磁共振扩散峰度成像在肝脏病变中的研究进展

磁共振扩散峰度成像（DKI）是扩散加权成像（DWI）、扩散张量成像（DTI）的延伸与补充,可进一步揭示生物体内水分子运动的非高斯扩散模型,更适合反映人体内微环境,较DWI、DTI技术可提供更准确、真实、丰富的组织微观结构信息。DKI技术问世以来,逐渐应用于各系统疾病的研究,尤其是在神经系统疾病中取得了显著成果,展现出良好的临床价值。DKI可反映微观信息,具有巨大的应用前景,在肝脏疾病方面的研究也越来越多,笔者就DKI在肝脏疾病中的应用予以综述。     

扩散峰度成像在正常成人视通路中的应用研究

目的 应用扩散峰度成像(DKI)定量测量正常成人视通路各区域的平均峰度(MK)值、部分各向异性(FA)值和平均扩散率(MD), 探讨DKI技术在正常成人视通路研究中的应用价值。 方法 按区组随机化选择30名正常成人志愿者, 按年龄分为3组:20~39岁组、40~59岁组和60岁以上组, 每组10名, 均行常规颅脑MR和DKI序列扫描, 并测量视通路不同区域的MK值、FA值和MD, 采用SPSS15.0统计学软件分析MK值、FA值和MD的正常范围, 及其随年龄递增的变化规律。 结果 视神经、外侧膝状体、视放射和视皮层区域MK值与年龄呈负相关; 外侧膝状体、视放射区域FA值与年龄呈负相关; 视神经、外侧膝状体区域MD与年龄呈正相关。 结论 DKI所得参数可以无创性地反映正常成人视通路随年龄增长的微观结构的变化。     

MR扩散峰度成像在腹部的研究现状

磁共振扩散峰度成像(DKI)是一种反映体内水分子非高斯分布扩散运动状态的MR成像新技术。近年来DKI已广泛应用于神经系统,并取得良好的效果。目前也逐步被应用于前列腺、肾脏、肝脏等体部脏器成像,其中在前列腺的应用最为广泛,通过应用DKI可提高对前列腺肿瘤诊断与分级的能力。就DKI技术在腹部的应用研究现状予以综述。     

高b值MR扩散加权成像在乳腺良恶性病变诊断中的应用价值

目的 评价高b值MR DWI及ADC值在乳腺良恶性病变诊断中的应用价值.方法 165例患者在行乳腺MR动态增强扫描前行不同b值(分别为500、1500 s/mm2)的DWI扫描,对171个怀疑或高度怀疑恶性病变者行回顾性分析.以正常乳腺组织为参考基准,选择增强图像中异常强化的高信号病变,同时在高b值(b= 1500 s/mm2)DWI中视觉判定是高信号的病变定义为恶性病变阳性结果,否则为良性病变阴性结果.对其中111个DWI视觉判定阳性结果的病变计算ADC值.依据全部病变穿刺活检病理诊断结果,应用Fisher精确检验和Wilcoxon秩和检验对比分析高b值DWI视觉评估中恶性和良性病变的阳性和阴性病灶数,以ADC值=1.13×10-3 mm2/s作为临界值,计算诊断的特异度和敏感度.结果 乳腺病变穿刺活检病理证实的171个乳腺病变中,91个恶性病变,80个良性病变.高b值DWI视觉评估,139个阳性结果中,恶性病变83个,良性病变56个;32个阴性结果中,良性病变24个,恶性病变8个(非肿块性导管原位癌),差异有统计学意义(P＜0.01).所有浸润性癌和肿块样导管原位癌(DCIS)在DWI视觉判定中为阳性,8例非肿块性DCIS判定为假阴性,总体的敏感度为91.2％ (83/91),特异性为30.0％ (24/80).110个肿块样病变和1个局灶性病变DWI视觉评估阳性结果的病变中,63个恶性病变平均ADC值为(0.73±0.24)×10-3 mm2/s,48个良性病变平均ADC值为(1.19±0.42)×10-3mm2/s,差异有统计学意义(Z=5.818,P＜0.01).以ADC值=1.13×10-3mm2/s作为临界值时,61个恶性病变为阳性结果,2个黏液癌为假阴性结果;27个良性病变为阴性结果,21个良性病变为假阳性,诊断敏感度是96.8％(61/63),特异度为56.2％ (27/48).结论 高b值DWI及ADC值对乳腺良恶性病变的鉴别诊断有一定的作用,但在诊断非肿块性乳腺病变时仍需慎重.     

