CRII: SCH: Analysis of Population-Based Image Metamorphosis

揭示大脑发育和疾病进展最终可以促进我们对大脑疾病的理解，如精神疾病和脑癌。然而，在脑扫描中对早期脑生长和病理解剖学变化的分析需要图像变形，该图像变形可以共同捕获在发育中的脑中发生的结构变化以及由脑髓鞘形成和肿瘤生长引入的外观变化。这个项目的目的是开发统计和计算模型，总结这些变形的变化，从图像对，序列和人口。通过丰富我们对健康大脑、精神病理学和大脑病理学的知识，这项研究有可能为制定大脑疾病的治疗策略提供线索。该项目的重点是开发基于人群的图像变形，同时解释大脑结构和图像外观的变化。所开发的模型的基本组成部分是一个先进的图像到图像的变形，允许建立图像对之间的大脑映射。本研究开发了一种新的变形回归模型来处理图像序列，并估计图像时间序列中与年龄相关的大脑变化。群体分析组件利用回归模型和统计工具对典型和非典型大脑变化进行分类和评估。所开发的图像变形模型的主要应用是早期脑发育和脑肿瘤生长的研究。由此产生的模型可以应用于了解发育障碍和精神疾病的病因，如自闭症，精神分裂症和双相情感障碍。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Uncovering brain development and disease progression can ultimately advance our understanding of brain disorders, such as mental illnesses and brain cancer. However, the analysis of early brain growth and pathological anatomy changes in brain scans needs image metamorphosis that can jointly capture structural changes occurring in a developing brain and appearance changes introduced by brain myelination and tumor growth. This project aims to develop statistical and computational models to summarize these metamorphic changes from image pairs, to sequences, and to populations. By enriching our knowledge of the healthy brain, psychopathologies, and brain pathologies, this research has the potential to provide clues in devising treatment strategies for brain disorders.The focus of this project is the development of population-based image metamorphosis that accounts for brain structure and image appearance changes simultaneously. The essential component of the developed model is an advanced image-to-image metamorphosis that allows establishing brain mappings between image pairs. This project develops a novel metamorphic regression model to handle image sequences and estimate age-related brain changes in image time-series. The population analysis component leverages the regression model and statistical tools for classification and assessment of typical and atypical brain changes. The primary applications of the developed image metamorphosis models are studies of early brain development and brain tumor growth. The resulting models could be applied to understand the etiology of developmental disorders and psychiatric conditions, such as autism, schizophrenia, and bipolar disease. They could also monitor brain tumor growth through predicting tumor evolution and classifying tumor type and grade.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.