Quantitative Image Analysis Techniques for Optic Nerve Disease

视神经疾病的定量图像分析技术

• 批准号: 8620598
• 负责人: Bennett A. Landman
• 金额: $ 22.51万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-12-01 至 2015-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8620598
• 关键词: Accounting; Acute; Address; Adrenal Cortex Hormones; Affect; Aftercare; Age; Algorithms; American; Area; Axon; Biological Markers; Blindness; Brain; Clinical; Clinical Research; Clinical Trials; Clinical assessments; Communities; Data; Defect; Demyelinations; Diagnostic; Disease; Edema; Eye; Foundations; Gap Junctions; Glaucoma; Goals; Health Care Costs; Image; Image Analysis; Individual; Inflammation; Inflammatory; Interferons; Intervention; Intracranial Hypertension; Lead; Lesion; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Medical Imaging; Methods; Metric; Modality; Multiple Sclerosis; Myelin; Nerve Tissue; Neurologic; Nutritional; Operative Surgical Procedures; Optic Disk; Optic Nerve; Optic Nerve Injuries; Optic Neuritis; Outcome; Patient Care; Patients; Phase; Phenotype; Procedures; Prognostic Marker; Property; Protective Agents; Public Health; Publishing; Recovery; Recurrence; Relapse; Research; Resource Sharing; Resources; Scanning; Sclerosis; Shapes; Signal Transduction; Source; Staging; Swelling; Symptoms; Tars; Techniques; Thyroid Diseases; Time; Training; Translating; Treatment outcome; Tweens; Validation; Visual; Work; X-Ray Computed Tomography; base; clinical care; clinical practice; computerized tools; cost; direct application; experience; high reward; high risk; image processing; imaging modality; improved; innovation; loss of function; nerve decompression; neuroimaging; optic nerve disorder; outcome forecast; pressure; prevent; prognostic; public health relevance; response; standard of care; success; thyroid associated ophthalmopathies; tool; treatment response; vector

PROJECT SUMMARY/ABSTRACT Disorders of the optic nerve (ON) account for a significant percentage of the 20 most impactful ophthalmological conditions. Collectively, diseases of the ON are the number one cause of irreversible blindness worldwide, and present serious public health concerns in the U.S. Consider, for example, that glaucoma impacts more than three million Ameri- cans and costs the U.S. economy almost $3 billion per year. Optic neuritis (i.e., inflammatory demyelination of the ON) is the initial symptom in ~25% of all multiple sclerosis (MS) cases (which impacts over 400 thousand Americans and intro- duces societal health care costs of nearly $30 billion per year). Nearly two thirds of MS patients will experience episodes of optic neuritis in their lifetimes, and 40-60% of patients have visual defects localized to the ON. These disorders irre- versibly damage the ON. Even so, damage to axons in the ON is progressive, defined by a window of opportunity for treatment between loss of function and actual degeneration. The potential for recovery exists because there are treatments that can help prevent progression if administered during this window of opportunity. Yet, we do not have effective means to assess who is in the window and who will benefit from treatment. We propose to translate computational imaging methods from the neuroimaging community to provide ro- bust, quantitative tools for assessing the optic nerve (ON) on clinical and research imaging sequences. These efforts will improve prognostic accuracy, lead to better understanding of patient responses, and enhance targeted interven- tions. The technical hypothesis of this work is that quantitative image processing can robustly and accurately segment, register, and fuse ON data from modern MRI and CT clinical sequences. The central hypothesis of this proposal is that qualitative ON phenotypes on longitudinal clinical imaging will differentiate individuals who respond to treatment versus those who do not. The overall goal of this research is to provide a foundation for image analysis of the ON and its relationships with pathological disorders. We will build upon recent advances in robust medical image computing to segment the ON in clinical CT and MRI acquisitions, develop registration procedures to establish intra- and inter-subject correspondence, and bring together information from the multi-modal battery of imaging studies that are typically used in clinical care (aim 1). With these new methods, we will address the exploratory hypothesis that quantitative use of clinical imaging data can increase prognostic accuracy (aim 2). We note that aim 2 is particularly exploratory and in line with the high- risk/high-reward aspect of this mechanism; many studies have shown that baseline imaging does not conclusively pre- dict long term outcome or treatment response. We hypothesize that this may be because early findings are related to edema and inflammation rather than cellular damage per se. Once this exploratory phase is complete, we will pursue promising prognostic biomarkers using more detailed condition staging criteria and including more than two longitudinal time points in the analysis. Ultimately, these efforts will improve assessment ON disease and, in turn, patient care.

项目概要/摘要 视神经 (ON) 疾病在 20 种最具影响力的眼科疾病中占很大比例 状况。总的来说，视神经疾病是全世界不可逆性失明的头号原因，目前 例如，青光眼影响了超过 300 万美国人 每年给美国经济造成近 30 亿美元的损失。视神经炎（即视神经炎性脱髓鞘） 约 25% 的多发性硬化症 (MS) 病例（影响超过 40 万美国人）的初始症状 每年造成近 300 亿美元的社会医疗保健费用）。近三分之二的多发性硬化症患者会经历发作 一生中最常见的视神经炎患者，40-60% 的患者存在局限于视神经眼的视觉缺陷。这些疾病不 明显损坏ON。即便如此，ON 中轴突的损伤是进行性的，由机会窗口定义 功能丧失和实际退化之间的治疗。存在恢复潜力是因为 如果在此机会之窗内进行治疗，可以帮助预防进展。然而，我们还没有 评估谁在窗口期以及谁将从治疗中受益的有效手段。 我们建议将神经影像学界的计算成像方法转化为机器人 半身像，用于评估临床和研究成像序列上的视神经 (ON) 的定量工具。这些努力 将提高预后准确性，更好地了解患者的反应，并增强有针对性的干预措施 系统蒸发散。这项工作的技术假设是定量图像处理可以稳健且准确地分割， 注册并融合现代 MRI 和 CT 临床序列的数据。该提案的中心假设是 纵向临床成像的定性 ON 表型将区分对治疗有反应的个体与 那些不这样做的人。 本研究的总体目标是为 ON 及其关系的图像分析提供基础 患有病理性疾病。我们将利用强大的医学图像计算的最新进展来细分 ON 在临床 CT 和 MRI 采集中，制定注册程序以建立受试者内和受试者间的对应关系， 汇集来自临床护理中通常使用的多模式成像研究的信息 （目标1）。通过这些新方法，我们将解决以下探索性假设：临床成像的定量使用 数据可以提高预测准确性（目标 2）。我们注意到目标 2 特别具有探索性，并且符合高目标 该机制的风险/高回报方面；许多研究表明，基线成像并不能最终预测 决定长期结果或治疗反应。我们假设这可能是因为早期发现与 水肿和炎症而不是细胞损伤本身。一旦这个探索阶段完成，我们将追求 有前途的预后生物标志物使用更详细的病情分期标准，并包括两个以上的纵向 分析中的时间点。最终，这些努力将改善疾病评估，进而改善患者护理。