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多发性硬化磁共振影像组学定量研究进展
曹际斌 崔玲玲 孙文阁 范国光

Cite this article as: Cao JB, Cui LL, Sun WG, et al. Research progress of quantitative MRI radiomics in multiple sclerosis[J]. Chin J Magn Reson Imaging, 2021, 12(2): 113-116, 120.本文引用格式:曹际斌, 崔玲玲, 孙文阁, 等. 多发性硬化磁共振影像组学定量研究进展[J]. 磁共振成像, 2021, 12(2): 113-116, 120. DOI:10.12015/issn.1674-8034.2021.02.028.


[摘要] 多发性硬化(multiple sclerosis,MS)的发病率在全球范围内逐渐呈上升趋势,目前认为MS是复杂的基因-环境因素所致,但具体发病机制仍然不详。早发现早治疗是延缓或降低MS致残率的重要手段。随着技术的进步和新序列的出现,MRI在多发性硬化的诊断及监测进展的临床价值等方面显得更为重要。先进的MRI技术有助于进一步探索MS的发病机制,目前新的观点认为MS是一种颅脑异构性过程,其特征是中枢神经系统广泛受损,而不仅仅是脑白质多发局灶性脱髓鞘。磁共振定量技术可以为广泛性疾病受损的假设提供可靠的支持,其相关定量指标被认为是轴突功能障碍的特异性标志。笔者就近些年来颅脑磁共振定量技术在MS的应用研究进展进行阐述。
[Abstract] The incidence of multiple sclerosis (MS) is on the rise globally. MS is believed to be caused by complex gene-environmental factors, but the specific pathogenesis is still unknown. Early detection and treatment is an important means to delay or reduce MS disability rate. With the development of technology and the emergence of new sequences, MRI becomes more important in the diagnosis of multiple sclerosis and the clinical value of monitoring the progress. Advanced MRI techniques are helpful to further explore the pathogenesis of MS. Currently, MS is considered as a brain isomerism process characterized by extensive damage to the central nervous system and not just multiple focal demyelination of the white matter. Quantitative magnetic resonance imaging (qMRI), which is considered a specific marker of axonal dysfunction, can reliably support the hypothesis of generalized disease impairment. In this paper, the application of craniocerebral qMRI in MS in recent years is reviewed.
[关键词] 多发性硬化;磁共振成像;定量;影像组学
[Keywords] multiple sclerosis;magnetic resonance imaging;quantitative;radiomics

曹际斌    崔玲玲    孙文阁    范国光 *  

中国医科大学附属第一医院放射科,沈阳 110001

范国光,E-mail:fanguog@sina.com

作者利益冲突声明:全体作者均声明无利益冲突。


收稿日期:2020-06-24
接受日期:2020-08-21
DOI: 10.12015/issn.1674-8034.2021.02.028
本文引用格式:曹际斌, 崔玲玲, 孙文阁, 等. 多发性硬化磁共振影像组学定量研究进展[J]. 磁共振成像, 2021, 12(2): 113-116, 120. DOI:10.12015/issn.1674-8034.2021.02.028.

       多发性硬化(multiple sclerosis,MS)是影响年轻人最常见的非创伤性致残疾病。尽管其发病机制仍然未明确,但无论在发达国家还是发展中国家,其发病率和流行率仍然在增加[1]。许多诱因增加了MS的易感性,比如维生素D缺乏、紫外线B照射、EB病毒感染、肥胖和吸烟[2]。MS的早期诊断和治疗是预防残疾的关键。近些年,MRI在MS的诊断[3]、预后[4]和治疗反应评估[5]等方面发挥着关键作用。随着MRI新技术的出现和图像后处理方法的进步,特别是多种磁共振定量技术的应用,使其在探究MS的发病机制做出了更多的贡献,对提高MS诊断的准确性和与其他中枢神经系统(central nervous system,CNS)炎症疾病的鉴别诊断提供更多的帮助[6]

       最初MS被认为是脑白质局灶性脱髓鞘,在2014版的中国专家共识中被定义为“以CNS白质炎症性脱髓鞘病变为主要特点的免疫介导性疾病”[7]。而2018版被定义为:一种以CNS神经炎性脱髓鞘病变为主要特点的免疫介导性疾病,病变主要累及白质[8]。因此MS的受累部位不仅局限于白质,而是以白质受累为主的炎症反应造成的局灶性和广泛性的损害。这些病理改变是由MS过程中不同程度的炎症(如细胞浸润和小胶质细胞活化)、脱髓鞘和神经变性(轴突和神经元丢失)的动态级联反应引起的[9]

       MS的病程常有两种不同的临床表现:一种是复发和缓解的交替,另一种是不良临床表现的进展和不可逆转的恶化。这些过程在个体患者中以不同的方式表达,导致了复发缓解型MS (relapsing-remitting MS,RRMS)、继发性进展型MS(secondary progressive MS,SPMS)、原发进展型MS (primary progressive MS,PPMS)等类型[10]。现在MRI主要用于评估CNS白质的病灶,其特点是以散在分布的多病灶(空间多发性)与病程中呈现的缓解复发(时间多发性)并存[3]。有大量研究表明磁共振定量技术在MS患者颅脑的活动性病灶、无活动病灶、以及看似正常脑白质(normal appearing white matter,NAWM)有较大的临床应用价值。本文将对颅脑磁共振定量技术在MS的应用及研究进展进行阐述。

