TimeAware Data Acquisition,RealTime Processing for fMRI

描述（由申请人提供）：功能磁共振成像已成为一种工具，映射大脑功能，因为它的高空间和时间分辨率和非侵入性。此外，它适用于单个受试者，因此可以在临床领域发挥作用。这些特性对于临床人群、研究、治疗和疾病监测具有吸引力。然而，与正常志愿者相比，临床人群更可能移动，更可能无法遵守任务，并且对长时间成像会话的耐受性较低。因此，这些受试者可能会产生更少的数据和更低的质量比正常，使其更难以检测低信号的变化，固有的BOLD对比功能磁共振成像。此外，合格的患者比正常情况下更难获得，因此确保获得有用的数据至关重要。及时反馈结果对于治疗计划也是必要的。因此，我们已经着手开发一种临床上胜任的功能磁共振成像系统，比现有系统更灵活，更有效。该系统的目标是在存在多个并发效应的情况下提供时间有效的激活估计，跟踪导致混淆效应（运动、语音、心脏搏动、呼吸等）的过程，并使处理适应受试者的表现，而不是预期的任务。该系统的主要组成部分，如时间感知架构，测量和生理数据（语音，心脏，呼吸，皮肤电导）和实时多元线性回归的过滤已经完成。我们将扩展该系统的运动，生理噪声的建模和实时语音检测的开发，用于在一个范例中使用，其中的范例交付和处理的数据的时间是由受试者的反应驱动的emphasatingdetection和校正。

DESCRIPTION (provided by applicant): FMRI has become a tool for mapping brain function because of its high spatial and temporal resolution and noninvasiveness. In addition, it is applicable to single subjects and thus could play a role in the clinical realm. These properties are appealing for use in clinical populations, for research, treatment and disease monitoring. However, in contrast to normal volunteers, clinical populations are more likely to move, more likely to fail to conform to the task and are less tolerant to long imaging sessions. Thus, these subjects may generate less data and of lower quality than normal making it more difficult to detect the low signal changes inherent in BOLD contrast fMRI. In addition, qualified patients are more difficult to come by than normal making it critical to insure that useful data has been acquired. Timely feedback from results is also necessary for treatment planning. Thus, we have set out to develop a clinical competent fMRI system that is more flexible and more efficient that existing systems. The goal of the system is to provide time efficient estimation of activation in the presence of multiple concurrent effects, track processes that lead to confounding effects (motion, speech, cardiac pulsatility, respiration, etc) and adapt the processing to the subject's performance rather than the expected task. Major components of this system such as the timeaware architecture, measurement and filtering of physiologic data (speech, cardiac, respiration, skin conductance) and real-time multiple linear regression have been completed. We will extend the system by incorporatingdetection of and correction for motion, modeling of physiologic noise and development of real-time speech detection for use in a paradigm where both the timing of paradigm delivery and processing of the data is driven by the subject's response.