Conformal Geometry for Medical Imaging

医学成像的共形几何

• 批准号: 7583787
• 负责人: ARIE E KAUFMAN
• 金额: $ 37.56万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7583787
• 关键词: Abbreviations; Alzheimer' s Disease; Bladder; Blood Vessels; Brain; Brain imaging; Case Study; Cities; Clinical; Clinical Research; Colon; Colon Carcinoma; Colonic Polyps; Computational algorithm; Computed Tomographic Colonography; Computer Assisted; Data; Data Set; Detection; Development; Diagnosis; Disease Progression; Doctor of Philosophy; Drug Addiction; Evaluation; Funding; Health; Healthcare; Human; Image; Imaging Techniques; Maps; Measures; Medial; Medical Imaging; Methodology; Methods; Modality; Movement; Operative Surgical Procedures; Organ; Patient Participation; Polyps; Procedures; Public Health; Radiology Specialty; Research Design; Screening for cancer; Screening procedure; Shapes; Solutions; Structure; Surface; Techniques; Universities; Validation; Waxes; base; clinical application; computer science; improved; novel; performance site; programs; tool; treatment planning; tumor

It is paramount in medical imaging to measure, compare, calibrate, register and analyze potentially deformed organ shapes with high accuracy and fidelity. However, this is extremely difficult due to the complicated shape of human organs. Different organs have different topologies and curvature distributions, and furthermore, the shape may deform due to disease progression, movement, imaging, surgery and treatment. We propose to use conformal geometry, a theoretically rigorous and practically efficient and robust method, to tack this challenge. The broad, long-term objective of this project is to develop conformal geometry as a primary tool in the vast biomedical applications of medial imaging. Conformal structure is a natural structure, ideally suited to study shape matching and deformation. A powerful tool, Ricci flow, can be used to compute conformal geometry. It has been applied recently in the proof of the Poincaré conjecture. We have developed practical computational algorithms to compute Ricci flow, obtained promising preliminary results, and plan to apply it with other conformal geometric methods in a variety of clinical case-studies for the colon and brain. The health relatedness of the project is to dramatically improve medical imaging techniques for clinical applications, thereby improving the diagnosis, procedure planning, treatment, follow-ups and clinical research. Consequently, health care will be substantially improved, as well as patients’ participation in screening programs will be noticeably increased. The specific aims of this project are to develop: (1) conformal surface flattening; (2) conformal mapping for volumetric parameterization; and (3) registration and fusion using conformal mapping. The research design and methodology will include developing and validating techniques to conformally flatten 3D organ surfaces to canonical parametric surfaces for colonic polyp detection. We will further extend flattening to implement volumetric parameterization based on Ricci flow and then apply it to brain and colon structure segmentation, and tumor evaluation. In addition, we will implement shape registration and data fusion using a common canonical parameter domain. Brain data sets will be fused between and within subjects and modalities, as well as colon supine and prone will be registered for improved cancer screening. PERFORMANCE SITE(S) (organization, city, state) Departments of Computer Science and Radiology Stony Brook University (SUNY at Stony Brook) Stony Brook, NY 11794-4400 Organization abbreviation:

在医学成像中，以高精度和保真度测量、比较、校准、配准和分析潜在变形的器官形状是至关重要的。然而，由于人体器官的复杂形状，这是非常困难的。不同的器官具有不同的拓扑结构和曲率分布，此外，形状可能由于疾病进展、运动、成像、手术和治疗而变形。我们建议使用共形几何，一个理论上严格和实际有效的和强大的方法，来应对这一挑战。这个项目的广泛的，长期的目标是开发适形几何作为医学成像的广泛的生物医学应用的主要工具。共形结构是一种自然结构，非常适合研究形状匹配和变形。一个强大的工具，里奇流，可以用来计算共形几何。它最近被应用于证明庞加莱猜想。我们已经开发出实用的计算算法来计算Ricci流，获得了有希望的初步结果，并计划将其与其他适形几何方法应用于各种临床病例研究的结肠和大脑。该项目的健康相关性是显著改善临床应用的医学成像技术，从而改善诊断、程序规划、治疗、随访和临床研究。因此，医疗保健将得到实质性改善，患者对筛查计划的参与将显著增加。该项目的具体目标是：（1）共形表面展平;（2）用于体积参数化的共形映射;和（3）使用共形映射的配准和融合。研究设计和方法学将包括开发和验证技术，以将3D器官表面共形平坦化为用于结肠息肉检测的规范参数化表面。我们将进一步扩展平坦化以实现基于Ricci流的体积参数化，然后将其应用于脑和结肠结构分割以及肿瘤评估。此外，我们将实现形状注册和数据融合使用一个共同的规范参数域。大脑数据集将在受试者之间和受试者内部进行融合，并且将登记仰卧和俯卧结肠，以改善癌症筛查。绩效地点（组织、城市、州）斯托尼布鲁克大学计算机科学和放射学系（纽约州立大学，斯托尼布鲁克），纽约州斯托尼布鲁克，邮编11794-4400组织简称：