A Bayesian approach to distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging

	A Bayesian approach to distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging
	Ye, Chuyang1,2 ; Murano, Erni 3; Stone, Maureen 4; Prince, Jerry L.2
刊名	COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
	2015-10-01
卷号	45 页码:63-74
关键词	Diffusion magnetic resonance imaging Limited gradient directions Sparse reconstruction Prior directional knowledge Interdigitated tongue muscles
英文摘要	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 l(1)-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. (C) 2015 Elsevier Ltd. All rights reserved.
WOS标题词	Science & Technology ; Technology ; Life Sciences & Biomedicine
类目[WOS]	Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
研究领域[WOS]	Engineering ; Radiology, Nuclear Medicine & Medical Imaging
关键词[WOS]	TENSOR MRI ; IN-VIVO ; TRACTOGRAPHY ; ARCHITECTURE ; BRAIN ; MODEL ; MYOARCHITECTURE ; SEGMENTATION ; RESOLUTION ; TRACTS
收录类别	SCI
语种	英语
WOS记录号	WOS:000364891200007
公开日期	2016-02-26
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/10512]
专题	自动化研究所_脑网络组研究中心
作者单位	1.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing, Peoples R China 2.Johns Hopkins Univ, Dept Elect & Comp Engn, Baltimore, MD 21218 USA 3.Johns Hopkins Univ, Sch Med, Dept Radiol & Radiol Sci, Baltimore, MD USA 4.Univ Maryland, Sch Dent, Dept Neural & Pain Sci, Baltimore, MD 21201 USA
推荐引用方式 GB/T 7714	Ye, Chuyang,Murano, Erni,Stone, Maureen,et al. A Bayesian approach to distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging[J]. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS,2015,45:63-74.
APA	Ye, Chuyang,Murano, Erni,Stone, Maureen,&Prince, Jerry L..(2015).A Bayesian approach to distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging.COMPUTERIZED MEDICAL IMAGING AND GRAPHICS,45,63-74.
MLA	Ye, Chuyang,et al."A Bayesian approach to distinguishing interdigitated tongue muscles from limited diffusion magnetic resonance imaging".COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 45(2015):63-74.