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This paper presents a new approach to inverse consistent image registration.
The idea of inverse consistency was first put forward by Thirion [14] and was generalized by Christensen and Johnson [42] in their inverse consistent image registration (ICIR) method, where symmetry is approximated by including a variational penalty term in the optimization algorithm.
However, in order to ensure that the coordinates of the points of the images in 2 phases were consistent, image registration was made only with the stable features around the surface mine as cognominal points while in the landslide area, unfortunately no such points can be used.
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Our segmentation algorithm consists of two image registration tasks.
The key idea was to use CT scans for image registration since CT includes more (consistent) information, and determining spatial correspondences between CT scans is relatively easier compared to PET-to-PET registration.
Image stitching consists of two processes which are image registration and image blending.
These results are also consistent with a previous study [16] showing that non-rigid intermodality image registration is a significant improvement over rigid registration for fusion between [18F]FDG PET and CT.
Image registration is crucial for CT perfusion imaging.
Consistent with a previous study [5] showing that the type of intermodality image registration had no significant impact on SUVmax, the present study showed that the type of intramodality registration had no significant impact on absolute TRT of SUVmax compared with delineating VOIs separately on both scans.
Despite the photoreceptor reflectance fluctuations [ 11, 41, 42] and other sources of variability such as image registration, tear film and subject alignment, the observed reflectance amplitude values (shown in Table 2 >) are consistent with the SCE predictions.
A fast diffeomorphic image registration algorithm.
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