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.

项目总结/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。 条件总的来说，ON的疾病是全球不可逆失明的头号原因， 美国严重的公共卫生问题。例如，青光眼影响着300多万美国人， 美国经济每年损失近30亿美元。视神经炎（即，ON的炎性脱髓鞘）是 所有多发性硬化症（MS）病例中约25%的初始症状（影响超过40万美国人， 每年减少近300亿美元的社会医疗保健费用）。近三分之二的MS患者将经历发作 的视神经炎，40-60%的患者有视觉缺陷局限于ON。这些疾病不需要治疗。 即使如此，ON中轴突的损伤是渐进的，由机会窗口定义， 功能丧失和实际退化之间的治疗。复苏的潜力是存在的，因为 如果在这个机会窗口期进行治疗，可以帮助预防疾病进展。然而，我们没有 有效的手段来评估谁在窗口期以及谁将从治疗中受益。 我们建议将神经影像学领域的计算成像方法转化为 胸部，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 将提高预后的准确性，更好地了解患者的反应，并加强有针对性的干预， 选择。这项工作的技术假设是，定量图像处理可以鲁棒和准确地分割， 配准并融合来自现代MRI和CT临床序列的ON数据。这一提议的核心假设是， 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 那些没有的人。 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建立在强大的医学图像计算的最新进展，以分割ON 在临床CT和MRI采集中，开发配准程序以建立受试者内和受试者间对应关系， 并将临床护理中通常使用的多模式成像研究的信息汇集在一起 (aim 1）。有了这些新方法，我们将解决探索性的假设，定量使用临床成像 数据可以提高预测的准确性（目标2）。我们注意到，目标2特别具有探索性，并符合高 这一机制的风险/高回报方面;许多研究表明，基线成像并不能决定性地预先 判断长期结果或治疗反应。我们推测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，我们将继续 使用更详细的疾病分期标准，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。