  Directional detection of Dark Matter is a promising search strategy. However,
to perform such kind of detection, the recoiling tracks have to be accurately
reconstructed: direction, sense and position in the detector volume. In order
to optimize the track reconstruction and to fully exploit the data from the
MIMAC detector, we developed a likelihood method dedicated to the track
reconstruction. This likelihood approach requires a full simulation of track
measurements with MIMAC in order to compare real tracks to simulated ones.
Finally, we found that the MIMAC detector should have the required performance
to perform a competitive directional detection of Dark Matter.
