A Novel Informatics System for Craniosynostosis Surgery

项目摘要 颅缝融合(CSO)是指连接单个头骨的一条或多条颅缝过早融合。 给孩子们的骨头。据估计，CSO的患病率为每2500名活产儿中就有一名，甚至更高。我们的终极目标 是开发一个开源的成像信息学平台eSuture，供临床医生客观地对 使用计算机断层扫描(CT)数据进行颅骨融合，并准确估计患者特定的弹簧力 用于弹簧辅助手术(SAS)。 CSO是一种极其严重的出生缺陷，涉及一条或多条缝合线的过早融合 婴儿的头骨。根据融合缝线，CSO可以主要分为几种类型的融合， 如矢状面、冠状面、超视面和板状面。患有CSO的婴儿可能有脑和头骨问题 发育，导致认知障碍。这一缺陷不仅可能毁掉婴儿的生活，而且还会深深地影响 婴儿的家人。SAS被认为是一种安全、有效和侵入性较小的治疗方法，被引入到 治疗CSO。这种疗法利用弹簧的力量以一种较慢的方式重塑头骨，从而利用 头骨的生长有助于形状的改变。针对患者的春季选择是实现 因为很少有外科医生有选择个性化弹簧的经验，所以SAS在CSO中的进步 对每个病人来说。弹簧力的选择是手术治疗中的关键一步，它依赖于 依靠外科医生的经验。选择弹簧力的重要因素包括 年龄、骨厚度和CSO亚型。一个例子是枕骨拉长的矢状面CSO 需要更强的后弹力，而没有明显特征的通常需要中档弹力 前弹簧和后弹簧。目前的问题是，我们没有一个完整的、客观的方法 CSO和矢状位CSO的分类和个体弹力的估计。 我们的假设是，根据缝合的特征可以准确地将CSO和矢状位CSO进行分类 和头部形状，以及在虚拟最佳弹力作用下的颅骨组织行为 通过将有限元方法与统计学习模型相结合进行模拟。为了检验我们的假设， 我们提出以下具体目标：(1)定义电子缝合信息系统，建立电子缝合信息系统 数据库，包括CT和DTI数据；(2)开发图像处理、分割、配准、 量化缝合，并自动将每个患者分类到一个CSO类型目录或 矢状面CSO亚型；(3)模拟和估计SAS的最佳弹簧力；以及(4)验证和 评估电子缝合系统。我们的系统将在CSO诊断和治疗方面产生范式转变。

Project Summary CranioSynOstosis (CSO) is the premature fusion of one or more of cranial sutures that connect individual skull bones for kids. The estimated prevalence of CSO is one in 2500 live births and even higher. Our ultimate goal is to develop an open-source imaging-informatics-platform, eSuture, for clinicians to objectively classify the craniosynostosis using computed tomography (CT) data, and accurately estimate patient-specific spring force for spring-assisted surgery (SAS). CSO is an extremely serious birth defect that involves the premature fusion, of one or more sutures on a baby's skull. CSO, in terms of the fused suture, could be classified mainly into several types of synostosis, such as sagittal, coronal, metopic, and lambdoid. Infants with CSO may have problems with brain and skull growth, resulting in cognitive impairment. This defect may not only ruin the infant's life, but also deeply affect the infant's family. SAS, recognized as a safe, effective, and less invasive treatment method, introduced to treat the CSO. This treatment uses the force of a spring to reshape the skull in a slower manner that harnesses the growth of the skull to assist with shape change. Patient-specific spring selection is the principal barrier to the advancement of SAS for CSO because few surgeons have the experience to select personalized springs for each patient. The selection of the spring force is a crucial step in this surgical treatment, and it is dependent on the experience of the surgeon. Important factors essential in the selection of the spring force include the ages, bone thickness and the subtypes of CSO. One example is that sagittal CSO with an elongated occiput needs a stronger posterior spring, while one with no predominant characteristics typically needs a mid-range anterior and posterior spring. The current problem is that we do not have a complete objective way of classification of CSO and sagittal CSO and estimation of the spring force for the individual. Our hypothesis is that CSO and sagittal CSO can be accurately classified based on the features from sutures and head shape, and behaviors of calvarial bone tissue following virtual optimal spring force can be accurately simulated by integrating a finite element method (FEM) with statistical learning model. To test our hypothesis, we are proposing the following Specific Aim: (1) To define the eSuture Informatic system and build the database, with CT and DTI data; (2) To develop tools for image processing, segmentation, registration, quantification of sutures, and automatically categorize each patient to one catalogue of the CSO types or sagittal CSO subtype; (3) To model and estimate the optimal spring force for SAS; and (4) To validate and evaluate the eSuture system. Our system will produce a paradigm shift in CSO diagnosis and treatment.