Predictive Statistical Model of Early Cranial Development.
Predictive Statistical Model of Early Cranial Development.
复制标题
DOI：
10.1109/tbme.2021.3100745
复制
发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Linguraru MG
中科院分区：
文献类型：
--
作者：
PorrasPerez AR;Keating R;Lee J;Linguraru MG
我们提出了一种数据驱动的方法来建立一个时空统计形状模型，预测从出生到2岁的正常颅骨生长。该模型是使用278名受试者的标准横断面计算机断层扫描图像数据集构建的。首先，我们提出了一个新的标准表示的颅骨使用球面地图，以建立解剖学之间的对应关系，受试者在颅缝-颅骨扩张的主要领域。然后，我们将颅骨形状建模为两个因素的双线性函数：受试者间解剖变异性和时间生长。我们估计这些因素使用主成分分析的空间和时间维度，使用一种新的粗到细的时间多分辨率方法，以减轻缺乏纵向图像的同一患者。我们的模型在一个独立的纵向数据集上预测发育的准确度为1.54 ± 1.05 mm。我们还使用该模型计算了头体积，头指数和颅骨表面的变化，在头两年的年龄，这与临床观察。据我们所知，这是婴儿期颅骨形状发育的第一个数据驱动和个性化预测模型，它可以作为研究人群异常生长模式的基线。
We present a data-driven method to build a spatiotemporal statistical shape model predictive of normal cranial growth from birth to the age of 2 years. The model was constructed using a normative cross-sectional computed tomography image dataset of 278 subjects. First, we propose a new standard representation of the calvaria using spherical maps to establish anatomical correspondences between subjects at the cranial sutures – the main areas of cranial bone expansion. Then, we model the cranial bone shape as a bilinear function of two factors: inter-subject anatomical variability and temporal growth. We estimate these factors using principal component analysis on the spatial and temporal dimensions, using a novel coarse-to-fine temporal multi-resolution approach to mitigate the lack of longitudinal images of the same patient. Our model achieved an accuracy of 1.54 ± 1.05 mm predicting development on an independent longitudinal dataset. We also used the model to calculate the cranial volume, cephalic index and cranial bone surface changes during the first two years of age, which were in agreement with clinical observations. To our knowledge, this is the first data-driven and personalized predictive model of cranial bone shape development during infancy and it can serve as a baseline to study abnormal growth patterns in the population.