Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain.
E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain; Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, UK.
Neuroimage Clin. 2020;28:102411. doi: 10.1016/j.nicl.2020.102411. Epub 2020 Sep 9.
Diffusion magnetic resonance imaging can reveal quantitative information about the tissue changes in multiple sclerosis. The recently developed multi-compartment spherical mean technique can map different microscopic properties based only on local diffusion signals, and it may provide specific information on the underlying microstructural modifications that arise in multiple sclerosis. Given that the lesions in multiple sclerosis may reflect different degrees of damage, we hypothesized that quantitative diffusion maps may help characterize the severity of lesions "in vivo" and correlate these to an individual's clinical profile. We evaluated this in a cohort of 59 multiple sclerosis patients (62% female, mean age 44.7 years), for whom demographic and disease information was obtained, and who underwent a comprehensive physical and cognitive evaluation. The magnetic resonance imaging protocol included conventional sequences to define focal lesions, and multi-shell diffusion imaging was used with b-values of 1000, 2000 and 3000 s/mm in 180 encoding directions. Quantitative diffusion properties on a macro- and micro-scale were used to discriminate distinct types of lesions through a k-means clustering algorithm, and the number and volume of those lesion types were correlated with parameters of the disease. The combination of diffusion tensor imaging metrics (fractional anisotropy and radial diffusivity) and multi-compartment spherical mean technique values (microscopic fractional anisotropy and intra-neurite volume fraction) differentiated two type of lesions, with a prediction strength of 0.931. The B-type lesions had larger diffusion changes compared to the A-type lesions, irrespective of their location (P < 0.001). The number of A and B type lesions was similar, although in juxtacortical areas B-type lesions predominated (60%, P < 0.001). Also, the percentage of B-type lesion volume was higher (64%, P < 0.001), indicating that these lesions were larger. The number and volume of B-type lesions was related to the severity of disease evolution, clinical disability and cognitive decline (P = 0.004, Bonferroni correction). Specifically, more and larger B-type lesions were correlated with a worse Multiple Sclerosis Severity Score, cerebellar function and cognitive performance. Thus, by combining several microscopic and macroscopic diffusion properties, the severity of damage within focal lesions can be characterized, further contributing to our understanding of the mechanisms that drive disease evolution. Accordingly, the classification of lesion types has the potential to permit more specific and better-targeted treatment of patients with multiple sclerosis.
扩散磁共振成像可以揭示多发性硬化症组织变化的定量信息。最近开发的多腔球形均值技术可以仅基于局部扩散信号来绘制不同的微观特性,它可能提供关于多发性硬化症中出现的潜在微观结构改变的特定信息。鉴于多发性硬化症的病变可能反映出不同程度的损伤,我们假设定量扩散图可能有助于在“体内”表征病变的严重程度,并将其与个体的临床特征相关联。我们在 59 名多发性硬化症患者(62%为女性,平均年龄 44.7 岁)的队列中评估了这一点,这些患者获得了人口统计学和疾病信息,并接受了全面的身体和认知评估。磁共振成像方案包括用于定义局灶性病变的常规序列,以及使用 1000、2000 和 3000 s/mm 的 b 值在 180 个编码方向上进行多壳扩散成像。使用 k-均值聚类算法,通过宏观和微观尺度上的定量扩散特性来区分不同类型的病变,并且那些病变类型的数量和体积与疾病参数相关联。扩散张量成像指标(各向异性分数和径向扩散系数)和多腔球形均值技术值(微观各向异性分数和神经元内体积分数)的组合区分了两种类型的病变,预测强度为 0.931。与 A 型病变相比,B 型病变的扩散变化更大,无论其位置如何(P<0.001)。尽管在皮质下区域 B 型病变占主导地位(60%,P<0.001),但 A 和 B 型病变的数量相似。此外,B 型病变体积的百分比更高(64%,P<0.001),表明这些病变更大。B 型病变的数量和体积与疾病进展的严重程度、临床残疾和认知能力下降相关(P=0.004,Bonferroni 校正)。具体而言,更多和更大的 B 型病变与多发性硬化症严重程度评分、小脑功能和认知表现更差相关。因此,通过结合几种微观和宏观扩散特性,可以对病灶内损伤的严重程度进行特征描述,进一步有助于我们了解导致疾病进展的机制。因此,病变类型的分类有可能允许对多发性硬化症患者进行更具体和更有针对性的治疗。