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/文摘