Fully automatic detection of deep white matter T1 hypointense lesions in multiple sclerosis
Fully automatic detection of deep white matter T1 hypointense lesions in multiple sclerosis
复制标题
全自动检测多发性硬化症深部白质T1低信号病变
DOI：
10.1088/0031-9155/58/23/8323
复制
发表时间：
2013
影响因子：
3.5
通讯作者：
Alaleh Raji
中科院分区：
文献类型：
--
作者：
L. Spies;Anja Tewes;P. Suppa;R. Opfer;R. Buchert;G. Winkler;Alaleh Raji
提出了一种新的方法，用于在三维高分辨率T1加权磁共振（MR）图像中全自动检测候选白色物质（WM）T1低信号病变。根据定义，T1低信号病变具有与灰质（GM）相似的强度，因此在T1加权图像中比周围正常WM更暗。新方法使用标准分类算法将T1加权图像划分为GM、WM和脑脊液（CSF）。因此，通过标准分类算法，T1低信号病变被分配增加的GM概率。然后，针对健康个体的规范数据库的GM分量图像逐个体素地测试患者的GM分量图像。在预定义的深部WM掩模内的GM密度显著增加的簇（≥0.1 ml）被定义为病变。该算法的性能进行了评估体素水平的模拟研究。一个典型的T1病变模式的对比度范围从WM到皮质GM的最大骰子相似系数为60%，表明地面实况和自动检测之间的实质性协议。对10例多发性硬化患者的回顾性应用表明，96个T1低信号病灶中有93个被检测到。平均每例患者有3.6个假阳性T1低信号病灶，新方法有望支持T1加权图像中低信号病灶的检测，这需要在更大的患者样本中进行进一步评估。
A novel method is presented for fully automatic detection of candidate white matter (WM) T1 hypointense lesions in three-dimensional high-resolution T1-weighted magnetic resonance (MR) images. By definition, T1 hypointense lesions have similar intensity as gray matter (GM) and thus appear darker than surrounding normal WM in T1-weighted images. The novel method uses a standard classification algorithm to partition T1-weighted images into GM, WM and cerebrospinal fluid (CSF). As a consequence, T1 hypointense lesions are assigned an increased GM probability by the standard classification algorithm. The GM component image of a patient is then tested voxel-by-voxel against GM component images of a normative database of healthy individuals. Clusters (≥0.1 ml) of significantly increased GM density within a predefined mask of deep WM are defined as lesions. The performance of the algorithm was assessed on voxel level by a simulation study. A maximum dice similarity coefficient of 60% was found for a typical T1 lesion pattern with contrasts ranging from WM to cortical GM, indicating substantial agreement between ground truth and automatic detection. Retrospective application to 10 patients with multiple sclerosis demonstrated that 93 out of 96 T1 hypointense lesions were detected. On average 3.6 false positive T1 hypointense lesions per patient were found. The novel method is promising to support the detection of hypointense lesions in T1-weighted images which warrants further evaluation in larger patient samples.