Section of Geriatric Psychiatry and Institute of Gerontology, Department of Psychiatry, Heidelberg University, Germany.
Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany.
Psychiatry Res. 2015 Sep 30;233(3):299-305. doi: 10.1016/j.pscychresns.2015.07.011. Epub 2015 Jul 17.
Magnetic resonance imaging (MRI) and brain volumetry allow for the quantification of changes in brain volume using automatic algorithms which are widely used in both, clinical and scientific studies. However, studies comparing the reliability of these programmes are scarce and mainly involved MRI derived from younger healthy controls. This study evaluates the reliability of frequently used segmentation programmes (SPM, FreeSurfer, FSL) using a realistic digital brain phantom and MRI brain acquisitions from patients with manifest Alzheimer's disease (AD, n=34), mild cognitive impairment (MCI, n=60), and healthy subjects (n=32) matched for age and sex. Analysis of the brain phantom dataset demonstrated that SPM, FSL and FreeSurfer underestimate grey matter and overestimate white matter volumes with increasing noise. FreeSurfer calculated overall smaller brain volumes with increasing noise. Image inhomogeneity had only minor, non- significant effects on the results obtained with SPM and FreeSurfer 5.1, but had effects on the FSL results (increased white matter volumes with decreased grey matter volumes). The analysis of the patient data yielded decreasing volumes of grey and white matter with progression of brain atrophy independent of the method used. FreeSurfer calculated the largest grey matter and the smallest white matter volumes. FSL calculated the smallest grey matter volumes; SPM the largest white matter volumes. Best results are obtained with good image quality. With poor image quality, especially noise, SPM provides the best segmentation results. An optimised template for segmentation had no significant effect on segmentation results. While our findings underline the applicability of the programmes investigated, SPM may be the programme of choice when MRIs with limited image quality or brain images of elderly should be analysed.
磁共振成像（MRI）和脑容量测量可使用自动算法来量化脑容量的变化，这些算法在临床和科学研究中都得到了广泛应用。然而，比较这些程序可靠性的研究很少，且主要涉及来自年轻健康对照者的 MRI。本研究使用逼真的数字脑模型和来自表现出阿尔茨海默病（AD，n=34）、轻度认知障碍（MCI，n=60）和年龄与性别匹配的健康受试者（n=32）的 MRI 脑采集，评估了常用分割程序（SPM、FreeSurfer、FSL）的可靠性。对脑模型数据集的分析表明，SPM、FSL 和 FreeSurfer 随着噪声的增加低估灰质并高估白质体积。随着噪声的增加，FreeSurfer 计算的总脑体积更小。图像不均匀性对 SPM 和 FreeSurfer 5.1 获得的结果只有较小的、无统计学意义的影响，但对 FSL 结果有影响（白质体积增加，灰质体积减少）。对患者数据的分析表明，随着脑萎缩的进展，灰质和白质的体积减少与使用的方法无关。FreeSurfer 计算的灰质体积最大，白质体积最小。FSL 计算的灰质体积最小；SPM 计算的白质体积最大。获得最佳结果需要良好的图像质量。图像质量较差，尤其是噪声较大时，SPM 提供最佳的分割结果。优化的分割模板对分割结果没有显著影响。虽然我们的研究结果强调了所研究程序的适用性，但当需要分析图像质量有限的 MRI 或老年患者的脑图像时，SPM 可能是首选程序。
