title: A Bayesian Approach to Distinguishing Interdigitated Muscles in the Tongue from Limited Diffusion Weighted Imaging.
abstract: Fiber tracking in crossing regions is a well known issue in diffusion tensor imaging (DTI). Multi-tensor models have been proposed to cope with the issue. However, in cases where only a limited number of gradient directions can be acquired, for example in the tongue, the multi-tensor models fail to resolve the crossing correctly due to insufficient information. In this work, we address this challenge by using a fixed tensor basis and incorporating prior directional knowledge. Within a maximum a posteriori (MAP) framework, sparsity of the basis and prior directional knowledge are incorporated in the prior distribution, and data fidelity is encoded in the likelihood term. An objective function can then be obtained and solved using a noise-aware weightedℓ1-norm minimization. Experiments on a digital phantom andin vivotongue diffusion data demonstrate that the proposed method is able to resolve crossing fibers with limited gradient directions.
authors.affiliation: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Chuyang
authors.initials: C
authors.lastname: Ye
authors.affiliation: Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Aaron
authors.initials: A
authors.lastname: Carass
authors.affiliation: Department Of Neural And Pain Sciences, University Of Maryland School Of Dentistry, Baltimore, Md, USA.
authors.forename: Emi
authors.initials: E
authors.lastname: Murano
authors.forename: Maureen
authors.initials: M
authors.lastname: Stone
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
keywords: diffusion imaging; prior directional knowledge; weighted ℓ1-norm minimization