Image analysis and machine learning methods for advanced MRI-neuropathology characterization

点击翻译按钮获取中文摘要

Introduction***Image analysis and machine learning methods represent a paradigm shift and are playing an increasingly critical role in many fields. The potential of these methods in medical imaging includes disease prediction, personalization, and pathological grading. This research program focuses on advancing technologies to categorize pathology. Specifically, we aim to develop new image texture analysis and machine learning methods for accurate detection of injury and repair in brain white matter lesions, along with validation by digital pathology. Currently our target applications are in magnetic resonance imaging (MRI); however, our approaches can be applied in multiple other science and engineering fields such as geoscience, physics, engineering, and biology.******Under the current Discovery Grant (2012-2018), we have developed methods and tools to understand the multi-resolution pattern of MRI texture using localized spatial frequency analysis. We have shown that a specific type of white matter structure is related to a unique series of texture spectra, reflecting the unique forming scales and aligning directions of the structure. Further, the heterogeneity of MRI texture correlates strongly with the severity of pathology as shown using multiple sclerosis as an example. However, there are still critical unmet needs in both modeling and verification of individual lesion pathology. ******Objective***The overall objective of this DG is to develop new texture analysis and machine learning methods for accurate and efficient mapping of neuropathology, particularly the degree of injury and repair in different types of brain white matter lesions. This includes 5 aims:***Aim 1: Develop new methods to extract the most critical MRI texture spectra ***Aim 2: Discover approaches for best detecting tissue directionality in MRI***Aim 3: Design a method to integrate texture analysis with machine learning ***Aim 4: Identify an algorithm for efficient quantification of digital pathology ***Aim 5: Build a method to fuse MRI with digital pathology for single lesion identity******Approach***In Aim 1, we will investigate new spectrum assessing methods and identify the most useful texture spectra using principal component analysis. In Aim 2, we will evaluate phase congruency and other alignment assessing methods that best correlate with lesion severity. Aim 3 will determine a machine learning method for accurate lesion type classification based on new texture features. In Aim 4, we will develop new image analysis and machine learning methods for quantifying pathology images, and in Aim 5 we will compare MRI and pathological measures to establish a non-invasive method for assessing single lesion property.******Impact***The application of new texture analysis and machine learning methods to imaging will lead to the development of advanced technologies for various fields such as signal processing, geoscience, tissue engineer, and healthcare.