自动测量和规范化脊髓横截面积使用圆柱骨髓交叉点
Sandrine Bédard1, Julien Cohen-Adad1,2,3
1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
Frontiers in neuroimaging
|August 9, 2023
概括
本研究介绍了一种自动化方法来测量脊髓横截面积 (CSA),使用脊髓横截面 (PMJ) 作为参考. 这种方法减少了变异性,并改善了神经退行性疾病中的缩评估.
科学领域:
- 神经成像是一种神经成像.
- 生物标志物开发 生物标志物开发
- 神经退行性疾病 神经退行性疾病
背景情况:
- 脊髓横切面积 (CSA) 是神经退行性疾病中脊髓缩的一个关键生物标志物.
- 目前用于CSA测量的方法具有很高的跨主体可变性,并依赖于使用脊椎水平的不精确的手动正常化.
- 现有的规范化模型需要人工干预,这限制了它们的临床适用性.
研究的目的:
- 开发和验证一个自动化管道来测量脊髓CSA,使用一致的神经参考.
- 调查导致脊髓CSA在个体间变化的因素.
- 为脊髓CSA测量建立改进的正常化策略.
主要方法:
- 实施了一条自动化管道,用于脊髓细分和标记,使用来自英国生物银行 (N=804) 的T1wMRI扫描.
- 脊髓CSA被自动测量,使用带关 (PMJ) 作为空间参考.
- 逐步回归确定了CSA正常化的显著的人口和解剖学预测因素 (大脑体积,大脑体积,性别).
主要成果:
- 基于PMJ的自动化CSA测量与C2-C3椎间盘方法 (p=0.0002) 有显著差异.
- 使用已识别的预测因子进行正常化,PMJ CSA的变化系数降低了14.85%,C2-C3 CSA降低了15.13%.
- 最好的正常化模型结合了丘脑体积,大脑体积,性别和它们的相互作用.
结论:
- 成功实施了一个端到端的自动管道,用于从神经参考 (PMJ) 测量和规范脊髓CSA.
- 开发的规范化策略显著降低了脊髓CSA的个体间变异性.
- 这种自动化方法为评估脊髓缩提供了更精确和可重复的方法,需要在纵向研究中进一步验证.
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