Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

Deformable MRI-Ultrasound Registration using Correlation-based Attribute Matching for Brain Shift Correction: Accuracy and Generality in Multi-site Data

Institution:
1Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: ines7.prata.machado@gmail.com.
2Department of Systems Engineering, École de Technologie Supérieure, Montreal, Canada.
3Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
4Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
Publisher:
Elsevier Science
Publication Date:
Nov-2019
Journal:
Neuroimage
Volume Number:
202
Pages:
116094
Citation:
Neuroimage. 2019 Nov 15;202:116094.
PubMed ID:
31446127
PMCID:
PMC6819249
Keywords:
Brain shift, Intraoperative ultrasound, MR-iUS registration, Multi-site data
Appears in Collections:
NCIGT, NAC, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
R01 NS049251/NS/NINDS NIH HHS/United States
Generated Citation:
Machado I., Toews M., George E., Unadkat P., Essayed W., Luo J., Teodoro P., Carvalho H., Martins J., Golland P., Pieper S., Frisken S., Golby A., Wells III W.M., Ou Y. Deformable MRI-Ultrasound Registration using Correlation-based Attribute Matching for Brain Shift Correction: Accuracy and Generality in Multi-site Data. Neuroimage. 2019 Nov 15;202:116094. PMID: 31446127. PMCID: PMC6819249.
Export citation:
Google Scholar: link

Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (US) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy US. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. To improve accuracy of registration, we use high-dimensional texture attributes instead of image intensities and propose to replace the standard difference-based attribute matching with correlation-based attribute matching. We also present a strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images. We optimize key parameters across independent MR-iUS brain tumor datasets acquired at three different institutions, with a total of 43 tumor patients and 758 corresponding landmarks to validate the registration algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, our algorithm was able to reduce landmark errors prior to registration in three data sets (5.37 ± 4.27, 4.18 ± 1.97 and 6.18 ± 3.38 mm, respectively) to a consistently low level (2.28 ± 0.71, 2.08 ± 0.37 and 2.24 ± 0.78 mm, respectively). Our algorithm is compared to 15 other algorithms that have been previously tested on MR-iUS registration and it is competitive with the state-of-the-art on multiple datasets. We show that our algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). We further characterized landmark errors according to brain regions and tumor types, a topic so far missing in the literature. We found that landmark errors were higher in high-grade than low-grade glioma patients, and higher in tumor regions than in other brain regions.