A Hidden Markov Model Based Segmentation Framework for MR Spectroscopy Imaging

基于隐马尔可夫模型的磁共振波谱成像分割框架

• 批准号: 7107639
• 负责人: Nigel M John
• 金额: $ 29.11万
• 依托单位: INFOTECH SOFT, INC.
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2007-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7107639
• 关键词: brain; measurement; model; neoplasm /cancer; tissues

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.

说明（申请方提供）：生物医学和计算以生物医学计算的形式融合在一起，这表明在基因序列、生物医学图像、卫生和社会科学定性描述符以及地理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，更准确和客观地表征解剖和分子结构，并建立易于处理的测量和正常和疾病状态的量化。磁共振波谱成像（MRSI）脑代谢物的准确定量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治疗效果方面变得越来越重要。在第一阶段，该提案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、稳健且计算效率高，最终可用作监测癌症治疗的工具。所提出的框架是基于一种新的利用隐马尔可夫模型（HMM）的训练，以识别不同的组织参数在大脑中，然后用于MR数据的分割。基于HMM的分割是用于其有吸引力的准确性，鲁棒性和计算效率（作为权衡与准确性）的特点，这是从HMM的数学基础以及初步的结果证明。然后，分割框架将被用作系统的一部分，以在用于癌症治疗分析的MRSI分析中提供脑代谢物的可再现和易处理的定量。基于第一阶段的成功，在第二阶段，从框架中开发的工具将被整合到图片存储和通信系统（PACS）中，用于临床使用，利用MRSI数据集监测癌症治疗的效果。脑代谢物的准确体内定量可用于检查疾病的长期影响和监测治疗效果。这提供了一个独特的机会，利用新的图像处理技术，更准确和客观地表征解剖和分子结构，并建立易于处理的测量和正常和疾病状态的量化。分段MR成像数据和"功能性" MR数据的共同分析可以提高评估神经退行性疾病、炎症/感染性疾病和神经血管疾病患者疾病负担的准确性。