Feasibility and Advantages of Diffusion Weighted Imaging Atlas Construction in Q-Space
In the field of diffusion weighted imaging (DWI), it is common to fit one of many available models to the acquired data. A hybrid diffusion imaging (HYDI) approach even allows to reconstruct different models and measures from a single dataset. Methods for DWI atlas construction (and registration) are as plenty as the number of available models. Therefore, it would be nice if we were able to perform atlas building before model reconstruction.
In this work, we present a method for atlas construction of DWI data in q-space: we developed a new multi-subject multi-channel diffeomorphic matching algorithm, which is combined with a recently proposed DWI retransformation method in q-space.
We applied our method to HYDI data of 10 healthy subjects. From the resulting atlas, we also reconstructed some advanced models. We hereby demonstrate the feasibility of q-space atlas building, as well as the quality, advantages and possibilities of such an atlas.
KeywordsTract Volume Spherical Harmonic Representation Mean Kurtosis Constrain Spherical Deconvolution Spherical Deconvolution
- 2.Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic Demons: Efficient Non-parametric Image Registration. NeuroImage 45(1), S61–S72 (2009)Google Scholar
- 4.Dhollander, T., Van Hecke, W., Maes, F., Sunaert, S., Suetens, P.: Spatial Transformations of High Angular Resolution Diffusion Imaging Data in Q-space. In: MICCAI 13, CDMRI Workshop, pp. 73–83 (2010)Google Scholar
- 9.Descoteaux, M., Deriche, R., Le Bihan, D., Mangin, J.F., Poupon, C.: Multiple Q-shell Diffusion Propagator Imaging. Medical Image Analysis (2010) (in press)Google Scholar