Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis.
Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis.
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DOI：
10.1038/s41598-023-47622-7
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发表时间：
2023-11-23
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
--
作者：
我们提出了第一个数据驱动的儿科模型，解释了儿科人群的颅缝生长。我们从2068名正常受试者（年龄0-10岁）的横截面CT图像中分割脑颅中的颅骨，并且我们使用基于2D流形的颅骨表示来建立受试者之间的局部解剖学对应关系，由颅缝的位置指导。我们设计了一个颅骨发育的局部缝生长率的函数的同构时空模型，并从我们的横截面数据集统计推断其参数。我们使用构建的模型来预测51名独立的正常患者的纵向图像的增长。此外，我们使用我们的模型来模拟单缝颅缝早闭的表型，我们比较了212例患者的观察结果。我们还评估了10例有术前纵向图像的颅缝早闭患者个性化颅骨生长预测的准确性。与现有的统计和模拟方法不同，我们的模型是从真实的图像观察中推断出来的，解释了颅骨膨胀和移位是缝生长的结果，它可以模拟颅缝早闭。这个小儿颅缝生长模型构成了一个必要的工具，研究颅缝病理存在的异常发展。
We present the first data-driven pediatric model that explains cranial sutural growth in the pediatric population. We segmented the cranial bones in the neurocranium from the cross-sectional CT images of 2068 normative subjects (age 0–10 years), and we used a 2D manifold-based cranial representation to establish local anatomical correspondences between subjects guided by the location of the cranial sutures. We designed a diffeomorphic spatiotemporal model of cranial bone development as a function of local sutural growth rates, and we inferred its parameters statistically from our cross-sectional dataset. We used the constructed model to predict growth for 51 independent normative patients who had longitudinal images. Moreover, we used our model to simulate the phenotypes of single suture craniosynostosis, which we compared to the observations from 212 patients. We also evaluated the accuracy predicting personalized cranial growth for 10 patients with craniosynostosis who had pre-surgical longitudinal images. Unlike existing statistical and simulation methods, our model was inferred from real image observations, explains cranial bone expansion and displacement as a consequence of sutural growth and it can simulate craniosynostosis. This pediatric cranial suture growth model constitutes a necessary tool to study abnormal development in the presence of cranial suture pathology.
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影响因子：
10.6
作者：
Porras AR;Paniagua B;Ensel S;Keating R;Rogers GF;Enquobahrie A;Linguraru MG
通讯作者：
Linguraru MG
影响因子：
3.6
作者：
Fong, KD;Warren, SM;Longaker, MT
通讯作者：
Longaker, MT
影响因子：
0.7
作者：
Maugans, TA;McComb, JG;Levy, ML
通讯作者：
Levy, ML
影响因子：
3.1
作者：
Boyadjiev, S. A.
通讯作者：
Boyadjiev, S. A.
影响因子：
10.9
作者：
Arsigny, V;Pennec, X;Ayache, N
通讯作者：
Ayache, N