Seshamani Sharmishtaa, Cheng Xi, Fogtmann Mads, Thomason Moriah E, Studholme Colin
Biomedical Image Computing Group, Departments of Pediatrics, Bioengineering, and Radiology, University of Washington, Seattle, WA 98195, USA.
Biomedical Image Computing Group, Departments of Pediatrics, Bioengineering, and Radiology, University of Washington, Seattle, WA 98195, USA.
Med Image Anal. 2014 Feb;18(2):285-300. doi: 10.1016/j.media.2013.10.011. Epub 2013 Nov 6.
This paper presents a method for intensity inhomogeniety removal in fMRI studies of a moving subject. In such studies, subtle changes in signal as the subject moves in the presence of a bias field can be a significant confound for BOLD signal analysis. The proposed method avoids the need for a specific tissue model or assumptions about tissue homogeneity by making use of the multiple views of the underlying bias field provided by the subject's motion. A parametric bias field model is assumed and a regression model is used to estimate the basis function weights of this model. Quantitative evaluation of the effects of motion and noise in motion estimates are performed using simulated data. Results demonstrate the strength and robustness of the new method compared to the state of the art 4D nonparametric bias estimator (N4ITK). We also qualitatively demonstrate the impact of the method on resting state neuroimage analysis of a moving adult brain with simulated motion and bias fields, as well as on in vivo moving fetal fMRI.
本文提出了一种在运动受试者的功能磁共振成像（fMRI）研究中去除强度不均匀性的方法。在这类研究中，当受试者在存在偏置场的情况下移动时，信号的细微变化可能会对血氧水平依赖（BOLD）信号分析造成重大干扰。所提出的方法通过利用受试者运动提供的潜在偏置场的多个视图，避免了对特定组织模型的需求或关于组织均匀性的假设。假设了一个参数化偏置场模型，并使用回归模型来估计该模型的基函数权重。使用模拟数据对运动和运动估计中的噪声影响进行了定量评估。结果表明，与现有技术的4D非参数偏置估计器（N4ITK）相比，新方法具有优势和稳健性。我们还定性地展示了该方法对具有模拟运动和偏置场的运动成人脑静息态神经图像分析的影响，以及对体内运动胎儿fMRI的影响。
