Multimodal Image Analysis Software for Epilepsy

癫痫多模态图像分析软件

• 批准号: 9205272
• 负责人: XENOPHON PAPADEMETRIS
• 金额: $ 49.4万
• 依托单位: ELECTRICAL GEODESICS, INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9205272
• 关键词: Address; Affect; Archives; Brain; Brain Diseases; Brain region; Certification; Child; Clinical; Clinical Research; Code; Complex; Computer software; Custom; Data; Databases; Development; Disease; Electrodes; Electroencephalography; Epilepsy; Excision; Failure; Fiber; Functional Magnetic Resonance Imaging; Funding; Goals; Hospitals; Image; Image Analysis; Imagery; Implanted Electrodes; Intractable Epilepsy; Joints; Life; Location; Magnetic Resonance Imaging; Maps; Measurement; Medical; Methodology; Methods; Modality; Multimodal Imaging; National Institute of Biomedical Imaging and Bioengineering; Navigation System; Operative Surgical Procedures; Patients; Pharmaceutical Preparations; Phase; Population; Positron-Emission Tomography; Postoperative Period; Procedures; Process; Research; Research Infrastructure; Seizures; Site; Small Business Innovation Research Grant; Software Tools; Source; Standardization; Surface; Techniques; Testing; Tissues; Translations; United States National Institutes of Health; Work; X-Ray Computed Tomography; anatomic imaging; base; bioimaging; brain abnormalities; brain surgery; clinical research site; cost; data sharing; design; experience; image guided; image processing; image registration; imaging modality; implantation; innovation; life time cost; multimodality; neuroimaging; neurosurgery; open source; programs; public health relevance; research clinical testing; research study; single photon emission computed tomography; tool

 DESCRIPTION (provided by applicant): Epilepsy is a debilitating brain disorder and surgery is the key treatment modality for those patients whose seizures cannot be controlled medically. Epilepsy surgery is complex, requiring multimodal imaging data for planning and execution, and image analysis software is essential for this process. The overall goal of this application is to develop a robust commercial software platform for multimodal image analysis for epilepsy surgery that can obtain regulatory approval for clinical use in both academic and non- academic hospitals. These software tools were developed in part for the needs of our epilepsy research at Yale over the last 10 years under NIH/NIBIB funding and in part internally at Electrical Geodesics Inc. for the brain segmentation, source analysis, and display parts of the forthcoming GeoSource 3.0 software package. The innovation in this proposal lies (i) the development of innovative image analysis methodology that addresses specific needs in epilepsy image analysis and (ii) the translation of tested research software to a new design that will enable its successful transition via regulatory approval to clinical use. The significance of this proposal is that it aims to provide clinically usable epilepsy surgical planning software with explicit support for multimodal image integration and intracranial electrode localization that can be integrated with the image-guided navigation systems used for neurosurgery. This tool would have a major impact on both surgical planning and image-guided epilepsy neurosurgery.

 描述（由申请人提供）：癫痫是一种使人衰弱的脑部疾病，手术是那些癫痫发作无法通过药物控制的患者的主要治疗方式。癫痫手术是复杂的，需要多模态成像数据进行规划和执行，图像分析软件是这个过程中必不可少的。本申请的总体目标是开发一个强大的商业软件平台，用于癫痫手术的多模态图像分析，该平台可以获得监管部门的批准，用于学术和非学术医院的临床使用。这些软件工具的开发部分是为了满足我们在耶鲁大学癫痫研究的需要，在过去的10年里，在NIH/NIBIB的资助下，部分是在电气大地测量公司内部开发的。用于即将推出的GeoSource 3.0软件包的大脑分割、源分析和显示部分。该提案的创新在于（i）开发创新的图像分析方法，以满足癫痫图像分析的特定需求，以及（ii）将测试的研究软件转换为新设计，使其能够通过监管批准成功过渡到临床使用。这一提议的意义在于 它旨在提供临床可用的癫痫手术计划软件， 用于多模态图像集成和颅内电极定位，可与用于神经外科的图像引导导航系统集成。该工具将对手术规划和图像引导癫痫神经外科手术产生重大影响。

项目成果

Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning.

使用局部面向外观和词典学习将大脑表面从CT图像分割。

• DOI: 10.1109/tmi.2018.2868045
• 发表时间: 2019-03
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6
• 作者: Onofrey JA;Staib LH;Papademetris X
• 通讯作者: Papademetris X

Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration.

使用字典学习从具有伪影的 CT 图像中分割大脑表面以进行非刚性 MR-CT 配准。

• DOI: 10.1007/978-3-319-19992-4_52
• 发表时间: 2015
• 期刊: Information processing in medical imaging : proceedings of the ... conference
• 作者: Onofrey,JohnA;Staib,LawrenceH;Papademetris,Xenophon
• 通讯作者: Papademetris,Xenophon