Short introduction

COVIPER is an academic software for correcting signal-dropout effects due to patient table vibrations. The basic idea of COVIPER is to optimally combine two images with the same diffusion weighting but different patient-table-vibration-related signal-dropouts (for details see Mohammadie et al. (2012)). The COVIPER toolbox is written by Siawoosh Mohammadi.

Use COVIPER

Pre-processing

• Step 1: Use EC and motion correction toolbox to correct EC and motion artefacts for blip-up and blip-down data. The EC and motion corrected images will have a prefix “r”.
• Step 2: To refine the overlap between blip-up and blip-down DTI data, perform another 6-parameter affine registration (e.g. using spm_coreg) between unwarped blip-up and blip-down image (use e.g. the first in each DTI data series). The registered images will have another prefix “r”.
• Step 3: Use HySCo to unwrap the registered blip-up and blip-down data. The unwarped images will have a prefix “u”.

Main processsing

• Define in matlab the bvecs (3xN matrix) and bvals (1xN vector) variables before running the COVIPER toolbox, where “N” is the number of images in the DTI sequence. The “i-th” bvec column and bval component must correspond to the vector of the diffusion gradient and the b-value of the “i-th” image in the DTI dataset. (In case the b-value for the low-b-value images is unknown, type b=1, and if the diffusion gradient direction is unknown, type a random direction, which is normalised to 1.)
• Load pre-processed blip-up und blip-down images, the bvecs, and bvals (defined in previous step).

Referencing

Mohammadi S, Nagy Z, Hutton C, Josephs O, Weiskopf N. Correction of vibration artifacts in DTI using phase–encoding reversal (COVIPER). Magn Res Med 2012; 68: 882–889; doi: 10.1002/mrm.24467.

(Please cite this paper when using this toolbox)