Versatile, robust, and efficient tractography with constrained higher-order tensor fODFs

Michael Ankele, Lek-Heng Lim, Samuel Gröschel, and Thomas Schultz
In: International Journal of Computer Assisted Radiology and Surgery (2017)
 

Abstract

Purpose. Develop a multi-fiber tractography method that produces fast and robust results based on input data from a wide range of diffusion MRI protocols, including high angular resolution diffusion imaging, multi-shell imaging, and clinical diffusion spectrum imaging (DSI)

Methods. In a unified deconvolution framework for different types of diffusion MRI protocols, we represent fiber orientation distribution functions as higher-order tensors, which permits use of a novel positive definiteness constraint (H-psd) that makes estimation from noisy input more robust. The resulting directions are used for deterministic fiber tracking with branching.

Results. We quantify accuracy on simulated data, as well as condition numbers and computation times on clinical data. We qualitatively investigate the benefits when processing suboptimal data, and show direct comparisons to several state-of-the-art techniques.

Conclusion. The proposed method works faster than state-of-the-art approaches, achieves higher angular resolution on simulated data with known ground truth, and plausible results on clinical data. In addition to working with the same data as previous methods for multi-tissue deconvolution, it also supports DSI data.

Images

Download Paper

Download Paper

Bibtex

@ARTICLE{Ankele:IJCARS2017,
    author = {Ankele, Michael and Lim, Lek-Heng and Gr{\"o}schel, Samuel and Schultz, Thomas},
     title = {Versatile, robust, and efficient tractography with constrained higher-order tensor fODFs},
   journal = {International Journal of Computer Assisted Radiology and Surgery},
      year = {2017},
      note = {Early view},
  abstract = {Purpose. Develop a multi-fiber tractography method that produces fast and robust results based on
              input data from a wide range of diffusion MRI protocols, including high angular resolution diffusion
              imaging, multi-shell imaging, and clinical diffusion spectrum imaging (DSI)
              
              Methods. In a unified deconvolution framework for different types of diffusion MRI protocols, we
              represent fiber orientation distribution functions as higher-order tensors, which permits use of a
              novel positive definiteness constraint (H-psd) that makes estimation from noisy input more robust.
              The resulting directions are used for deterministic fiber tracking with branching.
              
              Results. We quantify accuracy on simulated data, as well as condition numbers and computation times
              on clinical data. We qualitatively investigate the benefits when processing suboptimal data, and
              show direct comparisons to several state-of-the-art techniques.
              
              Conclusion. The proposed method works faster than state-of-the-art approaches, achieves higher
              angular resolution on simulated data with known ground truth, and plausible results on clinical
              data. In addition to working with the same data as previous methods for multi-tissue deconvolution,
              it also supports DSI data.},
       doi = {10.1007/s11548-017-1593-6}
}