Regularization is an important aspect in high angular resolution diffusion imaging (HARDI), since, unlike with classical diffusion tensor imaging (DTI), there is no a priori regularity of raw data in the co-domain, i.e. considered as a multispectral signal for fixed spatial position. HARDI preprocessing is therefore a crucial step prior to any subsequent analysis, and some insight in regularization paradigms and their interrelations is compulsory. In this paper we posit a codomain scale space regularization paradigm that has hitherto not been applied in the context of HARDI. Unlike previous (first and second order) schemes it is based on infinite order regularization, yet can be fully operationalized. We furthermore establish a closed-form relation with first order Tikhonov regularization via the Laplace transform.
|Title of host publication||2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops 2008, Anchorage AK, USA, June 23-28, 2008)|
|Place of Publication||Piscataway NJ|
|Publisher||Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2008|