A New Method for Registration-Based Medical Image Interpolation

Abstract

Abstract-A new technique is presented for interpolating between grey-scale images in a medical data set. Registration between neighboring slices is achieved with a modified control grid interpolation algorithm that selectively accepts displacement field updates in a manner optimized for performance. A cubic interpolator is then applied to pixel intensities correlated by the displacement fields. Special considerations are made for efficiency, interpolation quality, and compression in the implementation of the algorithm. Experimental results show that the new method achieves good quality, while offering dramatic improvement in efficiency relative to the best competing method.

Quantitative Effects of Coil Packing Density on Cerebral Aneurysm Fluid Dynamics: An In Vitro Steady Flow Study

“…More information can be found in the literature. [9][10][11] RESULTS Consistent flow across the neck of the aneurysm was observed in the uncoiled models, as shown in Fig. 3 where vector plots corresponding to different parent vessel flow rates are overlaid on a wide-neck model.…”

“…For 3D streamtracing and visualization, a modified control grid interpolation (MCGI) was used to reconstruct isotropically resolved 3D velocities throughout the fluid domain spanned by each set of three acquired velocity data planes. 9 MCGI is an established algorithm for fluid flow velocity data reconstruction that performs with high accuracy while minimizing divergence errors. 10 Displacement fields linking similar fluid structures in neighboring velocity magnitude images were determined by minimizing:…”

Improving image registration by correspondence interpolation

“…There has been recent work on correspondence-based interpolation, e.g. [1,2]. Inspired by those, we demonstrated the importance of taking into account correspondence both ways, providing more robustness in the interpolation where correspondence between slices is poor [3].…”

Computer Image Registration Techniques Applied to Nuclear Medicine Images

“…When the interpolation complexity increases, the number of polynomial variables also increases and the smoothing effect can be more severe or even generate artefacts [14]. Recent interpolation methods between neighbouring image slices in grey-scale are based on B-splines [65], geometric multi-grid [66], using a modified control grid interpolation algorithm [67] or adaptive 2D autoregressive modelling and softdecision estimation [68]. The interpolation error can introduce modulations in the similarity measure used in the registration process, since the transformations involve pure translations of images with equal sampling spacing, and the period of the modulation is the same as the sampling spacing.…”