1 常规MRI序列的现状

       在过去的30年里,MRI方法的技术革新带来了巨大的收益,同时也给医学领域提出了新的挑战和问题。尽管常规MRI对MS诊断敏感性很高,但缺乏特异性,且在临床鉴别诊断和预测残疾进展等方面受到限制。越来越多的研究发现常规MRI不能检测到NAWM的损伤[11],而NAWM的异常变化在MS的早期已出现,并且与残疾、认知障碍和脑萎缩程度等相关[12]

       用于诊断大脑和脊髓MS病变的常规MRI序列是T2序列,包括T2WI和T2 FLAIR序列。在最新的MS标准中列出了相应常规MRI序列的应用[8]。典型的T2病灶呈椭圆形或卵圆形,直径大于5 mm,而FLAIR对脑室周围和皮质/皮质旁损伤的检测更为敏感,MRI的场强越高,图像的信噪比越高,检测病变的灵敏度也就越高[13]。MS的T2高信号斑块在空间模式有一定的特征性,例如“道森手指”征,毗邻并垂直于大脑侧脑室的长轴,具有放射方向的指状形态,在矢状位上更具特征性。2016年,Filippi等[14]发表研究论文建议将视神经脊髓炎谱系疾病(neuromyelitis optica spectrum disorders,NMOSD)作为另一种类型的疾病,以提高MS诊断敏感性和特异性的另一个区域,并被McDonald MS诊断标准2017采用[3]

       MS炎症的特点是免疫细胞浸润,尤其是脱髓鞘病变周围的淋巴细胞和浆细胞。评估炎症活动最常用的方法是钆类对比增强T1WI,白质出现强化病灶表明中枢神经系统血脑屏障的破坏。然而,钆增强只反映了白质的局灶性和短暂性炎症反应。现在认识到这些免疫细胞浸润不仅存在于活动性病灶、无活动病灶中,也存在于软脑膜以及NAWM中,尽管其浸润的程度较轻,而且不易在MR上发现[15]。有些研究使用了在钆注射后延迟至少10 min进行3D T2 FLAIR 扫描,可以提高强化病灶的检出率,配合减影成像技术,会更好地查找病灶[16, 17]。T2 FLAIR增强扫描可避免脑膜血管影响,对脑膜的病灶较CE-T1WI更敏感(10倍)[18]。有研究表明3D双反转恢复(3D-double inversion recovery,3D-DIR)[17,19]、相位差增强成像(phase difference enhanced imaging,PADRE)[20]检测近皮质的病变、灰白质共同受累病灶的敏感度明显优于T2 FLAIR。在各个序列的优势中互补,才能达到常规MRI序列的最佳应用。但是常规序列可以观察MS病灶的形态、有无新发病灶等,无法预测其进展及预后评估。

2 定量MRI在MS的应用

2.1 磁共振波谱

       磁共振波谱(MR spectroscopy,MRS)是利用磁共振化学位移现象来测定组成物质的分子成分的一种检测方法,是目前唯一可测得活体组织代谢物的化学成分和含量的检查方法。当前常用的是氢质子波谱技术(1H-MRS)。由于1H在不同化合物中的频率存在差异,通过在MRS的谱线中共振峰的位置判断化合物的性质,而共振峰的峰高和面积定量分析化合物的浓度。一些重要的分子已经可以用1H-MRS进行定量测量,包括N-乙酰天冬氨酸(N-acetylaspartate,NAA)、肌酸(creatine,Cr)、胆碱、乳酸、脂质、肌醇、γ-氨基丁酸和谷氨酸/谷氨酰胺。目前MRS在MS的临床应用主要是作为与其他脱髓鞘病变和肿瘤鉴别的一种影像学检查手段。MRS通过所选感兴趣的选取而获得定量数据,这些数据代表了MS潜在的病理学特征。MRS评估MS的两个主要病理方面:活动性炎症性脱髓鞘和轴突/神经元损伤[21]

       NAA为正常神经元的标志物,存在于神经元包体及轴索中,是神经元/少突胶质细胞轴突和偶联的标记[22]。因此,NAA的减少通常被认为神经元、轴突完整性受到破坏,因此与MS斑块轴突损失相关[23]。另外有研究一致表明,临床孤立综合征(clinically isolated syndromes,CIS)的灰质、PPMS、RRMS中均出现NAA降低,这说明了MS为持续性的神经退行性变[24]。还有研究表明,NAA/Cr比值可以作为MS活动性的指标,在活动期NAA/Cr比值明显下降,缓解期逐渐恢复[25]

       MRS还显示MS中广泛存在谷氨酸异常,即与该神经递质相关的细胞和代谢功能障碍。在T2高信号病灶中的谷氨酸浓度升高[26],谷氨酸浓度升高的程度可以预测NAA的下降、脑萎缩、身体残疾和认知障碍的程度[27]

