Multiple-correlation similarity for block-matching based fast CT to ultrasound registration in liver interventions

Jyotirmoy Banerjee, Yuanyuan Sun, Camiel Klink, Renske Gahrmann, Wiro J. Niessen, Adriaan Moelker, Theo van Walsum

    Research output: Contribution to journalArticleScientificpeer-review

    2 Citations (Scopus)

    Abstract

    In this work we present a fast approach to perform registration of computed tomography to ultrasound volumes for image guided intervention applications. The method is based on a combination of block-matching and outlier rejection. The block-matching uses a correlation based multimodal similarity metric, where the intensity and the gradient of the computed tomography images along with the ultrasound volumes are the input images to find correspondences between blocks in the computed tomography and the ultrasound volumes. A variance and octree based feature point-set selection method is used for selecting distinct and evenly spread point locations for block-matching. Geometric consistency and smoothness criteria are imposed in an outlier rejection step to refine the block-matching results. The block-matching results after outlier rejection are used to determine the affine transformation between the computed tomography and the ultrasound volumes. Various experiments are carried out to assess the optimal performance and the influence of parameters on accuracy and computational time of the registration. A leave-one-patient-out cross-validation registration error of 3.6 mm is achieved over 29 datasets, acquired from 17 patients.

    Original languageEnglish
    Pages (from-to)132-141
    JournalMedical Image Analysis
    Volume53
    DOIs
    Publication statusPublished - 2019

    Keywords

    • 3D
    • CT
    • Liver
    • Multimodal
    • Registration
    • Ultrasound

    Fingerprint Dive into the research topics of 'Multiple-correlation similarity for block-matching based fast CT to ultrasound registration in liver interventions'. Together they form a unique fingerprint.

    Cite this