-3 mm2/s,两者之间有统计学意义(P=0.002);前列腺癌与前列腺增生的膀胱内尿液的平均ADC值分别为(3.24±0.30)×10-3 mm2/s、(3.25±0.29)×10-3 mm2/s,两者之间无统计学意义(P=0.834).结论 DWI可显示前列腺癌的位置和侵犯范围;根据DWI信号特点以及ADC值可以提高前列腺癌的诊断准确率,对前列腺癌与前列腺增生具有较高的鉴别诊断价值.     

MR弥散加权成像表观弥散系数在前列腺病变诊断中的临床价值

目的探讨MR弥散加权成像表观弥散系数在前列腺病变诊断中的应用价值。方法对50例前列腺病变患者采用磁共振常规扫描和磁共振弥散加权成像检查,选择感兴趣区域记录MR信号强度及表观弥散系数(ADC)值等数据,并对这些数据进行方差分析。结果本组50例患者经手术或穿刺活检诊断确诊前列腺癌26例,占52.00%(26/50);前列腺增生24例,占48.00%(24/50)。前列腺癌组、前列腺增生组及正常前列腺组在b=0s/mm~2、b=800s/mm~2信号强度值比较存在明显差异;另外,三组之间ADC值比较亦存在明显差异(F=51.02、14.92、176.54,P0.05)。进行两两比较发现正常前列腺组与前列腺增生组在b=0s/mm~2、b=800s/mm~2信号强度值及ADC值比较差异无统计学意义(P0.05);而其它各组之间进行两两比较则存在明显差异。结论 ADC值测量对于前列腺增生和前列腺癌可以提供定量诊断信息,在二者的鉴别诊断中具有重要作用。     

MR弥散加权成像在子宫内膜良恶性病变中的鉴别价值

目的 探讨MR弥散加权成像(DWI)及其表观弥散系数(ADC)在子宫内膜良恶性病变中的鉴别价值.方法 回顾性分析55例经病理证实的子宫内膜病变,其中良性组14例(6例内膜增生、8例内膜息肉),恶性组41例(39例内膜癌、2例癌肉瘤).所有病例行常规MRI平扫和增强检查,以及DWI(弥散敏感因子b值为0、1000s/mm2),分析病变的DWI信号特点和测定ADC值.结果 良性组DWI图12例表现为稍高信号、2例表现为等信号、平均ADC值为(1.34±0.19)×103mm2/s;恶性组DWI图34例表现为明显高信号,余下7例表现为稍高信号,平均ADC值为(0.84±0.14)×103 mm2/s;恶性组ADC值明显小于良性组(P=0.042),以1.07×103 mm2/s为临界值诊断子宫内膜良恶性病变的敏感性、特异性、准确性高达92.9％、97.6％、96.4％.结论 DWI及ADC值测定有助于子宫内膜良恶性病变的鉴别诊断.     

正常前列腺MR弥散成像的初步应用

目的　探讨MR弥散成像 (DWI)在正常前列腺中的特点及应用价值。方法　随机选择无泌尿系统疾病的健康志愿者 3 0名 (年龄 2 0～ 40岁 ) ,采用SiemensSymphony 1.5T磁共振成像系统 ,在平面回波成像 (EPI)基础上进行MR弥散成像 ,并计算前列腺中央带和外周带的平均表观弥散系数。结果　MR弥散成像能较清晰地显示正常前列腺的中央带和外周带 ,其平均表观弥散系数分别为 :(1.883± 0 .3 7)× 10 -3 mm2 /s ,(2 .2 5 4± 0 .77)× 10 -3 mm2 /s ;大b值、大b值差所测得的ADC值更精确 ;3种成像方式 (DWI、T2 WI、T2 WI FS)中对比噪声比 (CNR)无显著性差异 (Ρ >0 .0 5 )。结论　 1.5T磁共振成像系统可以进行正常前列腺的MR弥散成像 ,有望对前列腺疾病的机理研究和临床诊断提供新的帮助。     