2.2 灌注

       早期使用单光子发射计算机断层扫描或正电子发射断层扫描的研究表明,与健康对照组相比,MS患者组NAWM的脑血流(cerebral blood flow,CBF)明显减少[28]。随着脑灌注技术的发展,动态磁化率对比磁共振成像(dynamic susceptibility contrast MRI,DSC-MRI)[29, 30, 31]和动脉自旋标记(arterial spin labeling,ASL)磁共振成像[32, 33, 34, 35, 36]已广泛应用于神经系统。

2.2.1 DSC-MRI

       DSC-MRI利用顺磁性对比剂(通常为钆对比剂)作为示踪剂,能准确地测量脑组织的灌注值,其常用的参数有局部脑血流量(regional cerebral blood flow,rCBF)、局部脑血容量(regional cerebral blood volume,rCBV)、平均通过时间(mean transit time,MTT)等[29]。研究表明非活动期MS脑组织为低灌注状态[31,37],活动期的白质特征是rCBV、rCBF值的同时升高[38],MS患者深部灰质组织灌注减少与疲劳程度相关[31]。DSC-MRI可用于检测MS常规序列不易发现的炎症活动,血管损害相关的血流动力学异常等。

2.2.2 ASL

       ASL无需引入外源性对比剂,利用动脉血中的水作为内源性示踪剂,其测量的主要参数是CBF[33]。MS患者T2高信号病灶体积与局部脑血流呈负相关,ASL测定CBF可作为监测MS疾病活动性的客观指标[34]。Ma等[35]实验结果表明,SPMS、RRMS患者的NAWN灌注降低、代谢减少,而且灌注水平且与患者性别、年龄显著相关。MS患者的早期灌注改变可以用ASL序列来评估,即使对没有或仅有微小结构改变的患者,额叶皮层和丘脑的灌注数据可以为评估这些患者的认知功能障碍提供相关信息[36]

2.3 扩散成像

2.3.1 扩散加权成像

       尽管扩散加权成像(diffusion weighted imaging,DWI)由Stejskal和Tanner于1965年提出,但直到20世纪90年代才进入临床应用。DWI主要依赖于水分子的运动而非组织的自旋质子密度、T1或T2值,可以体现组织中水分子的微观布朗运动,其信号与水分子在体素中的表观扩散系数成反比,反映了水分子沿扩散梯度方向在不同组织腔中的平均扩散。其参数为ADC、扩散系数(Diffusion,D)。

       DWI表现与病程的进展密切相关,早期由于急性乏氧引起脱髓鞘炎性细胞和巨噬细胞浸润,水分子扩散受限,DWI表现为高信号,ADC呈低信号,随着病程的进展,髓鞘崩解,细胞外水分增加,ADC亦显著增加,DWT表现为等或低信号,因而DWI能够对病变的性质进行鉴别(活动期或静止期)[39]。NAWM的ADC或D值高于对照组相应白质的ADC或D值,但低于T2可见病变的ADC或D值[40]

2.3.2 扩散张量成像

       扩散张量成像(diffusion tensor imaging,DTI)的特征是3个主要扩散系数,它们与3个主要扩散方向相关。最常用的计算扩散系数是平均扩散系数(mean diffusivity,MD)、分数各向异性(fractional anisotropy,FA)、轴向扩散系数(axial diffusivity,AD)和径向扩散系数(radial diffusivity,RD)。MD反映水分子的平均扩散率,与髓鞘损伤、轴突丢失等有关;FA是水分子各向异性成分占整个扩散张量的比例,与白质结构的完整性、纤维的致密性及平行性呈正相关;RD为垂直轴突方向扩散率,反映髓鞘的损伤;AD为平行轴突方向扩散率,反映轴突的丢失。这些参数反映了每个体素在微观结构水平上对病理变化非常敏感,主要针对白质纤维束的完整性。

       Roosendaal等[41]指出MS患者在双侧视辐射、辐射冠、穹窿、胼胝体区域表现为FA值的降低、RD值的升高。Andersen等[42]研究SPMS胼胝体的RD增加和FA减少,但AD没有改变,若出现RD增高,是疾病进展的一个显著特征;RRMS、PPMS没有任何参数显示出差异。Kolasa等[43]对46例MS患者4年的DTI动态监测提示复发性MS患者DTI指标与健康对照组之间存在显著差异,然而其扩散异常的位置随时间变化而不同,胼胝体的扩散指标(高MD和AD)可能与复发性MS的残疾累积有关。

       DTI模型过度简化了水在复杂介质中的扩散行为,也会受到白质的自然空间变化的影响,其指标缺乏对MS发生的细微病理变化的敏感性和特异性,Zhang等[44]在2012年开发的一个多室模型轴突定向扩散和密度成像(neurite orientation dispersion and density imaging,NODDI),该模型能够估计更具体的指标,如神经轴突密度、定向和游离水成分。与DTI测量结果相比,NODDI对神经退行性变显示出更高的特异性和敏感性。NODDI比DTI能更好地区分取向色散和纤维密度,在MS微观结构中提供更好的对比度,并且可能对神经轴突定向等微观结构特征具有更大的特异性[45]

