A CT-ultrasound-coregistered augmented reality enhanced image-guided surgery system and its preliminary study on brain-shift estimation

H. Huang, H. Hsieh, D. Lee*, C. Huang, T. Lee, T. Wu, N. Sun, T. Wu

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

6 Scopus citations

Abstract

With the combined view on the physical space and the medical imaging data, augmented reality (AR) visualization can provide perceptive advantages during image-guided surgery (IGS). However, the imaging data are usually captured before surgery and might be different from the up-to-date one due to natural shift of soft tissues. This study presents an AR-enhanced IGS system which is capable to correct the movement of soft tissues from the pre-operative CT images by using intra-operative ultrasound images. First, with reconstructing 2-D free-hand ultrasound images to 3-D volume data, the system applies a Mutual-Information based registration algorithm to estimate the deformation between pre-operative and intra-operative ultrasound images. The estimated deformation transform describes the movement of soft tissues and is then applied to the pre-operative CT images which provide high-resolution anatomical information. As a result, the system thus displays the fusion of the corrected CT images or the real-time 2-D ultrasound images with the patient in the physical space through a head mounted display device, providing an immersive augmented-reality environment. For the performance validation of the proposed system, a brain phantom was utilized to simulate brain-shift scenario. Experimental results reveal that when the shift of an artificial tumor is from 5mm ∼ 12mm, the correction rates can be improved from 32% ∼ 45% to 87% ∼ 95% by using the proposed system.

Original languageEnglish
Article numberP08016
JournalJournal of Instrumentation
Volume7
Issue number8
DOIs
StatePublished - 08 2012

Keywords

  • Algoritms and Software for radiotherapy
  • Calibration and fitting methods
  • Cluster finding
  • Medical-image reconstruction methods and algorithms
  • Multi-modality systems
  • Pattern recognition
  • computer-aided so

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