ROBUST ESTIMATION OF REGIONAL PULMONARY PARAMETERS IN PRESENCE OF NOISE

存在噪声时区域肺参数的稳健估计

• 批准号: 7955345
• 负责人: Kiarash Emami
• 金额: $ 2.23万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7955345
• 关键词: Clinical; Computer Retrieval of Information on Scientific Projects Database; Data; Funding; Grant; Helium; Image; Institution; Lung; Magnetic Resonance; Magnetic Resonance Imaging; Measures; Methods; Noise; Oryctolagus cuniculus; Oxygen; Partial Pressure; Principal Component Analysis; Research; Research Personnel; Resolution; Resources; Signal Transduction; Simulate; Source; Structure; Techniques; United States National Institutes of Health; Work; base; optical imaging; tool; working group

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Hyperpolarized helium-3 magnetic resonance imaging (HP 3He MRI) is a powerful tool for analyzing pulmonary structure and function. It is capable of measuring the partial pressure of oxygen (PAO2) and oxygen depletion rate (ODR) in the lungs. Inherent noise in the image data can skew the estimation of these parameters, and thus limits the clinical usefulness of their estimation. Commonly used techniques for increasing the signal-to-noise ratio (SNR) in the image work by grouping adjacent voxels together into bins and averaging. This approach degrades spatial resolution and may exclude useful edge points from the analysis. This work presents a technique for simplifying data with principal component analysis (PCA) and clustering points nonspatially. This method, which is referred to here as PCA-based clustering, does not degrade spatial resolution and yields more accurate estimations of regional oxygen parameters in a simulated rabbit lung than the traditional binning technique.

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 超极化氦-3磁共振成像(HP-3He MRI)是一种强有力的分析工具 肺结构和功能。它能够测量氧气分压(PAO2) 和肺的氧耗率(ODR)。图像数据中的固有噪声可能会扭曲 对这些参数的估计，从而限制了其估计的临床用途。 用于提高图像中的信噪比(SNR)的常用技术通过以下方式工作 将相邻体素分组到箱中并进行平均。这种方法降低了空间 分辨率，并可能从分析中排除有用的边缘点。这项工作展示了一种技术 用于通过主成分分析(PCA)和非空间聚类点简化数据。这 方法，这里被称为基于主成分分析的聚类，不会降低空间分辨率和 在模拟兔肺中对局部氧参数的估计比 传统的装桶技术。