2.4 磁化传递成像

       磁化传递成像(magnetization transfer imaging,MTI)是通过测量游离水分子和中枢神经系统大分子(如髓鞘中的脂质)之间的相互作用来评估髓鞘的形成和组织的损伤。MTI的半定量指标是MT比值(MT ratio,MTR),它代表随髓鞘和轴突含量,MTR降低表明自由水与与之紧密接触的脑组织基质交换磁化的能力降低。在临床应用中,MTI技术主要与Gd联合应用以增强强化与非强化组织之间的对比度,或与TOF联合应用以增强血液与其他组织之间的对比度。

       有研究表明,MTI可能是评估MS患者再髓鞘程度有价值的技术,MS患者的MTR低于正常对照组,SPMS患者的MTR低于RRMS患者,MTR异常在脑室附近更显著,有助于发现除脱髓鞘病变以外的白质损伤迹象[46]。5年随访结果显示,MS病灶内MTR显著降低,NAWM也发生了明显的病灶[47]

       MTI针对MS的特异性是有限的,因为MTR可以受到含水量(水肿、炎症)和激活的活化小胶质细胞的影响,并且取决于采集和扫描仪参数的差异[48]。定量MT (quantitative magnetization transfer,qMT)通过提供更多的组织特异性指标来减少水肿等的影响[49];不均匀磁化转移(inhomogeneous magnetization transfer,ihMT)对中枢神经系统的髓磷脂具有特异性[50]等一些新的MTI技术对MS的特异性诊断有一定的帮助。

2.5 铁沉积成像

       磁敏感加权成像(susceptibility weighted imaging,SWI)是根据不同组织间的磁化率差异来提供图像对比的一种成像方法。MS在SWI特征表现是白质病中出现中心小静脉,被称为中心静脉征(central vein sign,CVS),这是静脉周围炎性脱髓鞘的影像学表现。MS的病理特征是白质和灰质的炎性脱髓鞘,脱髓鞘是由髓鞘蛋白特异性T细胞和髓鞘吞噬巨噬细胞介导的炎症反应,在过程中铁被巨噬细胞吞噬保留,这可以通过SWI以铁沉积的形式表现出来。MS患者在尾状核、苍白球、壳核、丘脑等部位发现有铁质沉积的表现[51]。MS病灶在SWI的中央静脉信号的存在,可增进对MS病变分布的了解,并可作为MS疾病的辅助诊断标准。基于一些研究,提出了40%的临界阈值(其中40%的白质病变显示CVS)来区分MS和非MS疾病[52]。但SWI只能查看铁质沉积的形态,不能进行定量。

       磁敏感定量成像技术(quantitative susceptibility mapping,QSM)利用相位图信息而不是幅值图信息进行成像。QSM使用不同的采集方法和后处理技术,以及年龄等因素的影响,研究结论不完全一致[53]。对MS患者QSM成像的回顾性研究表明,随着病灶长时间的变化,病变易感值急剧增加[54]。QSM成功地检测到健康对照组(healthy control,HC)和CIS患者尾状核和壳核的差异,HC和MS受试者苍白球的差异,随着神经功能缺损的加重,敏感性增高[55]。MS病灶中小胶质细胞中的铁来源可能是少突胶质细胞和髓鞘,它们在被破坏后释放铁到细胞外空间[56]。QSM检测到白质灶变中的铁主要积聚在病灶的边缘、T2WI显示的病灶外侧,或者QSM阳性值与脑脊液检查相关[57]

2.6 集成磁共振成像

       集成磁共振成像(synthetic magnetic resonance imaging,SyMRI)一次扫描可以得到T1、T2弛豫时间和质子密度,以及R1和R2弛豫率等多个定量参数,而且序列稳定,具有重复性和可靠性[58, 59]。定量集成磁共振成像可以避免每个定量参数(如:T1 mapping、T2 mapping、PD、R1、R2等)单独采集,耗时而且扫描层数不一致等影响扫描结果的缺点。SyMRI除了可以得到定量数据外,还可以得到多组对比加权图像,包括T1加权、T2加权和FLAIR图像等多组对比图像,而不需要额外的扫描时间[60]

       质子密度、T1弛豫和T2弛豫定量对中枢神经系统疾病的诊断有较大的临床意义,并被证明是MS的诊断和治疗评估很有价值的方法[61, 62]。有研究表明,NAWM的T2弛豫时间的延长是神经元损伤的标志[63]。MS患者的皮质和丘脑中存在细微的灰质损伤,其表现为T1弛豫的延长;皮质T1弛豫的延长是认知功能障碍的一个独立预测因子[64]

