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基于超高场 MP2RAGE 的多发性硬化病变的自动检测和分割。

Automated Detection and Segmentation of Multiple Sclerosis Lesions Using Ultra-High-Field MP2RAGE.

机构信息

Department of Radiology, Centre Hospitalier Universitaire Vaudois.

Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne, Lausanne.

出版信息

Invest Radiol. 2019 Jun;54(6):356-364. doi: 10.1097/RLI.0000000000000551.

Abstract

OBJECTIVES

The aim of this study was to develop a new automated segmentation method of white matter (WM) and cortical multiple sclerosis (MS) lesions visible on magnetization-prepared 2 inversion-contrast rapid gradient echo (MP2RAGE) images acquired at 7 T MRI.

MATERIALS AND METHODS

The proposed prototype (MSLAST [Multiple Sclerosis Lesion Analysis at Seven Tesla]) takes as input a single image contrast derived from the 7T MP2RAGE prototype sequence and is based on partial volume estimation and topological constraints. First, MSLAST performs a skull-strip of MP2RAGE images and computes tissue concentration maps for WM, gray matter (GM), and cerebrospinal fluid (CSF) using a partial volume model of tissues within each voxel. Second, MSLAST performs (1) connected-component analysis to GM and CSF concentration maps to classify small isolated components as MS lesions; (2) hole-filling in the WM concentration map to classify areas with low WM concentration surrounded by WM (ie, MS lesions); and (3) outlier rejection to the WM mask to improve the classification of small WM lesions. Third, MSLAST unifies the 3 maps obtained from 1, 2, and 3 processing steps to generate a global lesion mask.

RESULTS

Quantitative and qualitative assessments were performed using MSLAST in 25 MS patients from 2 research centers. Overall, MSLAST detected a median of 71% of MS lesions, specifically 74% of WM and 58% of cortical lesions, when a minimum lesion size of 6 μL was considered. The median false-positive rate was 40%. When a 15 μL minimal lesions size was applied, which is the approximation of the minimal size recommended for 1.5/3 T images, the median detection rate was 80% for WM and 63% for cortical lesions, respectively, and the median false-positive rate was 33%. We observed high correlation between MSLAST and manual segmentations (Spearman rank correlation coefficient, ρ = 0.91), although MSLAST underestimated the total lesion volume (average difference of 1.1 mL), especially in patients with high lesion loads. MSLAST also showed good scan-rescan repeatability within the same session with an average absolute volume difference and F1 score of 0.38 ± 0.32 mL and 84%, respectively.

CONCLUSIONS

We propose a new methodology to facilitate the segmentation of WM and cortical MS lesions at 7 T MRI, our approach uses a single MP2RAGE scan and may be of special interest to clinicians and researchers.

摘要

目的

本研究旨在开发一种新的自动化分割方法,用于对 7T MRI 上采集的磁化准备 2 反转对比快速梯度回波(MP2RAGE)图像中可见的白质(WM)和皮质多发性硬化(MS)病变进行分割。

材料与方法

所提出的原型(MSLAST [七特斯拉多发性硬化病变分析])以单个图像对比度作为输入,该对比度来自于 7T MP2RAGE 原型序列,并基于部分体积估计和拓扑约束。首先,MSLAST 对 MP2RAGE 图像进行头骨剥离,并使用每个体素内组织的部分体积模型计算 WM、灰质(GM)和脑脊液(CSF)的组织浓度图。其次,MSLAST 执行(1)连通分量分析,以 GM 和 CSF 浓度图为分类依据,将小的孤立成分归类为 MS 病变;(2)在 WM 浓度图中进行空洞填充,以将 WM 周围低 WM 浓度的区域归类为 MS 病变;(3)对 WM 掩模进行异常值拒绝处理,以改善小 WM 病变的分类。第三,MSLAST 将 1、2 和 3 个处理步骤获得的 3 个图谱进行统一,生成全局病变掩模。

结果

在来自 2 个研究中心的 25 名 MS 患者中,使用 MSLAST 进行了定量和定性评估。总体而言,当考虑最小病变大小为 6μL 时,MSLAST 检测到的 MS 病变中位数为 71%,具体为 WM 病变 74%,皮质病变 58%。假阳性率中位数为 40%。当应用最小病变大小为 15μL 时(这是对 1.5/3T 图像推荐的最小病变大小的近似值),WM 病变的中位检出率分别为 80%,皮质病变的中位检出率为 63%,假阳性率中位数为 33%。我们观察到 MSLAST 与手动分割之间具有高度相关性(Spearman 秩相关系数,ρ=0.91),尽管 MSLAST 低估了总病变体积(平均差异 1.1mL),尤其是在病变负荷较高的患者中。MSLAST 还在同一会话内显示出良好的扫描-重扫可重复性,平均绝对体积差异和 F1 分数分别为 0.38±0.32mL 和 84%。

结论

我们提出了一种新的方法,用于促进 7T MRI 上 WM 和皮质 MS 病变的分割,我们的方法仅使用单个 MP2RAGE 扫描,这可能对临床医生和研究人员特别有兴趣。

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