Deformable MRI-ultrasound registration via attribute matching and mutual-saliency weighting for image-guided neurosurgery

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Abstract

Intraoperative brain deformation reduces the effectiveness of using preoperative images for intraoperative surgical guidance. We propose an algorithm for deformable registration of intraoperative ultrasound (US) and preoperative magnetic resonance (MR) images in the context of brain tumor resection. From each image voxel, a set of multi-scale and multi-orientation Gabor attributes is extracted from which optimal components are selected to establish a distinctive morphological signature of the anatomical and geometric context of its surroundings. To match the attributes across image pairs, we assign higher weights – higher mutual-saliency values - to those voxels more likely to establish reliable correspondences across images. The correlation coefficient is used as the similarity measure to evaluate effectiveness of the algorithm for multi-modal registration. Free-form deformation and discrete optimization are chosen as the deformation model and optimization strategy, respectively. Experiments demonstrate our methodology on registering preoperative T2-FLAIR MR to intraoperative US in 22 clinical cases. Using manually labelled corresponding landmarks between preoperative MR and intraoperative US images, we show that the mean target registration error decreases from an initial value of 5.37 ± 4.27 mm to 3.35 ± 1.19 mm after registration.

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Machado, I., Toews, M., Luo, J., Unadkat, P., Essayed, W., George, E., … Ou, Y. (2018). Deformable MRI-ultrasound registration via attribute matching and mutual-saliency weighting for image-guided neurosurgery. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11042 LNCS, pp. 165–171). Springer Verlag. https://doi.org/10.1007/978-3-030-01045-4_20

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