A Novel Informatics System for Craniosynostosis Surgery

颅缝早闭手术的新型信息学系统

• 批准号: 10199743
• 负责人: Xiaobo Zhou
• 金额: $ 37.57万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10199743
• 关键词: 3-Dimensional; Accounting; Affect; Anterior; Attention; Baptist Church; Behavior; Biomechanics; Bone Tissue; Brain; Calvaria; Catalogs; Cephalic; Characteristics; Child; Classification; Clinical; Clinical Data; Complex; Congenital Abnormality; Congenital abnormal Synostosis; Craniosynostosis; Data; Databases; Defect; Deformity; Development; Diagnosis; Disease; Elements; Family; Goals; Growth; Head; Hospitals; Imaging Device; Impaired cognition; Individual; Infant; Informatics; Joint structure of suture of skull; Life; Live Birth; Machine Learning; Methods; Modeling; Operative Surgical Procedures; Patients; Prevalence; Property; Regression Analysis; Scanning; Shapes; Surface; Surgeon; Surgical sutures; System; Testing; Thick; Training; X-Ray Computed Tomography; base; bone; bone aging; clinical Diagnosis; cranium; experience; feature extraction; feature selection; forest; image processing; imaging informatics; improved; indexing; innovation; novel; novel strategies; occipital bone; open source; premature; segmentation algorithm; statistical learning; tool; vector; virtual

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.

项目摘要 颅缝早闭（CranioSynostosis，CSO）是指连接颅骨的一条或多条颅缝过早融合 给孩子们的骨头CSO的估计患病率为每2500名活产婴儿中有一人，甚至更高。我们的最终目标 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）数据进行颅缝早闭，并准确估计患者特定的弹簧力 弹簧辅助手术（SAS） CSO是一种非常严重的出生缺陷，涉及过早融合，一个或多个缝线上的一个或多个缝线， 宝宝的头骨CSO根据融合缝的不同可分为几种类型， 例如矢状面、冠状面、额颞面和颞下颌关节面。患有CSO的婴儿可能有大脑和头骨的问题 生长，导致认知障碍。这一缺陷不仅会毁了婴儿的一生， 婴儿的家人。SAS被公认为是一种安全、有效和微创的治疗方法， 治疗CSO。这种治疗利用弹簧的力量以较慢的方式重塑头骨， 头骨的生长有助于形状的改变。患者特定的弹簧选择是主要障碍， SAS用于CSO的进步，因为很少有外科医生有选择个性化弹簧的经验 每名患者。弹簧力的选择是这种手术治疗中的关键步骤，它依赖于 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。一个例子是矢状CSO与延长枕骨 需要一个更强的后弹簧，而一个没有主要特征的人通常需要一个中等的后弹簧。 前后弹簧。目前的问题是，我们没有一个完全客观的方法， CSO和矢状CSO的分类以及个体弹簧力的估计。 我们的假设是，CSO和矢状CSO可以准确地分类的基础上，从缝合的特点 和头部形状，以及颅骨组织遵循虚拟最佳弹簧力的行为， 通过将有限元方法（FEM）与统计学习模型相结合来模拟。为了验证我们的假设， 我们提出了以下具体目标：（1）定义eSuture信息系统并构建 （2）开发用于图像处理、分割、配准 量化缝线，并自动将每名患者分类为一个CSO类型目录，或 矢状面CSO亚型;（3）建模和估计SAS的最佳弹簧力;（4）验证和 评价eSuture系统。我们的系统将在CSO诊断和治疗方面产生范式转变。