磁共振弥散加权成像、T2加权成像及动态增强联合运用对前列腺癌的诊断价值

目的 探讨磁共振弥散加权成像(DWI)、T2加权像(T2WI)及动态增强(DCE)联合应用对前列腺癌的诊断价值.方法 100例前列腺疾病中前列腺癌49例和非癌病例51例(包括46例前列腺增生、3例外周带炎症及2例前列腺结核).所有病例在常规MR检查基础上加扫DCE及DWI序列,DWI的b值为800 s/mm2.比较T2WI、DCE、DWI及三者联合诊断前列腺癌的敏感性;统计分析前列腺组织与非癌组织的ADC值是否存在差异.结果 在DWI图像上,前列腺癌多呈明显高信号,6例局限于中央带前列腺癌得到正确诊断.前列腺癌组织的平均ADC值为(0.96±0.22)×10-3mm2/s,前列腺增生组织的平均ADC值为(1.56±0.23)×10-3mm2/s,两者之间有统计学差异(P＝0.001),但ADC值有小部分重叠.T2WI、DCE、DWI及三者联合诊断前列腺癌的敏感性分别为85.7%、87.8%、93.9%、100%.结论 T2WI、DCE及DWI三者联合应用可以弥补各自的缺点,提高前列腺癌诊断的敏感性.     

正常与退变腰椎间盘磁共振弥散加权成像的对比研究及其临床意义

目的：探讨磁共振弥散加权成像（DWI）及表观弥散系数测量值（ADC值）在腰椎间盘退变中的临床应用价值并对可能影响ADC值的相关因素进行分析。方法：对24例健康志愿者和16例腰腿痛患者进行常规T2WI及DWI扫描检查,进行相关研究。结果：正常腰椎间盘ADC值与年龄、性别、体重指数均无相关性。正常与退变椎间盘平均ADC值分别为（1.82±0.70）×10^-3mm^2/s和（1.22±0.27）×10^-3mm^2/s,正常与退变之间差异具有统计学意义。结论：弥散加权成像ADC测量值对退变椎间盘的诊断有一定的临床价值,利用磁共振弥散加权成像ADC值诊断退变椎间盘时可不考虑年龄、性别和体重指数的影响。     

3.0T磁共振扩散加权成像在前列腺癌诊断与鉴别诊断中的应用

 目的 探讨3.0T磁共振弥散加权成像(DWI)和表面弥散系数(ADC)在前列腺癌诊断及鉴别诊断中的应用价值。方法 回顾性分析65例经穿刺活检病理证实的前列腺疾病患者,其中前列腺癌组21例,前列腺炎组19例,良性前列腺增生(BPH)组25例,测量病变区及前列腺增生外周带的ADC值,并在癌与非癌组之间进行受试者操作特征曲线(ROC)分析。结果 前列腺癌组ADC值为(0.74±0.10)×10^-3 mm²/s,前列腺炎组为(0.98±0.07)×10^-3 mm²/s, BPH组中央带为(1.21±0.09)×10^-3 mm²/s,外周带为(1.38±0.14)×10^-3 mm²/s,组间ADC值两两比较,差异均有统计学意义(P<0.01)。根据ROC曲线,当ADC值为0.95×10^-3 mm²/s时,诊断的敏感性达92.8%,特异性达100%,ROC曲线下面积为0.995。结论 磁共振DWI和ADC值可用于前列腺癌的诊断和鉴别诊断,具有很高的临床应用价值。     

磁共振弥散加权成像及动态增强联合应用对前列腺癌的诊断价值分析

目的 探讨磁共振弥散加权成像联合动态增强扫描在前列腺良恶性病变中的应用价值。方法 对前列腺疾病患者采用磁共振常规扫描并对得到的数据进行方差分析。结果 前列腺癌组、前列腺增生组及正常前列腺组患者在b=50s/mm2、b=800s/mm2信号强度值及ADC值比较有显著性差异。前列腺癌组、前列腺增生组及正常前列腺组患者在Ktrans、Vc及Kep等DCE-MRI参数方面比较有显著性差异。前列腺癌组、前列腺增生组及正常前列腺组等三组患者进行两两比较发现正常前列腺组与前列腺增生组在Ktrans、Vc及Kep等方面比较无明显差异,而其它各组之间两两比较有显著性差异。结论 磁共振弥散加权成像联合动态增强扫描应用提高了MRI诊断前列腺癌的分期准确率。     

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.     

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.