MR Image Example-based Contrast Synthesis for Consistent Image Analysis

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)在这一科学过程以及临床神经成像中一直发挥着核心作用，因为它能够使用不同的脉冲序列，提供交替的对比，能够揭示正常组织和病变组织中的细微组织差异。然而，在可靠和一致地将自动图像处理算法应用于磁共振数据方面，有三个被广泛认识的问题。首先，图像测量缺乏标准化尺度，这意味着在不同扫描仪或不同时间获得的结果不一定能为个别研究进行比较量化，也不能可靠地汇集在一起进行人口研究。例如，常规获取T1加权图像，但脉冲序列的不同会导致脑组织对比度的显著差异。其次，对于自动处理中的某些步骤来说非常理想的组织对比并不总是在给定的研究或给定的成像中心获得的。例如，尽管常规获取双回波PD/T2加权图像，但FLAIR图像通常出于时间考虑而被省略，除非白质病变是预期的或直接在研究中。第三，图像通常存在由空间变化的线圈敏感度图案引起的强度阴影伪影。这些问题在场强越高的情况下更严重，无法在没有校正的情况下对这些数据进行一致的分析。这三个问题将在本研究项目中通过研究和进一步发展称为基于磁共振图像实例的对比度合成(MIMECS)方法来解决。MIMECS是一种后处理方法，它使用具有多个图像的标准化图集，以便合成与给定一个或多个主题图像的图集一致的对比度。这一策略与过去的方法有很大的不同，过去的方法侧重于丰富的数据获取、非线性图谱配准或直方图修改技术。MIMECS专注于使用索引到地图集数千次的补丁进行图像合成，以了解每个体素的最佳合成公式。它使用地图集的解剖信息，同时避免了多地图集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)针对不同的应用，改进和优化计算方法；3)在合成用于白质病变检测的FLAIR图像和用于提取皮质表面的优化T1加权图像方面，将在现有的大型数据集上进行彻底评估。该软件将经过彻底测试，然后作为Java图像科学工具包(JIST)中的开源软件发布，供神经科学界广泛使用。 公共卫生相关性：磁共振图像的自动图像分析在神经科学中发挥着核心作用，但当从不同的扫描仪或在显著不同的时间获取数据时，要获得一致的结果是非常具有挑战性的。这个探索性的研究项目将开发、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实例的对比度合成(MIMECS)的后处理方法，该方法使用一种新的基于图谱的策略来解决这些标准化问题。

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.