Department of Physics, University of Turin, Turin, Italy.
Medical Physics Department, Ospedale Santa Maria della Misericordia, Piazzale Giorgio Menghini 1, 06129, Perugia, Italy.
Brain Struct Funct. 2021 Jan;226(1):137-150. doi: 10.1007/s00429-020-02172-w. Epub 2020 Nov 24.
Accurate and reproducible automated segmentation of human hippocampal subfields is of interest to study their roles in cognitive functions and disease processes. Multispectral structural MRI methods have been proposed to improve automated hippocampal subfield segmentation accuracy, but the reproducibility in a multicentric setting is, to date, not well characterized. Here, we assessed test-retest reproducibility of FreeSurfer 6.0 hippocampal subfield segmentations using multispectral MRI analysis pipelines (22 healthy subjects scanned twice, a week apart, at four 3T MRI sites). The harmonized MRI protocol included two 3D-T1, a 3D-FLAIR, and a high-resolution 2D-T2. After within-session T1 averaging, subfield volumes were segmented using three pipelines with different multispectral data: two longitudinal ("long_T1s" and "long_T1s_FLAIR") and one cross-sectional ("long_T1s_FLAIR_crossT2"). Volume reproducibility was quantified in magnitude (reproducibility error-RE) and space (DICE coefficient). RE was lower in all hippocampal subfields, except for hippocampal fissure, using the longitudinal pipelines compared to long_T1s_FLAIR_crossT2 (average RE reduction of 0.4-3.6%). Similarly, the longitudinal pipelines showed a higher spatial reproducibility (1.1-7.8% of DICE improvement) in all hippocampal structures compared to long_T1s_FLAIR_crossT2. Moreover, long_T1s_FLAIR provided a small but significant RE improvement in comparison to long_T1s (p = 0.015), whereas no significant DICE differences were found. In addition, structures with volumes larger than 200 mm had better RE (1-2%) and DICE (0.7-0.95) than smaller structures. In summary, our study suggests that the most reproducible hippocampal subfield FreeSurfer segmentations are derived from a longitudinal pipeline using 3D-T1s and 3D-FLAIR. Adapting a longitudinal pipeline to include high-resolution 2D-T2 may lead to further improvements.
准确且可重现的人类海马亚区自动分割对于研究其在认知功能和疾病过程中的作用具有重要意义。多光谱结构 MRI 方法已被提出用于提高自动海马亚区分割的准确性，但在多中心环境中的可重复性至今尚未得到很好的描述。在这里，我们使用多光谱 MRI 分析管道评估了 FreeSurfer 6.0 海马亚区分割的测试-重测可重复性（22 名健康受试者两次扫描，相隔一周，在四个 3T MRI 站点进行）。协调的 MRI 方案包括两个 3D-T1、一个 3D-FLAIR 和一个高分辨率 2D-T2。在会话内 T1 平均后，使用具有不同多光谱数据的三个管道分割亚区体积：两个纵向（"long_T1s"和"long_T1s_FLAIR"）和一个横向（"long_T1s_FLAIR_crossT2"）。体积重现性在幅度（重现误差-RE）和空间（DICE 系数）上进行量化。与 long_T1s_FLAIR_crossT2 相比，所有海马亚区（除了海马裂）的纵向管道的 RE 较低（平均 RE 降低 0.4-3.6%）。同样，与 long_T1s_FLAIR_crossT2 相比，纵向管道在所有海马结构中具有更高的空间重现性（DICE 提高 1.1-7.8%）。此外，与 long_T1s 相比，long_T1s_FLAIR 提供了一个小但显著的 RE 改善（p = 0.015），而没有发现 DICE 差异。此外，体积大于 200mm 的结构具有更好的 RE（1-2%）和 DICE（0.7-0.95），而体积较小的结构则较差。总之，我们的研究表明，最可重复的海马亚区 FreeSurfer 分割来自于使用 3D-T1s 和 3D-FLAIR 的纵向管道。通过适应纵向管道以包含高分辨率 2D-T2 可能会进一步提高。
