Department of Radiology, Centre Hospitalier Universitaire Vaudois.
Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne, Lausanne.
Invest Radiol. 2019 Jun;54(6):356-364. doi: 10.1097/RLI.0000000000000551.
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.
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.
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.
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.
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。
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。
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%。
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。
