TU-AB-202-04: Development of a DeformableImage Registration (DIR) Error Correction Method Employing Kolmogorov-Zurbenko(KZ) Filter.
PURPOSE
This study aims to develop a DIR error correction method capable of utilizing sparse ground-truth motion information and recovering missing data with the Kolmogorov-Zurbenko (KZ) filter.


METHODS
The error correction method employs a two-step approach. First, sparse ground-truth displacement vectors are integrated into a pre-correction deformable vector field (DVF) to estimate a post-correction DVF with coarse resolution. Second, the coarse post-correction DVF is boosted to a full-resolution DVF through convolution with the KZ filter. To validate the use of the KZ filter for missing data recovery, recovery errors were determined by comparing a DVF recovered from down-sampling with the original full-resolution DVF. The entire error correction method was tested on an in-house developed digital lung motion phantom consisting of a primary volume, a DVF, and a secondary volume synthesized by applying the DVF on the primary volume. Five pre-correction DVFs were obtained by performing DIR between the two volumes using Velocity, MIM, ILK and OHS algorithms in DIRART toolbox, and Elastix, and then corrected. Primary volumes were synthesized with pre- and post-correction DVFs, respectively. The error correction method was evaluated with pre- and post-correction registration errors, and intensity errors in synthesized primary volumes.


RESULTS
Our test results for sparsely down-sampled (<0.4%) DVFs showed that the KZ filter outperformed the cubic polynomial interpolation method for whole lung DVF map recovery in terms of median error (0.60mm vs 0.73mm) and mean error (1.18mm vs 1.29mm). Pre- and post-correction 3D registration errors per voxel for Velocity, MIM, ILK, OHS, and Elastix are reduced by 2.39 mm on average. Pre- and post-correction intensity errors are reduced by 0.37 in unit of image intensity on average.


CONCLUSION
We have implemented a two-step method capable of utilizing sparse ground-truth displacement vectors for DIR error reduction, allowing DIR accuracy improvement utilizing clinically available motion data. This study is supported by NIH grant 1R21CA165384.