A Hidden Markov Model Based Segmentation Framework for MR Spectroscopy Imaging

描述(申请人提供)：生物医学与生物医学计算形式的计算相结合，在遗传序列、生物医学图像、健康和社会科学的定性描述以及地球空间图像和化学公式等领域取得了进展。这提供了一个独特的机会，利用新的图像处理技术来更准确和客观地表征解剖和分子结构，并建立易于处理的正常和疾病状态的测量和量化。从磁共振波谱成像(MRSI)中准确定量大脑代谢物在检查疾病的长期影响和监测癌症、神经退行性疾病和精神健康的治疗效果方面变得越来越重要。在第一阶段，这项建议将开发一个创新的图像分割框架，用于分析MRSI数据，该框架准确、健壮，计算效率高，最终可用作监测癌症治疗的工具。该框架基于隐马尔可夫模型(HMM)的一种新的应用，该模型被训练来识别大脑中的不同组织参数，然后被用于MR数据的分割。从隐马尔可夫模型的数学基础和初步实验结果可以看出，基于隐马尔可夫模型的分割方法具有良好的精确度、稳健性和计算效率(折衷于精度)的特点。然后，分割框架将被用作系统的一部分，以便在用于癌症治疗分析的MRSI分析中提供可重复性和易处理的大脑代谢物定量。在第一阶段取得成功的基础上，在第二阶段，根据该框架开发的工具将被整合到一个临床使用的图像存档和通信系统(PACS)中，该系统利用MRSI数据集来监测癌症治疗的效果。准确的体内脑代谢物定量在检查疾病的长期影响和监测治疗效果方面是有用的。这提供了一个独特的机会，利用新的图像处理技术来更准确和客观地表征解剖和分子结构，并建立易于处理的正常和疾病状态的测量和量化。分段磁共振成像数据和“功能性”磁共振数据的联合分析可以提高评估神经退行性疾病、炎性/感染性疾病和神经血管疾病患者疾病负担的准确性。

DESCRIPTION (provided by applicant): The convergence of biomedicine and computation in the form of biomedical computing has demonstrated gains in the areas of genetic sequences, biomedical images, qualitative descriptors for health and social science and geospatial images and chemical formulae. This provides a unique opportunity to utilize novel image processing techniques to more accurately and objectively characterize anatomical and molecular structures and establish tractable measurements and quantification of normal and disease states. Accurate quantification of brain metabolites from Magnetic Resonance Spectroscopic Imaging (MRSI) is becoming increasingly important in the examination of long-term effects of disease and monitoring of the effects of treatment in cancer, neuro-degenerative diseases, and mental health. During Phase I, this proposal will develop an innovative image segmentation framework for the analysis of MRSI data, which is accurate, robust, and computationally efficient for eventual use as a tool in monitoring cancer treatment. The proposed framework is based on a novel utilization of Hidden Markov Models (HMM) that are traind to recognize different tissue parameters in the brain and is then used for the segmentation of MR data. The HMM-based segmentation is used for its attractive accuracy, robustness and computational efficiency (as tradeoff with accuracy) characteristics which are demonstrated from the mathematical foundation of the HMM as well as the preliminary results. The segmentation framework will then be used as part of a system to provide reproducible and tractable quantification of brain metabolites in MRSI analysis for cancer treatment analysis. Based on the success of Phase I, in Phase II, the tools developed from the framework will be integrated within a Picture Archiving and Communication Systems (PACS) for clinical use utilizing MRSI datasets for monitoring the effects of cancer treatment. Accurate in vivo quantification of brain metabolites is useful in examination of long-term effects of disease and monitoring the effects of treatment. This provides a unique opportunity to utilize novel image processing techniques to more accurately and objectively characterize anatomical and molecular structures and establish tractable measurements and quantification of normal and disease states. Co-analysis of segmented MR imaging data and "functional" MR data can improve the accuracy of assessing the burden of disease in patients with neurodegenerative, inflammatory/infectious, and neurovascular disorders.