Chaari Lotfi, Forbes Florence, Vincent Thomas, Dojat Michel, Ciuciu Philippe
INRIA, MISTIS, Grenoble, France.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):260-8. doi: 10.1007/978-3-642-23629-7_32.
We address the issue of jointly detecting brain activity and estimating underlying brain hemodynamics from functional MRI data. We adopt the so-called Joint Detection Estimation (JDE) framework that takes spatial dependencies between voxels into account. We recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. It follows a new algorithm that has interesting advantages over the previously used intensive simulation methods (Markov Chain Monte Carlo, MCMC): tests on artificial data show that the VEM-JDE is more robust to model mis-specification while additional tests on real data confirm that it achieves similar performance in much less computation time.
我们探讨了从功能磁共振成像（fMRI）数据中联合检测大脑活动并估计潜在脑血流动力学的问题。我们采用了所谓的联合检测估计（JDE）框架，该框架考虑了体素之间的空间依赖性。我们将JDE重新构建为一个缺失数据框架，并推导了一种变分期望最大化（VEM）算法用于其推理。它遵循一种新算法，与先前使用的密集模拟方法（马尔可夫链蒙特卡罗，MCMC）相比具有有趣的优势：对人工数据的测试表明，VEM-JDE对模型错误指定更具鲁棒性，而对真实数据的额外测试证实，它在更短的计算时间内实现了类似的性能。
