Gao Yurui, Schilling Kurt G, Stepniewska Iwona, Xu Junzhong, Landman Bennett A, Dawant Benoit M, Anderson Adam W
Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235.
Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA 37235.
Proc SPIE Int Soc Opt Eng. 2018 Mar;10578. doi: 10.1117/12.2293879.
Clustering thalamic nuclei is important for both research and clinical purposes. For example, ventral intermediate nuclei in thalami serve as targets in both deep brain stimulation neurosurgery and radiosurgery for treating patients suffering from movement disorders (e.g., Parkinson's disease and essential tremor). Diffusion magnetic resonance imaging (dMRI) is able to reflect tissue microstructure in the central nervous system via fitting different models, such as, the diffusion tensor (DT), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI), diffusion kurtosis imaging (DKI) and the spherical mean technique (SMT).
To test which of the above-mentioned dMRI models is better for thalamic parcellation, we proposed a framework of -means clustering, implemented it on each model, and evaluated the agreement with histology.
An ex vivo monkey brain was scanned in a 9.4T MRI scanner at 0.3mm resolution with b values of 3000, 6000, 9000 and 12000 s/mm. -means clustering on each thalamus was implemented using maps of dMRI models fitted to the same data. Meanwhile, histological nuclei were identified by AChE and Nissl stains of the same brain. Overall agreement rate and agreement rate for each nucleus were calculated between clustering and histology. Sixteen thalamic nuclei on each hemisphere were included.
Clustering with the DKI model has slightly higher overall agreement rate but clustering with other dMRI models result in higher agreement rate in some nuclei.
dMRl models should be carefully selected to better parcellate the thalamus, depending on the specific purpose of the parcellation.
丘脑核团聚类对于研究和临床目的均具有重要意义。例如，丘脑腹中间核是深部脑刺激神经外科手术和放射外科手术治疗运动障碍患者（如帕金森病和特发性震颤）的靶点。扩散磁共振成像（dMRI）能够通过拟合不同模型来反映中枢神经系统的组织微观结构，这些模型包括扩散张量（DT）、约束球形反卷积（CSD）、神经突方向分散与密度成像（NODDI）、扩散峰度成像（DKI）和球形均值技术（SMT）。
为了测试上述哪种dMRI模型更适合丘脑分区，我们提出了一种K均值聚类框架，在每个模型上进行实施，并评估与组织学的一致性。
在一台9.4T MRI扫描仪中，以0.3mm分辨率对一个离体猴脑进行扫描，b值分别为3000、6000、9000和12000 s/mm²。使用拟合相同数据的dMRI模型图谱，对每个丘脑进行K均值聚类。同时，通过对同一脑进行乙酰胆碱酯酶（AChE）和尼氏染色来识别组织学核团。计算聚类结果与组织学之间的总体一致率以及每个核团的一致率。每个半球纳入16个丘脑核团。
使用DKI模型进行聚类的总体一致率略高，但使用其他dMRI模型进行聚类在某些核团中一致率更高。
应根据分区的具体目的，仔细选择dMRI模型以更好地对丘脑进行分区。
