Ye Chuyang, Murano Emi, Stone Maureen, Prince Jerry L
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Comput Med Imaging Graph. 2015 Oct;45:63-74. doi: 10.1016/j.compmedimag.2015.07.005. Epub 2015 Jul 21.
The tongue is a critical organ for a variety of functions, including swallowing, respiration, and speech. It contains intrinsic and extrinsic muscles that play an important role in changing its shape and position. Diffusion tensor imaging (DTI) has been used to reconstruct tongue muscle fiber tracts. However, previous studies have been unable to reconstruct the crossing fibers that occur where the tongue muscles interdigitate, which is a large percentage of the tongue volume. To resolve crossing fibers, multi-tensor models on DTI and more advanced imaging modalities, such as high angular resolution diffusion imaging (HARDI) and diffusion spectrum imaging (DSI), have been proposed. However, because of the involuntary nature of swallowing, there is insufficient time to acquire a sufficient number of diffusion gradient directions to resolve crossing fibers while the in vivo tongue is in a fixed position. In this work, we address the challenge of distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging by using a multi-tensor model with a fixed tensor basis and incorporating prior directional knowledge. The prior directional knowledge provides information on likely fiber directions at each voxel, and is computed with anatomical knowledge of tongue muscles. The fiber directions are estimated within a maximum a posteriori (MAP) framework, and the resulting objective function is solved using a noise-aware weighted ℓ1-norm minimization algorithm. Experiments were performed on a digital crossing phantom and in vivo tongue diffusion data including three control subjects and four patients with glossectomies. On the digital phantom, effects of parameters, noise, and prior direction accuracy were studied, and parameter settings for real data were determined. The results on the in vivo data demonstrate that the proposed method is able to resolve interdigitated tongue muscles with limited gradient directions. The distributions of the computed fiber directions in both the controls and the patients were also compared, suggesting a potential clinical use for this imaging and image analysis methodology.
舌头是一个执行多种功能的关键器官，包括吞咽、呼吸和言语功能。它包含内在肌和外在肌，这些肌肉在改变舌头形状和位置方面发挥着重要作用。扩散张量成像（DTI）已被用于重建舌肌纤维束。然而，先前的研究无法重建舌肌相互交错处出现的交叉纤维，而交叉纤维在舌体积中占很大比例。为了解决交叉纤维问题，人们提出了基于DTI的多张量模型以及更先进的成像方式，如高角分辨率扩散成像（HARDI）和扩散谱成像（DSI）。然而，由于吞咽的不自主性，在活体舌头处于固定位置时，没有足够的时间获取足够数量的扩散梯度方向来解析交叉纤维。在这项工作中，我们通过使用具有固定张量基的多张量模型并纳入先验方向知识，来应对从有限的扩散磁共振成像中区分交错舌肌的挑战。先验方向知识提供了每个体素处可能的纤维方向信息，并根据舌肌的解剖学知识进行计算。纤维方向在最大后验概率（MAP）框架内进行估计，并使用噪声感知加权ℓ1范数最小化算法求解由此产生的目标函数。我们对数字交叉模型以及包括三名对照受试者和四名舌切除患者的活体舌头扩散数据进行了实验。在数字模型上，研究了参数、噪声和先验方向准确性的影响，并确定了实际数据的参数设置。活体数据的结果表明，所提出的方法能够在有限的梯度方向下解析交错的舌肌。我们还比较了对照受试者和患者中计算出的纤维方向分布，这表明这种成像和图像分析方法具有潜在的临床应用价值。
