Electrical Engineering, Stanford University, Stanford, CA, USA.
Electrical & Computer Engineering, University of Arizona, Tucson, AZ, USA.
Neuroimage. 2019 Jul 1;194:272-282. doi: 10.1016/j.neuroimage.2019.03.021. Epub 2019 Mar 17.
The thalamus and its nuclei are largely indistinguishable on standard T1 or T2 weighted MRI. While diffusion tensor imaging based methods have been proposed to segment the thalamic nuclei based on the angular orientation of the principal diffusion tensor, these are based on echo planar imaging which is inherently limited in spatial resolution and suffers from distortion. We present a multi-atlas segmentation technique based on white-matter-nulled MP-RAGE imaging that segments the thalamus into 12 nuclei with computation times on the order of 10 min on a desktop PC; we call this method THOMAS (THalamus Optimized Multi Atlas Segmentation). THOMAS was rigorously evaluated on 7T MRI data acquired from healthy volunteers and patients with multiple sclerosis by comparing against manual segmentations delineated by a neuroradiologist, guided by the Morel atlas. Segmentation accuracy was very high, with uniformly high Dice indices: at least 0.85 for large nuclei like the pulvinar and mediodorsal nuclei and at least 0.7 even for small structures such as the habenular, centromedian, and lateral and medial geniculate nuclei. Volume similarity indices ranged from 0.82 for the smaller nuclei to 0.97 for the larger nuclei. Volumetry revealed that the volumes of the right anteroventral, right ventral posterior lateral, and both right and left pulvinar nuclei were significantly lower in MS patients compared to controls, after adjusting for age, sex and intracranial volume. Lastly, we evaluated the potential of this method for targeting the Vim nucleus for deep brain surgery and focused ultrasound thalamotomy by overlaying the Vim nucleus segmented from pre-operative data on post-operative data. The locations of the ablated region and active DBS contact corresponded well with the segmented Vim nucleus. Our fast, direct structural MRI based segmentation method opens the door for MRI guided intra-operative procedures like thalamotomy and asleep DBS electrode placement as well as for accurate quantification of thalamic nuclear volumes to follow progression of neurological disorders.
在标准 T1 或 T2 加权 MRI 上,丘脑及其核团很难区分。虽然已经提出了基于扩散张量成像的方法来根据主扩散张量的角向方位对丘脑核团进行分割,但这些方法基于平面回波成像,其固有空间分辨率有限,并存在失真。我们提出了一种基于白质消除 MP-RAGE 成像的多图谱分割技术,可将丘脑分割为 12 个核团,在台式 PC 上的计算时间约为 10 分钟;我们称这种方法为 THOMAS(丘脑优化多图谱分割)。THOMAS 在健康志愿者和多发性硬化症患者的 7T MRI 数据上进行了严格评估,通过与神经放射科医生根据莫雷尔图谱手动分割进行比较,评估了其准确性。分割精度非常高,Dice 指数均很高:对于较大的核团,如丘脑枕和背内侧核,至少为 0.85;对于较小的结构,如缰核、中央中核和外侧和内侧膝状体核,至少为 0.7。体积相似性指数范围为 0.82(较小的核团)至 0.97(较大的核团)。体积测量结果表明,调整年龄、性别和颅内体积后,MS 患者右侧前腹侧核、右侧腹后外侧核以及双侧丘脑枕核的体积明显低于对照组。最后,我们通过将术前数据中分割的 Vim 核团叠加到术后数据上来评估该方法用于深部脑手术和聚焦超声丘脑切开术的靶向 Vim 核团的潜力。消融区域和有源 DBS 接触的位置与分割的 Vim 核团非常吻合。我们的快速、直接的基于结构 MRI 的分割方法为 MRI 引导的手术程序(如丘脑切开术和睡眠 DBS 电极放置)以及准确量化丘脑核团体积以跟踪神经退行性疾病的进展打开了大门。