Reconstruction of high-resolution tongue volumes from MRI.

Reconstruction of high-resolution tongue volumes from MRI.
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DOI:
10.1109/tbme.2012.2218246
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发表时间:
2012-12
期刊:
IEEE transactions on bio-medical engineering
影响因子:
--
通讯作者:
Prince JL
Prince JL
中科院分区:
其他
文献类型:
--
作者:
Woo J;Murano EZ;Stone M;Prince JL

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舌头的磁共振 (MR) 图像已用于临床研究和科学研究,以揭示舌头结构。为了提取舌头的不同特征及其与声道的关系,获取三个正交图像体积(例如轴向体积、矢状体积和冠状体积)是有益的。为了保持低噪声和高视觉细节,并最大限度地减少由于非自愿运动伪影造成的模糊效果,每组图像均以远优于平面分辨率的平面内分辨率采集。因此,任何一个数据集本身都不是自动体积分析(例如分割、配准和图谱构建)的理想选择,甚至在需要倾斜切片时也不是可视化的理想选择。本文提出了一种舌头超分辨率体积重建方法,该方法使用三个正交图像体积生成各向同性图像体积。该方法使用包括配准和强度匹配的预处理步骤以及通过马尔可夫随机场优化执行的具有边缘保留特性的数据组合方法。该方法的性能在十五个临床数据集上得到了证明,与视觉和定量评估的不同重建方法相比,保留了解剖细节并产生了优越的结果。
Magnetic resonance (MR) images of the tongue have been used in both clinical studies and scientific research to reveal tongue structure. In order to extract different features of the tongue and its relation to the vocal tract, it is beneficial to acquire three orthogonal image volumes—e.g., axial, sagittal, and coronal volumes. In order to maintain both low noise and high visual detail and minimize the blurred effect due to involuntary motion artifacts, each set of images is acquired with an in-plane resolution that is much better than the through-plane resolution. As a result, any one data set, by itself, is not ideal for automatic volumetric analyses such as segmentation, registration, and atlas building or even for visualization when oblique slices are required. This paper presents a method of super-resolution volume reconstruction of the tongue that generates an isotropic image volume using the three orthogonal image volumes. The method uses preprocessing steps that include registration and intensity matching and a data combination approach with the edge-preserving property carried out by Markov random field optimization. The performance of the proposed method was demonstrated on fifteen clinical datasets, preserving anatomical details and yielding superior results when compared with different reconstruction methods as visually and quantitatively assessed.