Li Zhixin, Liang Li, Zhang Jinyuan, Fan Xueyi, Yang Yishuang, Yang Hua, Wang Qianyao, An Jing, Xue Rong, Zhuo Yan, Qian Hairong, Zhang Zihao
State Key Laboratory of Cognitive Science and Mental Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
NMR Biomed. 2025 Jun;38(6):e70042. doi: 10.1002/nbm.70042.
The pathological changes in deep medullary veins (DMVs) have been reported in various diseases. However, accurate modeling and quantification of DMVs remain challenging. We aim to propose and assess an automated approach for modeling and quantifying DMVs at 7 Tesla (7 T) MRI. A multi-echo-input Res-Net was developed for vascular segmentation, and a minimum path loss function was used for modeling and quantifying the geometric parameter of DMVs. Twenty-one patients diagnosed as subcortical vascular dementia (SVaD) and 20 condition matched controls were included in this study. The amplitude and phase images of gradient echo with five echoes were acquired at 7 T. Ten GRE images were manually labeled by two neurologists and compared with the results obtained by our proposed method. Independent samples t test and Pearson correlation were used for statistical analysis in our study, and p value < 0.05 was considered significant. No significant offset was found in centerlines obtained by human labeling and our algorithm (p = 0.734). The length difference between the proposed method and manual labeling was smaller than the error between different clinicians (p < 0.001). Patients with SVaD exhibited fewer DMVs (mean difference = -60.710 ± 21.810, p = 0.011) and higher curvature (mean difference = 0.12 ± 0.022, p < 0.0001), corresponding to their higher Vascular Dementia Assessment Scale-Cog (VaDAS-Cog) scores (mean difference = 4.332 ± 1.992, p = 0.036) and lower Mini-Mental State Examination (MMSE) (mean difference = -3.071 ± 1.443, p = 0.047). The MMSE scores were positively correlated with the numbers of DMVs (r = 0.437, p = 0.037) and were negatively correlated with the curvature (r = -0.426, p = 0.042). In summary, we proposed a novel framework for automated quantifying the morphologic parameters of DMVs. These characteristics of DMVs are expected to help the research and diagnosis of cerebral small vessel diseases with DMV lesions.
深髓静脉（DMV）的病理变化已在多种疾病中被报道。然而，对DMV进行精确建模和量化仍然具有挑战性。我们旨在提出并评估一种在7特斯拉（7T）磁共振成像（MRI）下对DMV进行建模和量化的自动化方法。开发了一种多回波输入的残差网络（Res-Net）用于血管分割，并使用最小路径损耗函数对DMV的几何参数进行建模和量化。本研究纳入了21例被诊断为皮质下血管性痴呆（SVaD）的患者和20例病情匹配的对照者。在7T下采集了具有五个回波的梯度回波的幅度和相位图像。由两名神经科医生对十幅梯度回波（GRE）图像进行手动标注，并与我们提出的方法所获得的结果进行比较。本研究采用独立样本t检验和Pearson相关性进行统计分析，p值<0.05被认为具有统计学意义。在人工标注和我们的算法所获得的中心线中未发现显著偏差（p = 0.734）。所提出的方法与人工标注之间的长度差异小于不同临床医生之间的误差（p < 0.001）。SVaD患者表现出较少的DMV（平均差异=-60.710±21.810，p = 0.011）和更高的曲率（平均差异=0.12±0.022，p < 0.0001），这与他们更高的血管性痴呆评估量表-认知（VaDAS-Cog）评分（平均差异=4.332±1.992，p = 0.036）和更低的简易精神状态检查表（MMSE）评分（平均差异=-3.071±1.443，p = 0.047）相对应。MMSE评分与DMV的数量呈正相关（r = 0.437，p = 0.037），与曲率呈负相关（r = -0.426，p = 0.042）。总之，我们提出了一种用于自动量化DMV形态学参数的新框架。DMV的这些特征有望有助于对伴有DMV病变的脑小血管疾病的研究和诊断。