       SyMRI通过相关的后处理算法还可以得到髓鞘体积分数(myelin volume fraction,MVF),MVF是测量髓鞘含量的重要参数。MVF与健康人群[65]和MS患者[66]的髓磷脂值与组织学相关,因此适用于MS患者的髓鞘评估[59];它对斑块或周围区与NAWM的差异有较好的敏感性,可以为MS患者的显微组织损伤提供额外的补充信息,为临床诊断和预后评估提供依据[66]。5年随访结果是MWF每年损失1.7%,其降低提示MS脑内髓鞘完整性的改变和髓鞘的丢失能是弥漫性和长时间的,慢性进行性髓鞘损伤是一个发生多年的演变过程[47]

       因此,SyMRI不仅可以定量显示MS患者斑块和斑块周围白质的组织异常,而且还能测量髓鞘的含量。但是目前SyMRI的临床研究相对较少,关于各组定量值以及定量值比值的临床价值需要更多的探索。

       综上所述,近年来,脑定量磁共振的大量研究为研究MS的发病机制及临床应用提供了有价值的信息。但是,图像采集相关的参数的标准化、定量数据的诊断阈值等还需要大量多中心数据的支持以及国家化专家共识的形成。期待未来更多有价值的研究使各种定量技术转化为临床实践,为MS的临床诊断、疗效评价、预测进展及预后评估提供影像学依据。

