Automated 4D Neuromorphometry: Detecting Change in Brain Structure

描述(由申请人提供)：该项目将开发软件，以促进联合配准、可视化和快速定量分析纵向获取的人脑磁共振图像(MRI)的变化。也就是说，它的目标是为四维(4D)神经形态分析提供更高的自动化和增强的可视化工具。如果系列变化检测和分析方法要有效，就必须克服主要的技术挑战。为了取得成功，该项目将需要实施和验证能够补偿系列图像数据集之间混淆差异的算法方法，同时准确地识别和量化感兴趣的结构的变化。CorTechs已经展示了开发智能图像分析方法的能力，在对患有神经疾病的患者进行单时间点MRI测量时，通过计算得出大脑解剖和病理的定量测量，我们将在当前应用程序中建立这些方法。在第一阶段，我们将通过首先实现技术来证明可行性，以最小化图像伪影和其他无关差异对序列数据集之间的解剖变化的评估的潜在影响。我们还将开发方法来优化这种纵向数据的空间配准，并促进对进行性神经系统疾病患者的连续扫描之间解剖变形的量化和视觉理解。此外，这些方法的有效性和价值将通过对它们对萎缩过程检测的改进的测试来说明。在第二阶段，我们将寻求进一步改进和扩展这些技术，将它们应用于更广泛的临床条件，对通过该技术实现的放射工作流程的改进进行实证评估，并获得FDA的批准，使其成为适合常规临床使用的医疗设备软件。我们预计将以智能图像分析工具的形式将由此产生的4D神经形态测量方法推向市场，这些工具将为参与系列MRI数据临床审查的研究人员和医生提供计算支持。临床神经放射学对有效和高效的计算支持方法的需求越来越大，这种方法可以改进对脑结构随时间的变化的检测和测量。改进的系列变化检测和分析方法的潜在应用包括在神经退行性疾病(如阿尔茨海默病(AD))或具有继发性神经元或轴突损伤的疾病(如多发性硬化症(MS)或失控癫痫)中推导萎缩度量。4D神经形态计量学方法的其他重要应用可能包括用于鉴别诊断、监测脑肿瘤生长、监测创伤性脑损伤、中风、酗酒和抑郁症所致萎缩的恢复或追踪。这个项目将提供的工具有很大的潜在市场。

DESCRIPTION (provided by applicant): This project will develop software to facilitate co-registration, visualization and rapid quantitative analysis of changes in longitudinally-acquired magnetic resonance images (MRIs) of the human brain. That is, it aims to provide increased automation and enhanced visualization tools for four dimensional (4D) neuromorphometric analysis. Major technical challenges must be overcome if serial change detection and analysis methods are to be effective. To be successful this project will need to implement and validate algorithmic approaches that can compensate for confounding differences between serial image data sets while accurately identifying and quantifying changes in structures of interest. CorTechs has already demonstrated the capability of developing intelligent image analysis methods for computationally deriving quantitative measures of brain anatomy and pathology in single time-point MRI measurements of patients with neurological conditions, methods that we will build on in the current application. In Phase I we will demonstrate feasibility by first implementing techniques to minimize the potential influence of image artifacts and other irrelevant differences on the assessment of anatomical change between serial data sets. We will also develop methods to optimize the spatial coregistration of such longitudinal data, and to facilitate the quantification and visual comprehension of anatomical deformations between serial scans of patients with progressive neurological disease. In addition, the validity and value of such methods will be illustrated through tests of their improvement to the detection of atrophic processes. In Phase II we would seek to further refine and extend these techniques, apply them to a wider variety of clinical conditions, empirically evaluate the improvements in radiology workflow enabled by the technology, and obtain FDA clearance for them as medical device software suitable for routine clinical use. We anticipate marketing the resulting 4D neuromorphometric methods in the form of intelligent image analysis tools that will provide computational support both to researchers and to physicians involved with the clinical review of serial MRI data. There is a growing need in clinical neuroradiology for effective and efficient computational support methods that can improve the detection and measurement of changes in brain structure over time. Potential applications of improved serial change detection and analysis methods include the derivation of atrophy measures in neurodegenerative disorders such as Alzheimer's disease (AD), or in diseases with secondary neuronal or axonal injury such as multiple sclerosis (MS) or uncontrolled epilepsy. Other important applications of 4D neuromorphometry methods may include its use in differential diagnosis, in the monitoring of brain tumor growth, and in the monitoring of recovery from, or tracking atrophy consequent to, traumatic brain injury, stroke, alcoholism, and depression. There is a large potential market for the tools this project will provide.