Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Deformable MRI-Ultrasound Registration via Attribute Matching and Mutual-Saliency Weighting for Image-Guided Neurosurgery

Institution:
1Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
2Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
MICCAI 2018
Publication Date:
Sep-2018
Pages:
165-71
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2018 Sep;20(WS). Workshop on Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation (2018). 2018 Sep;165-71.
Keywords:
Brain shift, Intraoperative ultrasound, Image registration, Attribute matching, Gabor filter bank , Mutual-saliency
Appears in Collections:
NCIGT, SPL
Generated Citation:
Machado I., Toews M., Luo J., Unadkat P., Essayed W., George E., Teodoro P., Carvalho H., Martins J., Golland P., Pieper S., Frisken S., Golby A., Wells III W.M., Ou Y. Deformable MRI-Ultrasound Registration via Attribute Matching and Mutual-Saliency Weighting for Image-Guided Neurosurgery. Int Conf Med Image Comput Comput Assist Interv. 2018 Sep;20(WS). Workshop on Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation (2018). 2018 Sep;165-71.
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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