1
Kobelt G, Thompson A, Berg J, et al. New insights into the burden and costs of multiple sclerosis in Europe[J]. Mult Scler, 2017, 23(8): 1123-1136. DOI: 10.1177/1352458517694432.
2
Michel L. Environmental factors in the development of multiple sclerosis[J]. Rev Neurol (Paris), 2018, 174(6): 372-377. DOI: 10.1016/j.neurol.2018.03.010.
3
Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria[J]. Lancet Neurol, 2018, 17(2): 162-173. DOI: 10.1016/S1474-4422(17)30470-2.
4
Wattjes MP, Rovira A, Miller D, et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-establishing disease prognosis and monitoring patients[J]. Nat Rev Neurol, 2015, 11(10): 597-606. DOI: 10.1038/nrneurol.2015.157.
5
Rotstein D, Montalban X. Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis[J]. Nat Rev Neurol, 2019, 15(5): 287-300. DOI: 10.1038/s41582-019-0170-8.
6
Geraldes R, Ciccarelli O, Barkhof F, et al. The current role of MRI in differentiating multiple sclerosis from its imaging mimics[J]. Nat Rev Neurol, 2018, 14(4): 199-213. DOI: 10.1038/nrneurol.2018.14.
7
中华医学会神经病学分会神经免疫学组, 中国免疫学会神经免疫分会. 多发性硬化诊断和治疗中国专家共识(2014版)[J]. 中华神经科杂志, 2015, 48(5): 362-367.
Neuroimmunlolgy group of neurology branch of Chinese Medical Association, Neuroimmunology branch of Chinese Society of Immunology. Chinese expert consensus on diagnosis and treatment of multiple sclerosis (2014)[J]. Chin J Neurology, 2015, 48(5): 362-367.
8
中国免疫学会神经免疫分会, 中华医学会神经病学分会神经免疫学组. 多发性硬化诊断和治疗中国专家共识(2018版)[J]. 中国神经免疫学和神经病学杂志, 2018, 25(6): 387-394. DOI: 10.3969/j.issn.1006-2963.2018.06.001.
Neuroimmunology branch of Chinese Society of Immunology, Neuroimmunlolgy group of neurology branch of Chinese Medical Association. Chinese expert consensus on diagnosis and treatment of multiple sclerosis (2018)[J]. Chin J Neuroimmunology & Neurology, 2018, 25(6): 387-394. DOI: 10.3969/j.issn.1006-2963.2018.06.001.
9
Lassmann H. Multiple sclerosis pathology[J]. Cold Spring Harb Perspect Med, 2018, 8(3): 1-5. DOI: 10.1101/cshperspect.a028936.
10
Mahad DH, Trapp BD, Lassmann H. Pathological mechanisms in progressive multiple sclerosis[J]. Lancet Neurol, 2015, 14(2): 183-193. DOI: 10.1016/S1474-4422(14)70256-X.
11
Filippi M, Falini A, Arnold DL, et al. Magnetic resonance techniques for the in vivo assessment of multiple sclerosis pathology: consensus report of the white matter study group[J]. J Magn Reson Imaging, 2005, 21(6): 669-675. DOI: 10.1002/jmri.20336.
12
Miller DH, Thompson AJ, Filippi M. Magnetic resonance studies of abnormalities in the normal appearing white matter and grey matter in multiple sclerosis[J]. J Neurol, 2003, 250(12): 1407-1419. DOI: 10.1007/s00415-003-0243-9.
13
Stankiewicz JM, Glanz BI, Healy BC, et al. Brain MRI lesion load at 1.5 T and 3 T versus clinical status in multiple sclerosis[J]. J Neuroimaging, 2011, 21(2): 50-56. DOI: 10.1111/j.1552-6569.2009.00449.x.
14
Filippi M, Rocca MA, Ciccarelli O, et al. MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines[J]. Lancet Neurol, 2016, 15(3): 292-303. DOI: 10.1016/S1474-4422(15)00393-2.
15
Frischer JM, Bramow S, Dal-Bianco A, et al. The relation between inflammation and neurodegeneration in multiple sclerosis brains[J]. Brain, 2009, 132(Pt 5): 1175-1189. DOI: 10.1093/brain/awp070.
16
Zivadinov R, Ramasamy DP, Hagemeier J, et al. Evaluation of leptomeningeal contrast enhancement using pre-and postcontrast subtraction 3D-FLAIR imaging in multiple sclerosis[J]. AJNR Am J Neuroradiol, 2018, 39(4): 642-647. DOI: 10.3174/ajnr.A5541.
17
Eichinger P, Kirschke JS, Hoshi MM, et al. Pre-and postcontrast 3D double inversion recovery sequence in multiple sclerosis: A aimple and effective MR imaging protocol[J]. AJNR Am J Neuroradiol, 2017, 38(10): 1941-1945. DOI: 10.3174/ajnr.A5329.
18
Absinta M, Vuolo L, Rao A, et al. Gadolinium-based MRI characterization of leptomeningeal inflammation in multiple sclerosis[J]. Neurology, 2015, 85(1): 18-28. DOI: 10.1212/WNL.0000000000001587.
19
Hamcan S, Battal B, Akgun V, et al. The value of qualitative and quantitative assessment of lesion to cerebral cortex signal ratio on double inversion recovery sequence in the differentiation of demyelinating plaques from non-specific T2 hyperintensities[J]. Eur Radiol, 2017, 27(2): 763-771. DOI: 10.1007/s00330-016-4379-2.
20
Futatsuya K, Kakeda S, Yoneda T, et al. Juxtacortical lesions in multiple sclerosis: Assessment of gray matter involvement using phase difference-enhanced imaging (PADRE)[J]. Magn Reson Med Sci, 2016, 15(4): 349-354. DOI: 10.2463/mrms.mp.2015-0099.
21
Tartaglia MC, Arnold DL. The role of MRS and fMRI in multiple sclerosis[J]. Adv Neurol, 2006, 98(1): 185-202.
22
Nordengen K, Heuser C, Rinholm JE, et al. Localisation of N-acetylaspartate in oligodendrocytes/myelin[J]. Brain Struct Funct, 2015, 220(2): 899-917. DOI: 10.1007/s00429-013-0691-7.
23
Nakamura K, Brown RA, Narayanan S, et al. Diurnal fluctuations in brain volume: Statistical analyses of MRI from large populations[J]. Neuroimage, 2015, 118(9): 126-132. DOI: 10.1016/j.neuroimage.2015.05.077.
