CAREER: Probabilistic Models for Integrating Biochemical and Morphological Markers for Cancer

根据这一职业奖，将开发一套新的计算机辅助分析技术，通过整合磁共振波谱成像(MRSI)和磁共振成像(MRI)的生化和形态标记，提高脑癌的非侵入性诊断。磁共振成像可以对生化代谢物进行表征和量化，并构建代谢物强度图像，与磁共振成像相结合，可以提供疾病的生化和形态视图。利用短MRSI回波时间技术，将研究10-20维多变量特征空间，以揭示用于表征癌症的特定特征。具体目标包括：利用最大后验概率框架在磁共振成像中恢复代谢物强度图像的“半盲”源分离；表征代谢物强度图像与来自MRI的形态信息之间的相关性和相关性；开发用于将代谢物强度图像与MRI相结合的分层概率模型，用于脑肿瘤的联合生化/形态表征；并在计算机辅助诊断的背景下评估模型的性能，与依赖于相当基本的关系的传统方法进行比较，例如两种代谢物浓度的比率。提案的教育部分侧重于生物医学工程的机器学习计划，包括一门新课程和计算机实验室，以及将作为工业实习计划基础的努力。本课程将向学生介绍机器学习和概率模型背后的数学理论，它们在生物医学科学中的应用，以及评估和验证它们的表现的技术。

0133804SajdaUnder this CAREER Award, a new set of computer-assisted analysis techniques will be developed to improve the noninvasive diagnosis of brain cancer by integrating biochemical and morphological markers from MRSI (magnetic resonance spectroscopy imaging) and MRI (magnetic resonance imaging). MRSI, which allows for characterization and quantification of biochemical metabolites and the construction of metabolite intensity images, combined with MRI provides a biochemical and morphological view of the disease. Using short MRSI echo time techniques, 10-20 dimensional multi-variant feature space will be studied to uncover specific signatures for characterizing cancer. Specific aims include: develop "semi-blind" source separation using a maximum a posteriori framework for recovery of metabolite intensity images in MRSI; characterize the correlations and dependencies between metabolite intensity images and morphological information derived from MRI; develop a hierarchical probabilistic model for integrating metabolite intensity images with MRI for the joint biochemical/morphological characterization of brain tumors; and assess the performance of the models within the context of computer-assisted diagnosis, making comparisons to traditional methods that have relied on fairly elementary relationships, such as the ratio of two metabolite concentrations.The educational component of the proposal focuses on a program in machine learning for biomedical engineering, including a new course and computer laboratories and efforts that would serve as a basis of an industrial internship program. The course will introduce students to the mathematical theory behind machine learning and probabilistic models, their application to the biomedical sciences, and techniques for evaluating and validating their performance.