扩散峰度成像直方图在脑胶质瘤分级中的应用

目的探讨MRI扩散峰度成像(diffusion kurtosis imaging,DKI)直方图分析在脑胶质瘤术前分级诊断中的价值。方法通过对经手术及病理证实的45例脑胶质瘤患者的DKI图像资料的回顾性分析。选取整个肿瘤的实质区作为感兴趣容积(volume of interest,VOI)进行直方图分析,获得表观扩散系数(apparent diffusion for gaussian distribution,Dapp)及表观扩散峰度(apparent kurtosis coefficient,Kapp)的直方图参数。使用独立样本t检验及Mann-Whitney U检验评估高级别和低级别神经胶质瘤各直方图参数间的差异。受试者工作特征曲线(receiver operating characteristic,ROC)用于分析各直方图参数对胶质瘤分级的诊断效能。应用组内相关系数(intra-class correlation coefficient,ICC)分析数据测量的可重复性。结果DKI直方图参数在高低级别胶质瘤中有显著性差异(P<0.05)。Dapp直方图参数中Dapp-10th的诊断效能最高,曲线下面积(area under the curve,AUC)、敏感性和特异性分别为0.856、74.1%、83.3%。Kapp直方图参数中Kapp-30th的诊断能力最高,AUC、敏感性和特异性分别为0.905、81.5%、94.4%。每个参数具有良好的观察者间信度,ICC范围为0.861~0.964。结论DKI直方图分析有助于提高低级别胶质瘤与高级别胶质瘤之间的鉴别诊断能力。     

DK I在D-半乳糖干预SD大鼠中的应用

随着人口老龄化形势不断严峻,脑老化及其相关退行性疾病的发生率也逐年升高。通过对脑老化的深入研究能让人们正确认识其带来的严重后果以及正确选择预防和治疗措施。脑老化是一个漫长而且极其复杂的过程,纵向研究中无论研究对象是人还是动物,往往都因历经时间过长而中断,而动物衰老模型就为这类研究提供了很好的研究对象。但是目前更多是将衰老模型应用于疾病或药物效应的研究中,对于常态脑老化的研究很少。脑组织在成熟、老化的不同阶段,大脑都会有结构和形态学的改变。磁共振成像(magnetic resonance ima-ging,MRI)技术作为活体组织内非侵袭性检查手段已成为评价脑老化过程中脑结构和功能改变的有效工具。磁共振扩散峰度成像(diffusion kurtosis imaging,DKI)是反映生物组织内水分子非高斯扩散分布的一项新技术,它的发展基于磁共振扩散张量成像(diffusion tensor imaging,DTI),但是较 DTI 能够获得额外的水分子扩散的峰度信息,能够更加真实、客观地反映组织的微观结构改变,解决多神经纤维交叉问题。     

