A comparison of thin-plate spline deformation and finite element modeling to compensate for brain shift during tumor resection
Abstract Purpose Brain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons’ ability to achieve optimal resections. This paper compares two methods that have been presented in the...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer International Publishing
2021
|
| Online Access: | https://hdl.handle.net/1721.1/131459 |
| _version_ | 1811081393200955392 |
|---|---|
| author | Frisken, Sarah Luo, Ma Juvekar, Parikshit Bunevicius, Adomas Machado, Ines Unadkat, Prashin Bertotti, Melina M Toews, Matt Wells, William M Miga, Michael I Golby, Alexandra J |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Frisken, Sarah Luo, Ma Juvekar, Parikshit Bunevicius, Adomas Machado, Ines Unadkat, Prashin Bertotti, Melina M Toews, Matt Wells, William M Miga, Michael I Golby, Alexandra J |
| author_sort | Frisken, Sarah |
| collection | MIT |
| description | Abstract
Purpose
Brain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons’ ability to achieve optimal resections. This paper compares two methods that have been presented in the literature to compensate for brain shift: a thin-plate spline deformation model and a finite element method (FEM). For this comparison, both methods are driven by identical sparse data. Specifically, both methods are driven by displacements between automatically detected and matched feature points from intraoperative 3D ultrasound (iUS). Both methods have been shown to be fast enough for intraoperative brain shift correction (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525–1538, 2018; Luo et al. in J Med Imaging (Bellingham) 4(3):035003, 2017). However, the spline method requires no preprocessing and ignores physical properties of the brain while the FEM method requires significant preprocessing and incorporates patient-specific physical and geometric constraints. The goal of this work was to explore the relative merits of these methods on recent clinical data.
Methods
Data acquired during 19 sequential tumor resections in Brigham and Women’s Hospital’s Advanced Multi-modal Image-Guided Operating Suite between December 2017 and October 2018 were considered for this retrospective study. Of these, 15 cases and a total of 24 iUS to iUS image pairs met inclusion requirements. Automatic feature detection (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525–1538, 2018) was used to detect and match features in each pair of iUS images. Displacements between matched features were then used to drive both the spline model and the FEM method to compensate for brain shift between image acquisitions. The accuracies of the resultant deformation models were measured by comparing the displacements of manually identified landmarks before and after deformation.
Results
The mean initial subcortical registration error between preoperative MRI and the first iUS image averaged 5.3 ± 0.75 mm. The mean subcortical brain shift, measured using displacements between manually identified landmarks in pairs of iUS images, was 2.5 ± 1.3 mm. Our results showed that FEM was able to reduce subcortical registration error by a small but statistically significant amount (from 2.46 to 2.02 mm). A large variability in the results of the spline method prevented us from demonstrating either a statistically significant reduction in subcortical registration error after applying the spline method or a statistically significant difference between the results of the two methods.
Conclusions
In this study, we observed less subcortical brain shift than has previously been reported in the literature (Frisken et al., in: Miller (ed) Biomechanics of the brain, Springer, Cham, 2019). This may be due to the fact that we separated out the initial misregistration between preoperative MRI and the first iUS image from our brain shift measurements or it may be due to modern neurosurgical practices designed to reduce brain shift, including reduced craniotomy sizes and better control of intracranial pressure with the use of mannitol and other medications. It appears that the FEM method and its use of geometric and biomechanical constraints provided more consistent brain shift correction and better correction farther from the driving feature displacements than the simple spline model. The spline-based method was simpler and tended to give better results for small deformations. However, large variability in the spline results and relatively small brain shift prevented this study from demonstrating a statistically significant difference between the results of the two methods. |
| first_indexed | 2024-09-23T11:45:59Z |
| format | Article |
| id | mit-1721.1/131459 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:45:59Z |
| publishDate | 2021 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1314592023-09-25T20:59:58Z A comparison of thin-plate spline deformation and finite element modeling to compensate for brain shift during tumor resection Frisken, Sarah Luo, Ma Juvekar, Parikshit Bunevicius, Adomas Machado, Ines Unadkat, Prashin Bertotti, Melina M Toews, Matt Wells, William M Miga, Michael I Golby, Alexandra J Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Abstract Purpose Brain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons’ ability to achieve optimal resections. This paper compares two methods that have been presented in the literature to compensate for brain shift: a thin-plate spline deformation model and a finite element method (FEM). For this comparison, both methods are driven by identical sparse data. Specifically, both methods are driven by displacements between automatically detected and matched feature points from intraoperative 3D ultrasound (iUS). Both methods have been shown to be fast enough for intraoperative brain shift correction (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525–1538, 2018; Luo et al. in J Med Imaging (Bellingham) 4(3):035003, 2017). However, the spline method requires no preprocessing and ignores physical properties of the brain while the