Brain-shift compensation by non-rigid registration of intra-operative ultrasound images with preoperative MR images based on residual complexity View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-05

AUTHORS

P. Farnia, A. Ahmadian, T. Shabanian, N. D. Serej, J. Alirezaie

ABSTRACT

PURPOSE: Compensation for brain shift is often necessary for image-guided neurosurgery, requiring registration of intra-operative ultrasound (US) images with preoperative magnetic resonance images (MRI). A new image similarity measure based on residual complexity (RC) to overcome challenges of registration of intra-operative US and preoperative MR images was developed and tested. METHOD: A new two-stage method based on the matching echogenic structures such as sulci is achieved by optimizing the residual complexity value in the wavelet domain between the ultrasound image and the probabilistic map of the MR image. The proposed method is a compromise between feature-based and intensity-based approaches. Evaluation was performed using a specially designed brain phantom and an in vivo patient data set. RESULT: The results of the phantom data set registration confirmed that the proposed objective function outperforms the accuracy of adapted RC for multi-modal cases by 48 %. The mean fiducial registration error reached 1.17 and 2.14 mm when the method was applied on phantom and clinical data sets, respectively. CONCLUSION: This improved objective function based on RC in the wavelet domain enables accurate non-rigid multi-modal (US and MRI) image registration which is robust to noise. This technology is promising for compensation of intra-operative brain shift in neurosurgery. More... »

PAGES

555-562

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11548-014-1098-5

DOI

http://dx.doi.org/10.1007/s11548-014-1098-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025672987

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24992912


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11548-014-1098-5'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11548-014-1098-5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11548-014-1098-5'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11548-014-1098-5'


 

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