Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TBME.2009.2039643 | IEEE Trans. Biomed. Engineering |
Keywords | Field | DocType |
biomedical MRI,brain,finite element analysis,inverse problems,physiological models,surgery,tumours,MR images,brain shift,cortical surface deformation,intraoperative MR imaging,inverse problem,subsurface deformations,subsurface shift error,tomograms,tumor resection therapy,Brain shift,finite elements,image deformation,image-guided surgery,inverse model | Biomedical engineering,Preoperative care,Surface deformation,Computer science,Image processing,Resection,Image-guided surgery,Tomography,Inverse problem,Magnetic resonance imaging | Journal |
Volume | Issue | ISSN |
57 | 6 | 1558-2531 |
Citations | PageRank | References |
16 | 0.81 | 17 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Dumpuri | 1 | 121 | 9.67 |
| Reid C. Thompson | 2 | 78 | 6.02 |
| Aize Cao | 3 | 64 | 7.05 |
| Siyi Ding | 4 | 75 | 5.19 |
| Ishita Garg | 5 | 19 | 3.25 |
| Benoit M. Dawant | 6 | 1388 | 223.11 |
| Michael I. Miga | 7 | 567 | 72.99 |