A Novel Richardson-Lucy Model with Dictionary Basis and Spatial Regularization for Isolating Isotropic Signals

PLoS One. 2017 Jan 12;12(1):e0168864. doi: 10.1371/journal.pone.0168864. eCollection 2017.


Diffusion-weighted magnetic resonance imaging is a non-invasive imaging method that has been increasingly used in neuroscience imaging over the last decade. Partial volume effects (PVEs) exist in sampling signal for many physical and actual reasons, which lead to inaccurate fiber imaging. We overcome the influence of PVEs by separating isotropic signal from diffusion-weighted signal, which can provide more accurate estimation of fiber orientations. In this work, we use a novel response function (RF) and the correspondent fiber orientation distribution function (fODF) to construct different signal models, in which case the fODF is represented using dictionary basis function. We then put forward a new index Piso, which is a part of fODF to quantify white and gray matter. The classic Richardson-Lucy (RL) model is usually used in the field of digital image processing to solve the problem of spherical deconvolution caused by highly ill-posed least-squares algorithm. In this case, we propose an innovative model integrating RL model with spatial regularization to settle the suggested double-models, which improve noise resistance and accuracy of imaging. Experimental results of simulated and real data show that the proposal method, which we call iRL, can robustly reconstruct a more accurate fODF and the quantitative index Piso performs better than fractional anisotropy and general fractional anisotropy.

MeSH terms

  • Animals
  • Diffusion Magnetic Resonance Imaging / methods*
  • Humans
  • Models, Theoretical*

Grant support

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61379020) and by the open foundation of Wenzhou Medical University (Grant No. LKFJ014). The first funders is the corresponding author, which has a important role in study design, data collection and analysis. The second funder provide the idea and analysis the data.