24
Wattjes MP, Harzheim M, Lutterbey GG, et al. Axonal damage but no increased glial cell activity in the normal-appearing white matter of patients with clinically isolated syndromes suggestive of multiple sclerosis using high-field magnetic resonance spectroscopy[J]. AJNR Am J Neuroradiol, 2007, 28(8): 1517-1522. DOI: 10.3174/ajnr.A0594.
25
武传华, 张志国, 辛德友. 脑多发性硬化1H-MR质子波谱分析应用研究[J]. 磁共振成像, 2014, 5(1): 19-23. DOI: 10.3969/j.issn.1674-8034.2014.01.005.
Wu CH, Zhang ZG, Xin DY. 1H magentic resonance spectroscopy in the diagnoisis of multiple sclerosis[J]. Chin J Magn Reson Imaging, 2014, 5(1): 19-23. DOI: 10.3969/j.issn.1674-8034.2014.01.005.
26
Srinivasan R, Sailasuta N, Hurd R, et al. Evidence of elevated glutamate in multiple sclerosis using magnetic resonance spectroscopy at 3 T[J]. Brain, 2005, 128(Pt 5): 1016-1025. DOI: 10.1093/brain/awh467.
27
Azevedo CJ, Kornak J, Chu P, et al. In vivo evidence of glutamate toxicity in multiple sclerosis[J]. Ann Neurol, 2014, 76(2): 269-278. DOI: 10.1002/ana.24202.
28
Sun X, Tanaka M, Kondo S, et al. Clinical significance of reduced cerebral metabolism in multiple sclerosis: a combined PET and MRI study[J]. Ann Nucl Med, 1998, 12(2): 89-94. DOI: 10.1007/BF03164835.
29
Rosen BR, Belliveau JW, Vevea JM, et al. Perfusion imaging with NMR contrast agents[J]. Magn Reson Med, 1990, 14(2): 249-265. DOI: 10.1002/mrm.1910140211.
30
Eskildsen SF, Gyldensted L, Nagenthiraja K, et al. Increased cortical capillary transit time heterogeneity in Alzheimer's disease: a DSC-MRI perfusion study[J]. Neurobiol Aging, 2017, 50(2): 107-118. DOI: 10.1016/j.neurobiolaging.2016.11.004.
31
Inglese M, Park SJ, Johnson G, et al. Deep gray matter perfusion in multiple sclerosis: dynamic susceptibility contrast perfusion magnetic resonance imaging at 3 T[J]. Arch Neurol, 2007, 64(2): 196-202. DOI: 10.1001/archneur.64.2.196.
32
Corno S, Giani L, Lagana MM, et al. The brain effect of the migraine attack: an ASL MRI study of the cerebral perfusion during a migraine attack[J]. Neurol Sci, 2018, 39(Suppl 1): 73-74. DOI: 10.1007/s10072-018-3346-x.
33
Telischak NA, Detre JA, Zaharchuk G. Arterial spin labeling MRI: clinical applications in the brain[J]. J Magn Reson Imaging, 2015, 41(5): 1165-1180. DOI: 10.1002/jmri.24751.
34
Ota M, Sato N, Nakata Y, et al. Abnormalities of cerebral blood flow in multiple sclerosis: a pseudocontinuous arterial spin labeling MRI study[J]. Magn Reson Imaging, 2013, 31(6): 990-995. DOI: 10.1016/j.mri.2013.03.016.
35
马笑笑, 吕晋浩, 唐静, 等. 复发缓解型多发性硬化患者看似正常脑白质区脑血流量分析[J]. 磁共振成像, 2017, 8(4): 270-275. DOI: 10.12015/issn.1674-8034.2017.04.007.
Ma XX, Lv JH, Tang J, et al. Reduced perfusion in normal appearing white matter in relapsing-remitting multiple sclerosis using 3D pseudocontinuous arterial spin labeling[J]. Chin J Magn Reson Imaging, 2017, 8(4): 270-275. DOI: 10.12015/issn.1674-8034.2017.04.007.
36
de la Pena MJ, Pena IC, Garcia PG, et al. Early perfusion changes in multiple sclerosis patients as assessed by MRI using arterial spin labeling[J]. Acta Radiol Open, 2019, 8(12): 2058460119894214. DOI: 10.1177/2058460119894214.
37
Law M, Saindane AM, Ge Y, et al. Microvascular abnormality in relapsing-remitting multiple sclerosis: perfusion MR imaging findings in normal-appearing white matter[J]. Radiology, 2004, 231(3): 645-652. DOI: 10.1148/radiol.2313030996.
38
Bester M, Forkert ND, Stellmann JP, et al. Increased perfusion in normal appearing white matter in high inflammatory multiple sclerosis patients[J]. PLoS One, 2015, 10(3): e0119356. DOI: 10.1371/journal.pone.0119356.
39
Iwasawa T, Matoba H, Ogi A, et al. Diffusion-weighted imaging of the human optic nerve: a new approach to evaluate optic neuritis in multiple sclerosis[J]. Magn Reson Med, 1997, 38(3): 484-491. DOI: 10.1002/mrm.1910380317.
40
Cercignani M, Bozzali M, Iannucci G, et al. Magnetisation transfer ratio and mean diffusivity of normal appearing white and grey matter from patients with multiple sclerosis[J]. J Neurol Neurosurg Psychiatry, 2001, 70(3): 311-317. DOI: 10.1136/jnnp.70.3.311.
41
Roosendaal SD, Geurts JJ, Vrenken H, et al. Regional DTI differences in multiple sclerosis patients[J]. Neuroimage, 2009, 44(4): 1397-1403. DOI: 10.1016/j.neuroimage.2008.10.026.
42
Andersen O, Hildeman A, Longfils M, et al. Diffusion tensor imaging in multiple sclerosis at different final outcomes[J]. Acta Neurol Scand, 2018, 137(2): 165-173. DOI: 10.1111/ane.12797.
43
Kolasa M, Hakulinen U, Brander A, et al. Diffusion tensor imaging and disability progression in multiple sclerosis: A 4-year follow-up study[J]. Brain Behav, 2019, 9(1): e01194. DOI: 10.1002/brb3.1194.
44
Zhang H, Schneider T, Wheeler-Kingshott CA, et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain[J]. Neuroimage, 2012, 61(4): 1000-1016. DOI: 10.1016/j.neuroimage.2012.03.072.
45
Schneider T, Brownlee W, Zhang H, et al. Sensitivity of multi-shell NODDI to multiple sclerosis white matter changes: a pilot study[J]. Funct Neurol, 2017, 32(2): 97-101. DOI: 10.11138/fneur/2017.32.2.097.
