ROBUST ESTIMATION OF REGIONAL PULMONARY PARAMETERS IN PRESENCE OF NOISE

点击翻译按钮获取中文摘要

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.