Zhang Xiaoxi, Johnson Timothy D, Little Roderick J A, Cao Yue
University of Michigan, Ann Arbor.
Ann Appl Stat. 2008 Jan 1;2(2):736-735. doi: 10.1214/07-AOAS157.
Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which, researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual's response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying pathological/physiological changes via qMRI, due to measurement errors or unpredictable influences. We use a hidden Markov Random Field to model the spatial dependence in the data and develop a maximum likelihood approach via the Expectation-Maximization algorithm with stochastic variation. An important improvement over previous work is the assessment of variability in parameter estimation, which is the valid basis for statistical inference. More importantly, we focus on the expected changes rather than image segmentation. Our research has shown that the approach is powerful in both simulation studies and on a real dataset, while quite robust in the presence of some model assumption violations.
定量磁共振成像（qMRI）帮助研究人员洞察活体组织的病理和生理变化，借助于此，研究人员希望早期预测（局部）治疗效果并确定最佳治疗方案。然而，qMRI的分析一直局限于临时启发式方法。我们的研究为图像分析提供了一个强大的统计框架，并为未来根据个体反应量身定制的局部自适应治疗方案提供了思路。我们假设在一个不完美的世界中，由于测量误差或不可预测的影响，我们只能通过qMRI观察到潜在病理/生理变化的模糊且有噪声的版本。我们使用隐马尔可夫随机场对数据中的空间依赖性进行建模，并通过带有随机变化的期望最大化算法开发一种最大似然方法。与先前工作相比的一个重要改进是对参数估计变异性的评估，这是统计推断的有效基础。更重要的是，我们关注的是预期变化而非图像分割。我们的研究表明，该方法在模拟研究和真实数据集上都很强大，并且在存在一些模型假设违背的情况下相当稳健。
