ROBUST ESTIMATION OF REGIONAL PULMONARY PARAMETERS IN PRESENCE OF NOISE

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 超极化氦-3磁共振成像（HP 3 He MRI）是分析 肺的结构和功能。它能够测量氧分压（PAO 2） 和肺中的氧消耗率（ODR）。图像数据中的固有噪声可能会扭曲 这些参数的估计，并因此限制了其估计的临床有用性。 提高图像信噪比的常用技术有： 将相邻的体素一起分组到仓中并求平均。这种方法降低了空间 分辨率，并且可以从分析中排除有用的边缘点。这项工作提出了一种技术 用于使用主成分分析（PCA）和非空间聚类点来简化数据。这 方法，在这里被称为基于PCA的聚类，不会降低空间分辨率， 在模拟兔肺中产生比 传统的装箱技术。

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.