Better Fiber ODFs From Suboptimal Data With Autoencoder Based Regularization

Kanil Patel, Samuel Gröschel, and Thomas Schultz
In proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Springer, 2018
 

Abstract

We propose a novel way of estimating fiber orientation distribution functions (fODFs) from diffusion MRI. Our method combines convex optimization with unsupervised learning in a way that preserves the relative benefits of both. In particular, we regularize constrained spherical deconvolution (CSD) with a prior that is derived from an fODF autoencoder, effectively encouraging solutions that are similar to fODFs observed in high-quality training data. Our method improves results on independent test data, especially when only few measurements or relatively weak diffusion weighting (low b values) are available.

Images

Additional Material

Bibtex

@INPROCEEDINGS{Patel:MICCAI2018,
     author = {Patel, Kanil and Gr{\"o}schel, Samuel and Schultz, Thomas},
      title = {Better Fiber ODFs From Suboptimal Data With Autoencoder Based Regularization},
  booktitle = {Medical Image Computing and Computer Assisted Intervention (MICCAI)},
       year = {2018},
  publisher = {Springer},
       note = {Accepted for publication.},
   abstract = {We propose a novel way of estimating fiber orientation distribution functions (fODFs) from diffusion
               MRI. Our method combines convex optimization with unsupervised learning in a way that preserves the
               relative benefits of both. In particular, we regularize constrained spherical deconvolution (CSD)
               with a prior that is derived from an fODF autoencoder, effectively encouraging solutions that are
               similar to fODFs observed in high-quality training data. Our method improves results on independent
               test data, especially when only few measurements or relatively weak diffusion weighting (low $b$
               values) are available.}
}