title: 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.affiliation: Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA. prashanth.dumpuri@vanderbilt.edu
authors.forename: Prashanth
authors.initials: P
authors.lastname: Dumpuri
authors.forename: Reid C
authors.initials: RC
authors.lastname: Thompson
authors.forename: Aize
authors.initials: A
authors.lastname: Cao
authors.forename: Siyi
authors.initials: S
authors.lastname: Ding
authors.forename: Ishita
authors.initials: I
authors.lastname: Garg
authors.forename: Benoit M
authors.initials: BM
authors.lastname: Dawant
authors.forename: Michael I
authors.initials: MI
authors.lastname: Miga
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001932:Brain Neoplasms / Q000473:pathology* / Q000601:surgery*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D019635:Neurosurgical Procedures / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D011182:Postoperative Care / Q000379:methods; D011300:Preoperative Care / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D025321:Surgery, Computer-Assisted / Q000379:methods*