Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization

PLoS One. 2015 Oct 15;10(10):e0138910. doi: 10.1371/journal.pone.0138910. eCollection 2015.

Abstract

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel MRI signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in human brain data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Diffusion Magnetic Resonance Imaging*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Imaging, Three-Dimensional
  • Models, Theoretical*
  • Normal Distribution
  • Phantoms, Imaging
  • Signal-To-Noise Ratio
  • Spatial Analysis
  • White Matter / cytology

Grants and funding

This work was supported by the Catalonian Government (2014SGR1573), several grants from the Plan Nacional de I+D+i and co-funded by the Instituto de Salud Carlos III-Subdirección General de Evaluación y Fomento de la Investigación and the European Regional Development Fund (FEDER): Miguel Servet Research Contracts (CP10/00596 to EP-C, CP13/00018 to RS and CP14/00041 to JR) and Research Project Grants (PI14/00292 to JR, PI14/01148 to EP-C and PI14/01151 to RS). The funding organizations played no role in the study design, data collection and analysis, or manuscript approval.