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

0133804 Sajda在这项CAREER奖下，将开发一套新的计算机辅助分析技术，通过整合MRSI（磁共振光谱成像）和MRI（磁共振成像）的生物化学和形态学标志物，改善脑癌的非侵入性诊断。 MRSI允许生化代谢物的表征和定量以及代谢物强度图像的构建，与MRI结合提供了疾病的生化和形态学视图。 使用短MRSI回波时间技术，将研究10-20维多变量特征空间，以揭示表征癌症的特定特征。 具体目标包括：使用最大后验框架开发“半盲”源分离，用于恢复MRSI中的代谢物强度图像;表征代谢物强度图像与源自MRI的形态信息之间的相关性和依赖性;开发用于将代谢物强度图像与MRI整合的分层概率模型，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，与依赖于相当基本关系的传统方法进行比较，例如两种代谢物浓度的比率。该提案的教育部分侧重于生物医学工程的机器学习计划，包括一个新的课程和计算机实验室和努力，将作为一个工业实习计划的基础。 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，以及评估和验证其性能的技术。

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.