Results

The CDMRI workshop was successfully held on September 18th 2014. There were mainly seven multi-shell estimation methods and six single-shell estimation methods participated in the challenge. The results are available in the attached file and are briefly summarized in below.

Muti-Shell Challenge:

The methods that participated the multi-shell challenge are listed as follows:

  • Spherical Fourier-Bessel (SFB)
  • Spherical Finite Rate of Innovation (SFRI)
  • 3D-SHORE          
    (a) Positive EAP, Laplace-Beltrami
    (b) Laplacian, Non-Local Means
  • MAP with Laplacian regularization
  • Constrained Spherical Deconvolution (CSD)
    (a)  Non-Local Means (NLM)           
    (b)  Non-Local Spatial and Angular Matching (NLSAM)
  • Sharpening Deconvolution Transform (SDT)
    (a)  Non-Local Means (NLM)
    (b)  Non-Local Spatial and Angular Matching (NLSAM)
  • Self-Adjusted (SA) basis
  • Directional Radial Basis (DRB) function
  • Spherical Ridgelets with Radial decay (DRR)

We have collected method-wise comparisons and self-comparisons for each method with different number of measurements. The main conclusions are as follows:

  • Some of the methods (e.g. MAP) perform extremely well in terms of signal fitting (NMSE), however perform sub-optimally in terms of angular error !
  • Other methods perform well in terms of angular error, but not so well in terms of signal fitting (RTOP) !

Single-Shell Challenge: 

The methods that participated the single-shell challenge are:

  • Spherical Finite Rate of Innovation (SFRI)
  • Fiber Orientation Distribution (FOD) using non-negative sparse recovery.
  • Constrained Spherical Deconvolution (CSD)
    (a) Non-Local Means (NLM)
    (b) Non-Local Spatial and Angular Matching (NLSAM)
  • Sharpening Deconvolution Transform (SDT)           
    (a) Non-Local Means (NLM)
    (b) Non-Local Spatial and Angular Matching (NLSAM)
  • Self-Adjusted (SA) Basis
    (a) Non-Local Means (NLM)
    (b) Non-Local Spatial and Angular Matching (NLSAM)
  • Anonymous method
  • Spherical Ridgelets (SR)

The conclusions for single-shell challenge are:

  • If connectivity analysis is your only goal and you have time to acquire only 30 directions, then use SFRI method with a b-value of 3000 (about 10% error in signal fit).
  • At b=2000, and 30 gradient directions, almost all methods do well (except Sharpening Deconvolution Transform-NLM).
  • For b=2000, and 60 gradient directions, CSD methods do very well.

Now, the gold-standard data has been released! All participants could refine their algorithms from this data. Please do so and send us the updated results.