Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Direct Estimation of the Fiber Orientation Density Function from Diffusion-Weighted MRI Data using Spherical Deconvolution. Medical Image Analysis 12(1), 26–41 (2008)ĭhollander, T., Van Hecke, W., Maes, F., Sunaert, S., Suetens, P.: Spatial Transformations of High Angular Resolution Diffusion Imaging Data in Q-space.
#QSPACE 2011 REGISTRATION#
NeuroImage 45(1), S61–S72 (2009)Īvants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic Demons: Efficient Non-parametric Image Registration. Wu, Y.C., Alexander, A.L.: Hybrid Diffusion Imaging. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. We hereby demonstrate the feasibility of q-space atlas building, as well as the quality, advantages and possibilities of such an atlas. From the resulting atlas, we also reconstructed some advanced models.
We applied our method to HYDI data of 10 healthy subjects. 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. Therefore, it would be nice if we were able to perform atlas building before model reconstruction. Methods for DWI atlas construction (and registration) are as plenty as the number of available models. A hybrid diffusion imaging (HYDI) approach even allows to reconstruct different models and measures from a single dataset. In the field of diffusion weighted imaging (DWI), it is common to fit one of many available models to the acquired data.
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