MR Image Example-based Contrast Synthesis for Consistent Image Analysis

基于 MR 图像示例的对比度合成，用于一致的图像分析

• 批准号: 8191836
• 负责人: Jerry L Prince
• 金额: $ 19.75万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8191836
• 关键词: Address; Algorithms; Atlases; Clinical; Collection; Communities; Computer software; Cues; Data; Data Analyses; Data Set; Detection; Development; Elements; Evaluation; Exploratory/Developmental Grant; Face; Grant; Image; Image Analysis; Individual; Investigation; Java; Learning; Lesion; Location; Magnetic Resonance; Magnetic Resonance Imaging; Manufacturer Name; Measurement; Medical Imaging; Methods; Modification; Morphologic artifacts; Nature; Neurology; Neurosciences; Paper; Patients; Pattern; Physiologic pulse; Play; Population Study; Process; Property; Publications; Relative (related person); Research; Research Personnel; Research Project Grants; Resolution; Role; Science; Shapes; Software Tools; Standardization; Surface; Techniques; Technology; Testing; Time; Tissues; Weight; base; brain tissue; data acquisition; image processing; improved; indexing; method development; neuroimaging; novel; novel strategies; open source; prevent; prototype; symposium; theories; tool; white matter

DESCRIPTION (provided by applicant): The automatic analysis of medical images has played a key role in many discoveries in neuroscience over the past two decades. Magnetic resonance imaging (MRI) maintains a central role in this scientific process as well as in clinical neuroimaging because of its ability to use different pulse sequences that can provide alternate contrasts capable of revealing subtle tissue differences in both normal and diseased tissues. Yet there are three widely recognized problems in the reliable and consistent application of automatic image processing algorithms to MR data. First, the lack of a standardized scale in the image measurements means that results obtained on different scanners or at different times are not necessarily comparably quantified for individual studies or reliably pooled for population studies. For example, T1-weighted images are routinely acquired, but differences in the pulse sequences can cause significant differences in the brain tissue contrasts. Second, tissue contrasts that are ideal for certain steps in automatic processing are not always acquired in a given study or at a given imaging center. For example, although double-echo PD/T2-weighted images are routinely acquired, FLAIR images are often omitted for time considerations unless white matter lesions are expected or directly under study. Third, images often have intensity shading artifacts caused by spatially varying coil sensitivity patterns. These problems are worse at higher field strengths, preventing consistent analysis of these data without correction. All three of these problems will be addressed in this research project by investigation and further development of the method called Magnetic Resonance Image Example-based Contrast Synthesis (MIMECS). MIMECS is a post processing method that uses a standardized atlas with multiple images in order to synthesize contrasts that are consistent with the atlas given one or more subject images. The strategy is quite different than past approaches, which have focused on rich data acquisition, nonlinear atlas registration, or histogram modification techniques. MIMECS focuses on image synthesis using patches that index into an atlas thousands of times in order to learn an optimal synthesis formula at each voxel. It uses anatomical information from the atlas while avoiding the time-consuming process that would be required of a multi-atlas nonlinear registration approach. The research plan comprises three specific aims: 1) The theory of example-based image synthesis will be studied in order to optimize MIMECS; 2) The computational approach will be refined and optimized for different applications; 3) The use of MIMECS in synthesizing both FLAIR images for white matter lesion detection and optimized T1-weighted images for cortical surface extraction will be thoroughly evaluated on large existing data sets. The software will be thoroughly tested and then released as open source software within the Java Image Science Toolkit (JIST) for widespread availability to the neuroscience community. PUBLIC HEALTH RELEVANCE: Automated image analysis of magnetic resonance images plays a central role in neuroscience, yet it is very challenging to obtain consistent results when data is acquired from different scanners or at significantly different times. This exploratory research project will develop, validate, and make freely available as an open source software tool a post processing method called Magnetic Resonance Image Example-based Contrast Synthesis (MIMECS), which addresses these standardization issues using a novel atlas-based strategy.

描述（由申请人提供）：在过去的二十年里，医学图像的自动分析在神经科学的许多发现中发挥了关键作用。 磁共振成像（MRI）在这一科学过程中以及在临床神经成像中保持着核心作用，因为它能够使用不同的脉冲序列，这些脉冲序列可以提供能够揭示正常和病变组织中细微组织差异的交替对比。 然而，在MR数据的自动图像处理算法的可靠和一致的应用中存在三个广泛认识到的问题。 首先，在图像测量中缺乏标准化的尺度意味着在不同扫描仪或不同时间获得的结果不一定被量化用于个体研究或可靠地汇集用于群体研究。 例如，常规采集T1加权图像，但脉冲序列的差异可能导致脑组织对比度的显著差异。 第二，对于自动处理中的某些步骤来说理想的组织对比度并不总是在给定的研究中或在给定的成像中心获得。 例如，尽管常规采集双回波PD/T2加权图像，但出于时间考虑，通常省略FLAIR图像，除非预期或直接研究白色病变。 第三，图像通常具有由空间变化的线圈灵敏度图案引起的强度阴影伪影。 这些问题在更高的场强下更严重，从而阻止了在不进行校正的情况下对这些数据进行一致的分析。 所有这三个问题将在本研究项目中解决的调查和进一步发展的方法称为磁共振图像基于实例的对比度合成（MIMECS）。 MIMECS是一种后处理方法，其使用具有多个图像的标准化图谱，以便合成与给定的一个或多个受试者图像的图谱一致的对比度。 该策略与过去的方法有很大的不同，过去的方法主要集中在丰富的数据采集、非线性图谱配准或直方图修改技术。 MIMECS专注于图像合成，使用数千次索引到图集中的补丁，以学习每个体素的最佳合成公式。 它使用图谱中的解剖信息，同时避免了多图谱非线性配准方法所需的耗时过程。 研究计划包括三个具体目标：1）研究基于实例的图像合成理论，以优化MIMECS; 2）改进和优化计算方法，以适应不同的应用; 3）MIMECS在合成用于白色物质病变检测的FLAIR图像和优化的T1-T4图像中的使用。用于皮层表面提取的加权图像将在大的现有数据集上被彻底评估。 该软件将经过全面测试，然后作为Java Image Science Toolkit（JIST）中的开源软件发布，以广泛提供给神经科学社区。 公共卫生关系：磁共振图像的自动图像分析在神经科学中起着核心作用，但当数据从不同的扫描仪或在显著不同的时间采集时，获得一致的结果是非常具有挑战性的。 这个探索性研究项目将开发、验证并作为开源软件工具免费提供一种名为基于磁共振图像样本的对比度合成（MIMECS）的后处理方法，该方法使用一种新型的基于地图集的策略来解决这些标准化问题。