A fast and efficient method to compensate for brain shift for tumor resection therapies measured between preoperative and postoperative tomograms.

Abstract

In this paper, an efficient paradigm is presented to correct for brain shift during tumor resection therapies. For this study, high resolution preoperative (pre-op) and postoperative (post-op) MR images were acquired for eight in vivo patients, and surface/subsurface shift was identified by manual identification of homologous points between the pre-op and immediate post-op tomograms. Cortical surface deformation data were then used to drive an inverse problem framework. The manually identified subsurface deformations served as a comparison toward validation. The proposed framework recaptured 85% of the mean subsurface shift. This translated to a subsurface shift error of 0.4 +/- 0.4 mm for a measured shift of 3.1 +/- 0.6 mm. The patient's pre-op tomograms were also deformed volumetrically using displacements predicted by the model. Results presented allow a preliminary evaluation of correction both quantitatively and visually. While intraoperative (intra-op) MR imaging data would be optimal, the extent of shift measured from pre- to post-op MR was comparable to clinical conditions. This study demonstrates the accuracy of the proposed framework in predicting full-volume displacements from sparse shift measurements. It also shows that the proposed framework can be extended and used to update pre-op images on a time scale that is compatible with surgery.

• Authors: Prashanth Dumpuri, Reid C Thompson, Aize Cao, Siyi Ding, Ishita Garg, Benoit M Dawant, Michael I Miga
• Journal: IEEE transactions on bio-medical engineering (IEEE Trans Biomed Eng) Vol. 57 Issue 6 Pg. 1285-96 (Jun 2010) ISSN: 1558-2531 [Electronic] United States
• PMID: 20172796 (Publication Type: Journal Article, Research Support, N.I.H., Extramural)

Topics

• Algorithms
• Artifacts
• Brain Neoplasms (pathology, surgery)
• Female
• Humans
• Image Enhancement (methods)
• Image Interpretation, Computer-Assisted (methods)
• Magnetic Resonance Imaging (methods)
• Male
• Middle Aged
• Neurosurgical Procedures (methods)
• Pattern Recognition, Automated (methods)
• Postoperative Care (methods)
• Preoperative Care (methods)
• Reproducibility of Results
• Sensitivity and Specificity
• Subtraction Technique
• Surgery, Computer-Assisted (methods)