MRI non-uniformity correction through interleaved bias estimation and B-spline deformation with a template.
MRI non-uniformity correction through interleaved bias estimation and B-spline deformation with a template.
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DOI：
10.1109/embc.2012.6345882
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Decarli C
中科院分区：
文献类型：
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作者：
Fletcher E;Carmichael O;Decarli C
我们提出了一种基于模板的方法来校正人脑磁共振图像（MRI）中的场不均匀性偏差。在每次算法迭代中，无偏模板图像和主体图像之间的 B 样条变形的更新与基于当前模板到图像对准的偏差场的估计交织在一起。基于变形模板和主体图像之间的局部图像块强度平均值的比率，使用空间平滑的薄板样条插值对偏置场进行建模。这用于迭代地校正对象图像强度，然后用于改进模板到图像的变形。对患有和不患有阿尔茨海默病的图像的合成数据集和真实数据集进行的实验表明，该方法可能比流行的 N3 技术更有优势，可用于建模偏差场和缩小灰质、白质和脑脊液的强度范围。这种偏置场校正方法有可能比仅基于固有图像属性或假设图像强度分布的校正方案更准确。
We propose a template-based method for correcting field inhomogeneity biases in magnetic resonance images (MRI) of the human brain. At each algorithm iteration, the update of a B-spline deformation between an unbiased template image and the subject image is interleaved with estimation of a bias field based on the current template-to-image alignment. The bias field is modeled using a spatially smooth thin-plate spline interpolation based on ratios of local image patch intensity means between the deformed template and subject images. This is used to iteratively correct subject image intensities which are then used to improve the template-to-image deformation. Experiments on synthetic and real data sets of images with and without Alzheimer’s disease suggest that the approach may have advantages over the popular N3 technique for modeling bias fields and narrowing intensity ranges of gray matter, white matter, and cerebrospinal fluid. This bias field correction method has the potential to be more accurate than correction schemes based solely on intrinsic image properties or hypothetical image intensity distributions.