BAYESIAN SOURCE IMAGING OF PEDIATRIC EPILEPSY

小儿癫痫的贝叶斯源成像

• 批准号: 8363719
• 负责人: CHRISTOPHER R. JOHNSON
• 金额: $ 8.88万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8363719
• 关键词: Adverse effects; Affect; Algorithms; American; Biomedical Computing; Blinded; Childhood; Clinical; Curative Surgery; Data; Data Analyses; Epilepsy; Evaluation; Excision; Fiber; Focal Seizure; Funding; Grant; Health Care Costs; Image; Methods; Modality; Modeling; Morphologic artifacts; National Center for Research Resources; Noise; Operative Surgical Procedures; Outcome; Pathway interactions; Patients; Pharmaceutical Preparations; Physics; Principal Investigator; Research; Research Infrastructure; Resources; Seizures; Services; Solutions; Source; Structure; Technology; United States National Institutes of Health; base; clinical care; clinical practice; cost; data acquisition; design; improved; technological innovation

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Background: Epilepsy affects over 2.5 million Americans and has an estimated total annual health care cost close to $12.5 billion per year. Medications for epilepsy often have significant side effects and currently fail to halt seizures in up to 20% of patients. Approximately 75% of epilepsy patients have their first seizure in childhood. Many only become candidates for surgical intervention after a long period of partially-effective medication that can have debilitating educational and sociological side effects. The overall objective of this DBP is to develop quantitative analysis algorithms to dramatically improve our capacity to detect and localize epileptogenic foci, in order to enable curative surgery for more patients. Rationale: Technological innovations, as described in this proposal, offer the prospect of improving foci localization and thereby dramatically improving patient outcomes. If foci are considered to be away from eloquent cortex, rather than in or nearby critical fiber pathways, they may be successfully treated, for example, by resection or transection of related fibers. These assessments rely on knowing where the seizure foci are located and their relationship to surrounding structures. With poor localization, some patients may be considered inoperable. If more accurate foci localization can be developed, more patients will be considered candidates for surgery. Questions: Today, the use of sophisticated physics and realistic forward models has been made practical by the exponentially increasing compute capacity of modern workstations. Furthermore, the use of computationally expensive methods can dramatically improve the reliability of the inverse solution, enabling more realistic dipole models and allowing better noise and artifact rejection. Unfortunately, these technologies have not yet reached clinical practice. Design & Methods: Each patient who is identified by the clinical epilepsy service for evaluation as a potential candidate for surgery will be approached and given the opportunity to participate in this study. If a patient chooses to participate in the study, additional data acquisition and data analysis will be carried out. All patients will receive the standard clinical care, which involves independent assessment of each modality. Experts blinded to the clinical interpretation of the data will review the multi-modality data fusion and determine if the patient is a candidate for surgical intervention based on the presence of a focal seizure zone.

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 背景资料： 癫痫影响着超过250万美国人，估计每年的医疗保健总额 每年花费近125亿美元。癫痫的治疗方法有哪些？ 目前，高达20%的患者无法停止癫痫发作。约75%的 癫痫患者在童年时第一次癫痫发作。许多人只是成为候选人 手术干预后，长期的部分有效的药物，可以有 削弱教育和社会学的副作用。本DBP的总体目标是 开发定量分析算法，以显著提高我们的检测能力， 定位致痫灶，使更多的患者能够进行根治性手术。 基本原理： 本提案中所述的技术创新提供了改进的前景， 病灶定位，从而显著改善患者的治疗效果。如果病灶 被认为是远离功能皮层，而不是在或附近的关键纤维 通过切除或横断血管通路，可以成功治疗这些疾病。 相关纤维这些评估依赖于了解癫痫病灶的位置， 与周围结构的关系。由于定位不良，有些患者可能 被认为是不可操作的。如果能够开发更准确的病灶定位， 都将被考虑做手术 问题： 今天，使用复杂的物理和现实的前向模型已经取得了 现代工作站的计算能力呈指数级增长。 此外，使用计算上昂贵的方法可以显著地改善 逆解的可靠性，使更现实的偶极子模型，并允许更好的 噪声和伪影抑制。不幸的是，这些技术还没有达到 临床实践 设计方法（& M）： 每一个被临床癫痫服务确定为潜在癫痫的患者， 手术候选人将被接触，并有机会参加这一 study.如果患者选择参与研究，则需要额外的数据采集和数据 将进行分析。所有患者都将接受标准的临床护理， 对每种模式进行独立评估。对临床不知情的专家 数据的解释将审查多模态数据融合，并确定 基于局灶性癫痫发作的存在，患者是手术干预的候选者 区