MR扩散加权成像表观扩散系数在乳腺结节病变诊断中的应用价值

目的 评价ADC值及相对ADC值(rADC)在乳腺结节病灶检查中的应用价值.方法 对52例66个乳腺结节病灶行MR DWI扫描,b值分别为0、800、1000 s/mm2,测量乳腺结节及同侧、对侧乳头层面未受累乳腺实质组织ADC值,计算rADC1(病灶ADC值/同侧未受累乳腺实质ADC值)及rADC2(病灶ADC值/对侧未受累乳腺实质ADC值),对所获资料采用两独立样本t检验、x2检验进行统计学分析.所有病灶均经穿刺活检或手术病理证实.结果 52例患者中,浸润性导管癌18例,纤维腺瘤34例,DWI显示50例,共64个病灶.b=800s/mm2时,良、恶性结节平均ADC值、同侧rADC800-1及对侧rADC800-2分别为(1.54±0.28)×10-3、(1.01±0.09)×10-3 mm2/s和0.77±0.15、0.52±0.07,0.76±0.14、0.51±0.06;其差异均有统计学意义(t值分别为8.217、9.339、10.394,P值均＜0.01);以乳腺浸润性导管癌平均值95％参考值范围上限界值作为恶性病变上限阈值点,ADC值、rADC8o0-1及rADC800-2分别为1.05×10-3 mm2/s、0.55、0.53,诊断的敏感度分别为75.0％、65.0％、60.0％,特异度分别为100.0％、95.7％、97.8％,阳性预测值分别为100.0％、86.7％、92.3％,阴性预测值分别为90.2％、86.3％、84.9％,诊断符合率分别为92.4％、86.4％、86.4％.b=1000 s/mm2时,良、恶性结节平均ADC值、rADC1000-1及rADC1000-2分别为(1.45±0.28)×10-3、(0.93±0.08)×10-3mm2/s和0.75±0.16、0.53±0.09,0.74±0.15、0.52±0.07;其差异有统计学意义(t值分为11.844、5.820、8.082;P值均＜0.01);ADC值、rADC1ooo-1及rADC1000-2阈值点分别为0.97×10-3 mm2/s、0.58、0.55,诊断的敏感度均为70.0％,特异度分别为100.0％、95.7％、93.5％,阳性预测值分别为100.0％、87.5％、82.4％,阴性预测值分别为88.5％、88.0％、87.8％,诊断符合率分别为90.9％、87.9％、86.5％.以上6种诊断方法的敏感度及诊断符合率差异无统计学意义(x2值分别为1.232、2.263,P值分别为0.942、0.812).结论 ADC值与rADC值均为鉴别良恶性乳腺病变的重要参数,尤其是b= 800 s/mm2时的ADC值临床应用价值最高.     

Constrained maximum likelihood estimation of the diffusion kurtosis tensor using a Rician noise model

A computational framework to obtain an accurate quantification of the Gaussian and non‐Gaussian component of water molecules' diffusion through brain tissues with diffusion kurtosis imaging, is presented. The diffusion kurtosis imaging model quantifies the kurtosis, the degree of non‐Gaussianity, on a direction dependent basis, constituting a higher order diffusion kurtosis tensor, which is estimated in addition to the well‐known diffusion tensor. To reconcile with the physical phenomenon of molecular diffusion, both tensor estimates should lie within a physically acceptable range. Otherwise, clinically and artificially significant changes in diffusion (kurtosis) parameters might be confounded. To guarantee physical relevance, we here suggest to estimate both diffusional tensors by maximizing the joint likelihood function of all Rician distributed diffusion weighted images given the diffusion kurtosis imaging model while imposing a set of nonlinear constraints. As shown in this study, correctly accounting for the Rician noise structure is necessary to avoid significant overestimation of the kurtosis values. The performance of the constrained estimator was evaluated and compared to more commonly used strategies during simulations. Human brain data were used to emphasize the need for constrained estimators as not imposing the constraints give rise to constraint violations in about 70% of the brain voxels. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.     

DKI对动态增强时间-信号曲线呈平台型乳腺纤维腺瘤及乳腺癌的诊断价值

目的 探讨扩散峰度成像(DKI)对动态增强时间-信号曲线呈平台型的乳腺纤维腺瘤和乳腺癌的鉴别诊断价值.方法 选取DCE-TIC为平台型的20例(20个病灶)乳腺纤维腺瘤患者和61例(64个病灶)乳腺癌患者.测得病变的平均扩散峰度系数(MK)和平均扩散系数(MD)以及ADC值,观察病变形态和强化特征.以病理结果为金标准,...     

