Markov Chain Monte Carlo Random Effects Modeling in Magnetic Resonance Image Processing Using the BRugs Interface to WinBUGS
Markov Chain Monte Carlo Random Effects Modeling in Magnetic Resonance Image Processing Using the BRugs Interface to WinBUGS
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DOI：
10.18637/jss.v044.i02
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发表时间：
2011-10
影响因子：
5.8
通讯作者：
M. King;F. Calamente;C. Clark;D. Gadian
中科院分区：
文献类型：
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作者：
M. King;F. Calamente;C. Clark;D. Gadian
许多磁共振图像(MRI)数据处理方法的共同特征是执行处理的逐体素(体素是体积元素)方式。然而，通常情况下，MRI数据将显示出一定程度的空间相关性，使得独立体素处理在使用数据方面效率低下。贝叶斯随机效应模型由于其信息借用行为，预计将更加有效。为了说明贝叶斯随机效应方法，本文概述了对一个灌注MRI数据集的马尔可夫链蒙特卡罗(MCMC)分析，并使用Brugs程序包在R中实现。Brugs提供了到WinBUGS及其GeoBUGS附加组件的接口。WinBUGS是一个广泛使用的程序，用于执行MCMC分析，重点是贝叶斯随机效应模型。演示了体素(限于感兴趣区域)和多个对象的同时建模。尽管磁共振信号强度数据的信噪比很低，但仍获得了有用的模型信号强度剖面。通过与基于感兴趣区域平均和重复独立体素分析的其他方法的比较，讨论了随机效果建模的优点。为了说明这一点，本文主要以磁共振灌注成像为例，其主要观点是随机效应模型有望在信噪比是限制因素的许多其他MRI应用中受益。
A common feature of many magnetic resonance image (MRI) data processing methods is the voxel-by-voxel (a voxel is a volume element) manner in which the processing is performed. In general, however, MRI data are expected to exhibit some level of spatial correlation, rendering an independent-voxels treatment inefficient in its use of the data. Bayesian random effect models are expected to be more efficient owing to their information-borrowing behaviour. To illustrate the Bayesian random effects approach, this paper outlines a Markov chain Monte Carlo (MCMC) analysis of a perfusion MRI dataset, implemented in R using the BRugs package. BRugs provides an interface to WinBUGS and its GeoBUGS add-on. WinBUGS is a widely used programme for performing MCMC analyses, with a focus on Bayesian random effect models. A simultaneous modeling of both voxels (restricted to a region of interest) and multiple subjects is demonstrated. Despite the low signal-to-noise ratio in the magnetic resonance signal intensity data, useful model signal intensity profiles are obtained. The merits of random effects modeling are discussed in comparison with the alternative approaches based on region-of-interest averaging and repeated independent voxels analysis. This paper focuses on perfusion MRI for the purpose of illustration, the main proposition being that random effects modeling is expected to be beneficial in many other MRI applications in which the signal-to-noise ratio is a limiting factor.