
This paper presents a novel affine registration algorithm for diffusion tensor images. The proposed metric derived from the standpoint of diffusion profiles not only has concrete physical underpinning but also can be extended for comparing higher-order diffusion models. The non-translational part of the affine transformation is parametrized in the spirit of the Polar Decomposition Theorem. The registration objective function and its derivatives are derived analytically by combining this parametrization scheme with finite strain tensor reorientation. The affine algorithm is embeded in a multi-resolution piecewise affine framework for non-rigid registration.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 12 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