46
Liu Z, Pardini M, Yaldizli O, et al. Magnetization transfer ratio measures in normal-appearing white matter show periventricular gradient abnormalities in multiple sclerosis[J]. Brain, 2015, 138(Pt 5): 1239-1246. DOI: 10.1093/brain/awv065.
47
Vavasour IM, Huijskens SC, Li DK, et al. Global loss of myelin water over 5 years in multiple sclerosis normal-appearing white matter[J]. Mult Scler, 2018, 24(12): 1557-1568. DOI: 10.1177/1352458517723717.
48
Brown JW, Pardini M, Brownlee WJ, et al. An abnormal periventricular magnetization transfer ratio gradient occurs early in multiple sclerosis[J]. Brain, 2017, 140(2): 387-398. DOI: 10.1093/brain/aww296.
49
Bagnato F, Hametner S, Franco G, et al. Selective inversion recovery quantitative magnetization transfer brain MRI at 7T: Clinical and postmortem validation in multiple sclerosis[J]. J Neuroimaging, 2018, 28(4): 380-388. DOI: 10.1111/jon.12511.
50
Zhang L, Chen T, Tian H, et al. Reproducibility of inhomogeneous magnetization transfer (ihMT): A test-retest, multi-site study[J]. Magn Reson Imaging, 2019, 57(4): 243-249. DOI: 10.1016/j.mri.2018.11.010.
51
Habib CA, Liu M, Bawany N, et al. Assessing abnormal iron content in the deep gray matter of patients with multiple sclerosis versus healthy controls[J]. AJNR Am J Neuroradiol, 2012, 33(2): 252-258. DOI: 10.3174/ajnr.A2773.
52
Sparacia G, Agnello F, Gambino A, et al. Multiple sclerosis: High prevalence of the 'central vein' sign in white matter lesions on susceptibility-weighted images[J]. Neuroradiol J, 2018, 31(4): 356-361. DOI: 10.1177/1971400918763577.
53
Schweser F, Raffaini AL, Hagemeier J, et al. Mapping of thalamic magnetic susceptibility in multiple sclerosis indicates decreasing iron with disease duration: A proposed mechanistic relationship between inflammation and oligodendrocyte vitality[J]. Neuroimage, 2018, 167(2): 438-452. DOI: 10.1016/j.neuroimage.2017.10.063.
54
Chen W, Gauthier SA, Gupta A, et al. Quantitative susceptibility mapping of multiple sclerosis lesions at various ages[J]. Radiology, 2014, 271(1): 183-192. DOI: 10.1148/radiol.13130353.
55
Langkammer C, Liu T, Khalil M, et al. Quantitative susceptibility mapping in multiple sclerosis[J]. Radiology, 2013, 267(2): 551-559. DOI: 10.1148/radiol.12120707.
56
Hametner S, Wimmer I, Haider L, et al. Iron and neurodegeneration in the multiple sclerosis brain[J]. Ann Neurol, 2013, 74(6): 848-861. DOI: 10.1002/ana.23974.
57
Wisnieff C, Ramanan S, Olesik J, et al. Quantitative susceptibility mapping (QSM) of white matter multiple sclerosis lesions: Interpreting positive susceptibility and the presence of iron[J]. Magn Reson Med, 2015, 74(2): 564-570. DOI: 10.1002/mrm.25420.
58
Hagiwara A, Hori M, Cohen-Adad J, et al. Linearity, bias, intrascanner repeatability, and interscanner reproducibility of quantitative multidynamic multiecho sequence for rapid simultaneous relaxometry at 3 T: A validation study with a standardized phantom and healthy controls[J]. Invest Radiol, 2019, 54(1): 39-47. DOI: 10.1097/RLI. 0000000000000510.
59
Warntjes M, Engstrom M, Tisell A, et al. Modeling the presence of myelin and edema in the brain based on multi-parametric quantitative MRI[J]. Front Neurol, 2016, 7(1): 1-15. DOI: 10.3389/fneur.2016.00016.
60
Krauss W, Gunnarsson M, Nilsson M, et al. Conventional and synthetic MRI in multiple sclerosis: a comparative study[J]. Eur Radiol, 2018, 28(4): 1692-1700. DOI: 10.1007/s00330-017-5100-9.
61
Louapre C, Bodini B, Lubetzki C, et al. Imaging markers of multiple sclerosis prognosis[J]. Curr Opin Neurol, 2017, 30(3): 231-236. DOI: 10.1097/WCO.0000000000000456.
62
Rovira A, de Stefano N. MRI monitoring of spinal cord changes in patients with multiple sclerosis[J]. Curr Opin Neurol, 2016, 29(4): 445-452. DOI: 10.1097/WCO.0000000000000343.
63
Hasan KM, Walimuni IS, Abid H, et al. Human brain atlas-based multimodal MRI analysis of volumetry, diffusimetry, relaxometry and lesion distribution in multiple sclerosis patients and healthy adult controls: implications for understanding the pathogenesis of multiple sclerosis and consolidation of quantitative MRI results in MS[J]. J Neurol Sci, 2012, 313(1-2): 99-109. DOI: 10.1016/j.jns.2011.09.015.
64
Steenwijk MD, Vrenken H, Jonkman LE, et al. High-resolution T1-relaxation time mapping displays subtle, clinically relevant, gray matter damage in long-standing multiple sclerosis[J]. Mult Scler, 2016, 22(10): 1279-1288. DOI: 10.1177/1352458515615953.
65
Warntjes JBM, Persson A, Berge J, et al. Myelin detection using rapid quantitative MR imaging correlated to macroscopically registered luxol fast blue-stained brain specimens[J]. AJNR Am J Neuroradiol, 2017, 38(6): 1096-1102. DOI: 10.3174/ajnr.A5168.
66
Saccenti L, Hagiwara A, Andica C, et al. Myelin measurement using quantitative magnetic resonance imaging: A correlation study comparing various imaging techniques in patients with multiple sclerosis[J]. Cells, 2020, 9(2): 393-406. DOI: 10.3390/cells9020393.

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