title: A Bayesian approach to distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging.
abstract: 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.
authors: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. Electronic address: chuyang.ye@nlpr.ia.ac.cn.
authors: Chuyang
authors: C
authors: Ye
authors: Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors: Emi
authors: E
authors: Murano
authors: Department of Neural and Pain Sciences, University of Maryland School of Dentistry, Baltimore, MD, USA.
authors: Maureen
authors: M
authors: Stone
authors: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
authors: Jerry L
authors: JL
authors: Prince
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D018482:Muscle, Skeletal / Q000033:anatomy & histology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014059:Tongue / Q000033:anatomy & histology*
keywords: Diffusion magnetic resonance imaging; Interdigitated tongue muscles; Limited gradient directions; Prior directional knowledge; Sparse reconstruction