Automated brain parcellation and morphometry

描述（由申请人提供）：阿尔茨海默病（AD）是一种毁灭性的疾病，随着美国人口老龄化，它正在达到流行病的比例。阿尔茨海默病的早期诊断对于个人和家庭规划他们的未来、获得适当的护理和尽早开始治疗至关重要。尽管有这种明确而迫切的需求，但早期阿尔茨海默病的诊断严重不足。研究表明，MRI量化脑萎缩有助于AD的诊断；目前的拨款建议完成FDA监管批准软件设备的必要步骤，该设备可以自动向临床医生提供此类信息。我们的第一个目标是整合、完善和进一步优化在以前的SBIR拨款中开发的结构分析流。这将涉及将研究代码移植到fda批准的代码库中，改进颅骨剥离和部分体积估计方法，并将其集成到分割处理流软件中。我们将实现一种方法来识别自动量化可能产生不准确结果的情况，并改进判别函数以最佳地分类不同的诊断类别。我们的第二个目标是修改我们现有的图像管道软件，使其成为临床应用的强大商业软件。产品，添加行业标准（DICOM）服务，入站序列参数过滤和错误/异常/管理报告，以及用于快速临床审查的有效图形界面，所有这些都在一个完全自动化的处理流程中集成在放射科的数字环境中。我们的第三个目标是评估该医疗软件设备的安全性和有效性，以便获得FDA上市前批准，将其作为辅助诊断AD的临床使用。这将需要遵守所有相关的FDA法规和指南，盲法实验室测试和自动定量分割结果的科学验证，通过外部beta测试对用户标签（说明和用户界面可用性）进行临床验证，并通过在控制良好的AD临床人群中使用该流程对诊断标签进行统计验证。如果成功，目前的项目将产生fda批准的产品，该产品将广泛用于提高阿尔茨海默病的准确性和早期诊断。

DESCRIPTION (provided by applicant): Alzheimer's disease (AD) is a devastating disease that is reaching epidemic proportions as the US population ages. Early diagnosis of AD is crucial for individuals and families to plan for their futures, to obtain appropriate care, and to start treatment as early as possible. Despite this clear and pressing need, early AD is severely under-diagnosed. Research studies show that quantification of cerebral atrophy from MRI could aid in AD diagnosis; the current grant proposes to complete the steps necessary for FDA regulatory approval of a software device that could provide such information automatically to clinicians. Our first aim is to integrate, refine and further optimize the structural analysis stream developed in previous SBIR grants. This will involve porting research code into an FDA-approvable code base, refinement of skull- stripping and partial volume estimation methods, and their integration into the segmentation processing stream software. We will implement a method for identifying cases where the automatic quantification may have yielded inaccurate results, and refine discriminant functions to optimally classify different diagnostic categories. Our second aim is to modify our existing image pipeline software for clinical utilization as a robust commercial .product, adding industry standard (DICOM) services, inbound sequence parameter filtering and error/exception/management reporting, and an effective graphical interface for rapid clinical review, all within a completely automated processing flow integrated in a radiology department's digital environment. Our third aim is to evaluate the safety and efficacy this medical software device in order to obtain FDA Pre- Market Approval for its clinical use as an adjunct in the diagnosis of AD. This will require compliance with all relevant FDA regulations and guidelines, blinded laboratory testing and scientific validation of the automated quantitative segmentation results, clinical validation of user labeling (instructions and user interface usability) through external beta testing, and statistical validation of diagnostic labeling by using the stream in a well- controlled clinical population with AD. If successful, the current project would result in an FDA-approved product that will be widely used to increase the accurate and early diagnosis of AD.