FEM method requires significant preprocessing and incorporates patient-specific physical and geometric constraints. The goal of this work was to explore the relative merits of these methods on recent clinical data. Methods Data acquired during 19 sequential tumor resections in Brigham and Women’s Hospital’s Advanced Multi-modal Image-Guided Operating Suite between December 2017 and October 2018 were considered for this retrospective study. Of these, 15 cases and a total of 24 iUS to iUS image pairs met inclusion requirements. Automatic feature detection (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525–1538, 2018) was used to detect and match features in each pair of iUS images. Displacements between matched features were then used to drive both the spline model and the FEM method to compensate for brain shift between image acquisitions. The accuracies of the resultant deformation models were measured by comparing the displacements of manually identified landmarks before and after deformation. Results The mean initial subcortical registration error between preoperative MRI and the first iUS image averaged 5.3 ± 0.75 mm. The mean subcortical brain shift, measured using displacements between manually identified landmarks in pairs of iUS images, was 2.5 ± 1.3 mm. Our results showed that FEM was able to reduce subcortical registration error by a small but statistically significant amount (from 2.46 to 2.02 mm). A large variability in the results of the spline method prevented us from demonstrating either a statistically significant reduction in subcortical registration error after applying the spline method or a statistically significant difference between the results of the two methods. Conclusions In this study, we observed less subcortical brain shift than has previously been reported in the literature (Frisken et al., in: Miller (ed) Biomechanics of the brain, Springer, Cham, 2019). This may be due to the fact that we separated out the initial misregistration between preoperative MRI and the first iUS image from our brain shift measurements or it may be due to modern neurosurgical practices designed to reduce brain shift, including reduced craniotomy sizes and better control of intracranial pressure with the use of mannitol and other medications. It appears that the FEM method and its use of geometric and biomechanical constraints provided more consistent brain shift correction and better correction farther from the driving feature displacements than the simple spline model. The spline-based method was simpler and tended to give better results for small deformations. However, large variability in the spline results and relatively small brain shift prevented this study from demonstrating a statistically significant difference between the results of the two methods. 2021-09-20T17:17:10Z 2021-09-20T17:17:10Z 2019-08-23 2020-09-24T21:15:37Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131459 en https://doi.org/10.1007/s11548-019-02057-2 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. CARS application/pdf Springer International Publishing Springer International Publishing |
| spellingShingle | Frisken, Sarah Luo, Ma Juvekar, Parikshit Bunevicius, Adomas Machado, Ines Unadkat, Prashin Bertotti, Melina M Toews, Matt Wells, William M Miga, Michael I Golby, Alexandra J A comparison of thin-plate spline deformation and finite element modeling to compensate for brain shift during tumor resection |
| title | A comparison of thin-plate spline deformation and finite element modeling to compensate for brain shift during tumor resection |
| title_full | A comparison of thin-plate spline deformation and finite element modeling to compensate for brain shift during tumor resection |
| title_fullStr | A comparison of thin-plate spline deformation and finite element modeling to compensate for brain shift during tumor resection |
| title_full_unstemmed | A comparison of thin-plate spline deformation and finite element modeling to compensate for brain shift during tumor resection |
| title_short | A comparison of thin-plate spline deformation and finite element modeling to compensate for brain shift during tumor resection |
| title_sort | comparison of thin plate spline deformation and finite element modeling to compensate for brain shift during tumor resection |
| url | https://hdl.handle.net/1721.1/131459 |
| work_keys_str_mv | AT friskensarah acomparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT luoma acomparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT juvekarparikshit acomparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT buneviciusadomas acomparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT machadoines acomparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT unadkatprashin acomparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT bertottimelinam acomparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT toewsmatt acomparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT wellswilliamm acomparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT migamichaeli acomparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT golbyalexandraj acomparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT friskensarah comparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT luoma comparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT juvekarparikshit comparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT buneviciusadomas comparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT machadoines comparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT unadkatprashin comparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT bertottimelinam comparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT toewsmatt comparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT wellswilliamm comparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT migamichaeli comparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection AT golbyalexandraj comparisonofthinplatesplinedeformationandfiniteelementmodelingtocompensateforbrainshiftduringtumorresection |