A Bayesian Approach to Distinguishing Interdigitated Muscles in the Tongue from Limited Diffusion Weighted Imaging
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 weighted ℓ1-norm minimization. Experiments on a digital phantom and in vivo tongue diffusion data demonstrate that the proposed method is able to resolve crossing fibers with limited gradient directions.
KeywordsDiffusion imaging weighted ℓ1-norm minimization prior directional knowledge
Unable to display preview. Download preview PDF.
- 9.Kim, S.-J., Koh, K., Lustig, M., Boyd, S.: An efficient method for compressed sensing. In: IEEE International Conference on Image Processing, ICIP 2007, vol. 3, pp. 111–117. IEEE (2007)Google Scholar
- 15.Daducci, A., Van De Ville, D., Thiran, J.P., Wiaux, Y.: Reweighted sparse deconvolution for high angular resolution diffusion MRI. arXiv preprint arXiv:1208.2247 (2012)Google Scholar