Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD.
Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston.
J Speech Lang Hear Res. 2023 Feb 13;66(2):513-526. doi: 10.1044/2022_JSLHR-22-00329. Epub 2023 Jan 30.
Muscle groups within the tongue in healthy and diseased populations show different behaviors during speech. Visualizing and quantifying strain patterns of these muscle groups during tongue motion can provide insights into tongue motor control and adaptive behaviors of a patient.
We present a pipeline to estimate the strain along the muscle fiber directions in the deforming tongue during speech production. A deep convolutional network estimates the crossing muscle fiber directions in the tongue using diffusion-weighted magnetic resonance imaging (MRI) data acquired at rest. A phase-based registration algorithm is used to estimate motion of the tongue muscles from tagged MRI acquired during speech. After transforming both muscle fiber directions and motion fields into a common atlas space, strain tensors are computed and projected onto the muscle fiber directions, forming so-called strains in the line of actions (SLAs) throughout the tongue. SLAs are then averaged over individual muscles that have been manually labeled in the atlas space using high-resolution T2-weighted MRI. Data were acquired, and this pipeline was run on a cohort of eight healthy controls and two glossectomy patients.
The crossing muscle fibers reconstructed by the deep network show orthogonal patterns. The strain analysis results demonstrate consistency of muscle behaviors among some healthy controls during speech production. The patients show irregular muscle patterns, and their tongue muscles tend to show more extension than the healthy controls.
The study showed visual evidence of correlation between two muscle groups during speech production. Patients tend to have different strain patterns compared to the controls. Analysis of variations in muscle strains can potentially help develop treatment strategies in oral diseases.
健康人群和患病人群的舌内肌肉群在言语时表现出不同的行为。可视化和量化这些肌肉群在舌运动过程中的应变模式，可以深入了解舌运动控制和患者的适应行为。
我们提出了一个在言语产生过程中估计变形舌内肌肉纤维方向应变的流水线。一个深度卷积网络使用在休息时采集的弥散加权磁共振成像（MRI）数据来估计舌内交叉肌肉纤维方向。基于相位的配准算法用于从在言语过程中采集的带标记的 MRI 估计舌肌运动。在将肌肉纤维方向和运动场转换到共同的图谱空间之后，计算应变张量并将其投影到肌肉纤维方向上，从而在整个舌上形成所谓的作用线上的应变（SLAs）。然后，使用高分辨率 T2 加权 MRI 在图谱空间中手动标记的各个肌肉上对 SLA 进行平均。数据被采集，该流水线在 8 名健康对照者和 2 名舌切除术患者的队列上运行。
由深度网络重建的交叉肌肉纤维呈现正交模式。应变分析结果表明，在言语产生过程中，一些健康对照者的肌肉行为具有一致性。患者表现出不规则的肌肉模式，他们的舌肌往往比健康对照者表现出更多的伸展。
该研究显示了在言语产生过程中两个肌肉群之间存在相关性的视觉证据。与对照组相比，患者往往具有不同的应变模式。肌肉应变的分析变化可能有助于开发口腔疾病的治疗策略。
