mi2b2 Enabled Pediatric Radiological Decision Support

描述（由申请人提供）：我们建议将最近开发的医学成像信息学工作台扩展到床边（mi2b2）工作台软件，使临床转化研究人员可以在常规临床护理期间收集医学图像。如今，这个病人信息的金矿并不容易获得。对于有特定想法的临床转化研究人员来说，来自临床知识库的具有良好特征的医学图像将是非常有价值的资源，这些研究人员需要在来自许多不同疾病的患者的大量图像中进行测试。我们的医院在开发和部署用于临床实践的先进生物医学成像技术（MRI、高速CT、超声、PET等）方面处于国际领先地位。在我们参与的医院（麻省总医院、布里格姆妇女医院和波士顿儿童医院）的每个放射科内的图片存档和通信系统（PACS）包含了丰富的医学图像，这些图像通常与现有临床研究成像数据的质量相当，并且在患者数量和疾病类型方面大大超过了其容量。虽然可以立即扩展到其他研究应用和诊断场景，但我们首先将我们的方法应用于解释儿童脑磁共振图像（MRI）的困难任务。拟议的儿科MRI模块和哈佛催化剂放射决策支持工具包的宿主是针对解决理解“正常”的直接临床挑战，因为正常是一个不断移动的目标，随着大脑发育而迅速变化。这些免费的开源软件工具是当前资助的政府项目的自然扩展。这包括来自两个国家生物医学计算中心的软件；集成生物学和床边信息学（i2b2）提供了从电子病历、实验室数据、计费信息系统和国家医学图像计算联盟（NAMIC）中以安全、符合HIPAA的方式提取和集成数据的工具，该联盟提供了用于医学图像分析的工具，包括注册、个体受试者和大型队列数据集的可视化和计算，并将构成哈佛催化剂放射决策支持工具包的基础。

DESCRIPTION (provided by applicant): We propose to extend the recently developed Medical Imaging Informatics Bench to Bedside (mi2b2) Workbench software to make medical images collected during routine clinical care available to clinical translational investigators. Today, this gold mine of patient information is not easily available. Well characterized medical images from clinical repositories would be an extremely valuable resource for clinical translational investigators who have specific ideas that require testing in large sets of images from many patients with different diseases. Our hospitals are international leaders in the development and deployment of advanced biomedical imaging technologies (MRI, high speed CT, ultrasound, PET and others) for clinical practice. The Picture Archive and Communication Systems (PACS) within each of the Departments of Radiology in our participating hospitals (Massachusetts General Hospital, Brigham and Women's Hospital, and Children's Hospital Boston) contain a wealth of medical images that often equal the quality of available clinical research imaging data, and greatly exceed its volume in terms of the number of patients and disease types. Though immediately extensible to other research applications and diagnostic scenarios, we apply our approach first to the difficult task of interpreting pediatric brain magnetc resonance images (MRI). The proposed Pediatric MRI module and Harvard Catalyst Radiological Decision Support toolkit that host it are targeted to address the immediate clinical challenge of understanding "normal" because normal is a constantly moving target, changing rapidly with brain development. These free and open source software tools are natural extensions of currently funded government projects. This includes software from two National Centers for Biomedical Computing; Informatics for Integrating Biology & the Bedside (i2b2) which provides tools that extract and integrate data in a secure, HIPAA compliant manner from electronic medical records, laboratory data, billing information systems and the National Alliance for Medical Image Computing (NAMIC), which provides tools for medical image analysis including registration, visualization and computation of individual subject and large cohort data sets and will form the foundation for the Harvard Catalyst Radiological Decision Support Toolkit.