扩散张量成像在乳腺良恶性病变鉴别中的临床研究

乳腺癌是女性最常见的恶性肿瘤之一,其发病率逐年上升,平均年增长率为2%~3%。在我国大城市乳腺癌已占女性恶性肿瘤患病率第1位,严重影响到女性生活质量[1]。据临床资料显示,早期乳腺癌的5年生存率为90%以上。因此,乳腺癌的早期发现、早期诊断和早期治疗是降低其死亡率的关键。扩散张量成像(diffusion tensor imaging,DTI)作为新型的检查手段,无创伤、无辐射,不仅可以测定病变的扩散能力,同时还能测定各向异性,用于评估良恶性不失为一种快速易行可重复的方法,在诊断和鉴别诊断乳腺良恶性病变中具有潜在独特的应用价值。本研究回顾性分析乳腺良恶性病灶与患、健侧腺体表观扩散系数(apparent diffusion coefficient,ADC)和部分各向异性(fractional anistropy,FA)的差异,旨在探讨 DTI 鉴别乳腺良恶性病变的价值,对临床把握治疗时机及手术方式提供指导。     

Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas

Introduction

In this work, we aim to assess the significance of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameters in grading gliomas.

Methods

Retrospective studies were performed on 53 subjects with gliomas belonging to WHO grade II (n = 19), grade III (n = 20) and grade IV (n = 14). Expert marked regions of interest (ROIs) covering the tumour on T2-weighted images. Statistical texture measures such as entropy and busyness calculated over ROIs on diffusion parametric maps were used to assess the tumour heterogeneity. Additionally, we propose a volume heterogeneity index derived from cross correlation (CC) analysis as a tool for grading gliomas. The texture measures were compared between grades by performing the Mann-Whitney test followed by receiver operating characteristic (ROC) analysis for evaluating diagnostic accuracy.

Results

Entropy, busyness and volume heterogeneity index for all diffusion parameters except fractional anisotropy and anisotropy of kurtosis showed significant differences between grades. The Mann-Whitney test on mean diffusivity (MD), among DTI parameters, resulted in the highest discriminability with values of P = 0.029 (0.0421) for grade II vs. III and P = 0.0312 (0.0415) for III vs. IV for entropy (busyness). In DKI, mean kurtosis (MK) showed the highest discriminability, P = 0.018 (0.038) for grade II vs. III and P = 0.022 (0.04) for III vs. IV for entropy (busyness). Results of CC analysis illustrate the existence of homogeneity in volume (uniformity across slices) for lower grades, as compared to higher grades. Hypothesis testing performed on volume heterogeneity index showed P values of 0.0002 (0.0001) and 0.0003 (0.0003) between grades II vs. III and III vs. IV, respectively, for MD (MK).

Conclusion

In summary, the studies demonstrated great potential towards automating grading gliomas by employing tumour heterogeneity measures on DTI and DKI parameters.

     

磁共振扩散峰度成像预测食管癌放疗敏感性的实验研究

目的 从动物模型层面探讨扩散峰度成像（DKI）在预测食管癌放疗敏感性方面的应用价值。方法 建立人食管癌Eca-109裸鼠移植瘤模型,实验组给予单次剂量15 Gy（6 MV X射线）照射,对照组不接受任何处理。比较两组移植瘤体积及表观弥散系数（ADC）、平均扩散峰度（MK）、平均扩散系数（MD）的变化情况,观察实验组与对照组在相应时间点的细胞密度和坏死比例。结果 实验组裸鼠移植瘤在照射后明显出现了生长延迟现象,从照后第7天开始实验组移植瘤体积明显小于对照组（t=3.206~6.149,P<0.05）。照后第3天开始实验组ADC值及MD值明显高于对照组,MK值明显低于对照组（t_ADC=-11.018~-2.049,t_MD=-6.609~-2.052,t_MK=2.492~9.323,P<0.05）。照后第3天开始对照组细胞密度高于实验组,实验组的坏死比例高于对照组（t_密度=-8.387~-2.239,t_坏死比例=2.980~17.430,P<0.05）。结论 单次大剂量照射可以抑制荷瘤裸鼠肿瘤生长,ADC、MK、MD值均可以先于肿瘤形态学变化而发生改变,移植瘤细胞密度及坏死比例的变化与ADC、MK、MD值的变化基本吻合。DKI具有早期预测食管癌放疗敏感性的价值。     

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.