针对脑小血管疾病的 7-T TOF-MRA 图谱动脉定量建模。

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Radiology Experimental Pub Date : 2024-11-05 DOI:10.1186/s41747-024-00512-7
Zhixin Li, Dongbiao Sun, Chen Ling, Li Bai, Jinyuan Zhang, Yue Wu, Yun Yuan, Zhaoxia Wang, Zhe Wang, Yan Zhuo, Rong Xue, Zihao Zhang
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引用次数: 0

摘要

背景:我们开发了一个框架,用于在 7 T 飞行时间磁共振血管造影上分割和建模扁桃体状动脉(LSA),并在脑常染色体显性动脉病伴皮层下梗死和白质脑病(CADASIL)患者和对照组中测试了该框架的性能:我们对 29 名 CADASIL 患者和 21 名对照组进行了前瞻性研究。该框架包括一个用于精细分割的小片段卷积神经网络(SP-CNN)、一个用于LSA建模的随机森林和一个用于去除错误分支的筛选模型。我们将 SP-CNN 的分割性能与竞争网络进行了比较。对十名动脉瘤患者进行了不同分辨率的外部验证。计算了每个网络与人工分割之间的骰子相似系数(DSC)和豪斯多夫距离(HD)。将 LSA 中心线、直径和长度的建模结果与四位神经学家的手动标记结果进行了比较:结果:在分割 LSA 时,SP-CNN 获得了更高的 DSC(92.741 ± 2.789,平均值 ± 标准差)和更低的 HD(0.610 ± 0.141 mm)。在外部验证中,它的表现也优于竞争网络(DSC 82.6 ± 5.5,HD 0.829 ± 0.143 mm)。在一级分支血管长度(中位数-0.040 毫米,四分位距-0.209 至 0.059 毫米)和二级分支血管长度(0.202 毫米,0.016 至 0.537 毫米)以及一级分支血管中心线偏移量(0.071 毫米,0.065 至 0.078 毫米)和二级分支血管中心线偏移量(0.072 毫米,0.064 至 0.080 毫米)方面,框架与人工的差异低于人工观察者之间的差异,P 为 结论:与人工标注相比,我们的 LSA 建模/量化框架具有很高的可靠性和准确性:NCT05902039 ( https://clinicaltrials.gov/study/NCT05902039?cond=NCT05902039 ).相关性声明:所提出的自动分割和建模框架可精确量化皮样动脉的形态参数。这项创新技术简化了脑小血管疾病的诊断和研究,消除了人工标记的负担,促进了队列研究和临床诊断:要点:LSA 的形态对 CSVD 的诊断非常重要,但难以量化。所提出的算法达到了与神经科医生手工标记相当的效果。我们的方法可以提供标准化的定量结果,减轻放射科医生在队列研究中的工作量。
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Quantitative modeling of lenticulostriate arteries on 7-T TOF-MRA for cerebral small vessel disease.

Background: We developed a framework for segmenting and modeling lenticulostriate arteries (LSAs) on 7-T time-of-flight magnetic resonance angiography and tested its performance on cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) patients and controls.

Methods: We prospectively included 29 CADASIL patients and 21 controls. The framework includes a small-patch convolutional neural network (SP-CNN) for fine segmentation, a random forest for modeling LSAs, and a screening model for removing wrong branches. The segmentation performance of our SP-CNN was compared to competitive networks. External validation with different resolution was performed on ten patients with aneurysms. Dice similarity coefficient (DSC) and Hausdorff distance (HD) between each network and manual segmentation were calculated. The modeling results of the centerlines, diameters, and lengths of LSAs were compared against manual labeling by four neurologists.

Results: The SP-CNN achieved higher DSC (92.741 ± 2.789, mean ± standard deviation) and lower HD (0.610 ± 0.141 mm) in the segmentation of LSAs. It also outperformed competitive networks in the external validation (DSC 82.6 ± 5.5, HD 0.829 ± 0.143 mm). The framework versus manual difference was lower than the manual inter-observer difference for the vessel length of primary branches (median -0.040 mm, interquartile range -0.209 to 0.059 mm) and secondary branches (0.202 mm, 0.016-0.537 mm), as well as for the offset of centerlines of primary branches (0.071 mm, 0.065-0.078 mm) and secondary branches (0.072, 0.064-0.080 mm), with p < 0.001 for all comparisons.

Conclusion: Our framework for LSAs modeling/quantification demonstrated high reliability and accuracy when compared to manual labeling.

Trial registration: NCT05902039 ( https://clinicaltrials.gov/study/NCT05902039?cond=NCT05902039 ).

Relevance statement: The proposed automatic segmentation and modeling framework offers precise quantification of the morphological parameters of lenticulostriate arteries. This innovative technology streamlines diagnosis and research of cerebral small vessel disease, eliminating the burden of manual labeling, facilitating cohort studies and clinical diagnosis.

Key points: The morphology of LSAs is important in the diagnosis of CSVD but difficult to quantify. The proposed algorithm achieved the performance equivalent to manual labeling by neurologists. Our method can provide standardized quantitative results, reducing radiologists' workload in cohort studies.

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来源期刊
European Radiology Experimental
European Radiology Experimental Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
6.70
自引率
2.60%
发文量
56
审稿时长
18 weeks
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