Vrenken Hugo, Vos Eline K, van der Flier W M, Sluimer Ingrid C, Cover Keith S, Knol Dirk L, Barkhof Frederik
Department of Radiology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
Hum Brain Mapp. 2014 Apr;35(4):1101-10. doi: 10.1002/hbm.22237. Epub 2013 Jan 30.
In many retrospective studies and large clinical trials, high-resolution, good-contrast 3DT1 images are unavailable, hampering detailed analysis of brain atrophy. Ventricular enlargement then provides a sensitive indirect measure of ongoing central brain atrophy. Validated automated methods are required that can reliably measure ventricular enlargement and are robust across magnetic resonance (MR) image types.
To validate the automated method VIENA for measuring the percentage ventricular volume change (PVVC) between two scans.
Accuracy was assessed using four image types, acquired in 15 elderly patients (five with Alzheimer's disease, five with mild cognitive impairment, and five cognitively normal elderly) and 58 patients with multiple sclerosis (MS), by comparing PVVC values from VIENA to manual outlining. Precision was assessed from data with three imaging time points per MS patient, by measuring the difference between the direct (one-step) and indirect (two-step) measurement of ventricular volume change between the first and last time points. The stringent concordance correlation coefficient (CCC) was used to quantify absolute agreement.
CCC of VIENA with manual measurement was 0.84, indicating good absolute agreement. The median absolute difference between two-step and one-step measurement with VIENA was 1.01%, while CCC was 0.98. Neither initial ventricular volume nor ventricular volume change affected performance of the method.
VIENA has good accuracy and good precision across four image types. VIENA therefore provides a useful fully automated method for measuring ventricular volume change in large datasets.
VIENA is a robust, accurate, and precise method for measuring ventricular volume change.
在许多回顾性研究和大型临床试验中,无法获得高分辨率、对比度良好的3D T1图像,这妨碍了对脑萎缩的详细分析。脑室扩大则提供了正在发生的中枢脑萎缩的敏感间接指标。需要经过验证的自动化方法,能够可靠地测量脑室扩大,并且在磁共振(MR)图像类型中具有稳健性。
验证用于测量两次扫描之间脑室体积变化百分比(PVVC)的自动化方法VIENA。
通过比较VIENA得出的PVVC值与手动勾勒结果,使用在15名老年患者(5名患有阿尔茨海默病、5名患有轻度认知障碍、5名认知正常的老年人)和58名多发性硬化症(MS)患者中采集的四种图像类型评估准确性。通过测量MS患者每个有三个成像时间点的数据中第一个和最后一个时间点之间脑室体积变化的直接(一步)测量和间接(两步)测量之间的差异来评估精密度。使用严格的一致性相关系数(CCC)来量化绝对一致性。
VIENA与手动测量的CCC为0.84,表明绝对一致性良好。VIENA的两步测量和一步测量之间的中位绝对差异为1.01%,而CCC为0.98。初始脑室体积和脑室体积变化均不影响该方法的性能。
VIENA在四种图像类型中具有良好的准确性和精密度。因此,VIENA为在大型数据集中测量脑室体积变化提供了一种有用的全自动方法。
VIENA是一种测量脑室体积变化的稳健、准确且精确的方法。