Aman Aniket, Hoskote Aaryaman, Jadhav Kshitij S, Aggarwal Bharat
Max Super Speciality Hospital, Saket, New Delhi, India.
Indian Institute of Technology - Bombay, Mumbai, Maharashtra, India.
Neurosci Lett. 2025 Feb 6;848:138118. doi: 10.1016/j.neulet.2025.138118. Epub 2025 Jan 7.
Clinical brain MRI scans, including contrast-enhanced (CE-MR) images, represent an underutilized resource for neuroscience research due to technical heterogeneity.
To evaluate the reliability of morphometric measurements from CE-MR scans compared to non-contrast MR (NC-MR) scans in normal individuals.
T1-weighted CE-MR and NC-MR scans from 59 normal participants (aged 21-73 years) were compared using CAT12 and SynthSeg+ segmentation tools. Volumetric measurements and age prediction efficacy were analyzed.
SynthSeg+ demonstrated high reliability (ICCs > 0.90) for most brain structures between CE-MR and NC-MR scans, with discrepancies in CSF and ventricular volumes. CAT12 showed inconsistent performance. Age prediction models using SynthSeg + yielded comparable results for both scan types.
Deep learning-based approaches like SynthSeg+ can reliably process CE-MR scans for morphometric analysis, potentially broadening the application of clinically acquired CE-MR images in neuroimaging research.
由于技术异质性,包括对比增强(CE-MR)图像在内的临床脑部MRI扫描在神经科学研究中是一种未充分利用的资源。
评估正常个体中CE-MR扫描与非对比MR(NC-MR)扫描形态测量的可靠性。
使用CAT12和SynthSeg+分割工具比较了59名正常参与者(年龄21 - 73岁)的T1加权CE-MR和NC-MR扫描。分析了体积测量和年龄预测效能。
对于大多数脑结构,SynthSeg+在CE-MR和NC-MR扫描之间显示出高可靠性(组内相关系数>0.90),脑脊液和脑室体积存在差异。CAT12表现不一致。使用SynthSeg+的年龄预测模型对两种扫描类型产生了可比的结果。
像SynthSeg+这样基于深度学习的方法可以可靠地处理CE-MR扫描以进行形态测量分析,可能会拓宽临床获取的CE-MR图像在神经影像研究中的应用。