Of the 20,500 people diagnosed, and the 12,740 deaths from cancer of the central nervous system this year, approximately 85-90% of these patients are afflicted with brain tumors. The 5-year survival rate is approximately 34-37% despite an increase by 20% over the past 20 years. According to the National Cancer Institute surgical removal is the recommended treatment for most brain tumors with the goal of the most complete resection possible while preserving neurological function. With respect to the mission of the National Institute for Neurological Disorders and Stroke, more complete resection reduces the burden of neurological disease by restoring function, extending life, and improving the quality of that life. With respect to surgical therapy, the deployment of visual displays that relate the patient's exposed brain within the operating room (OR) to the pre-operatively acquired neuroanatomical images has become commonplace. More specifically, surgeon's can use a pen-like stylus to point at a specific piece of the patient's brain tissue and see where that tissue resides on the neuroanatomical images as facilitated by an interactive display. One detriment to this process is when the patient's brain deforms due to common surgical manipulations. As a result, the alignment between images and the patient's physical brain becomes compromised and surgical error could ensue. In recent work, laser range scanning (LRS) technology has been demonstrated to both improve the alignment of the cortical surface and measure brain deformations during surgery. In this application, the use of LRS technology will be extended in conjunction with computer models to correct the deformation-induced misalignment during surgery. The hypothesis to be tested is that computer models, laser range data of the intra-operative environment, and tracked stylus digitization technology can be combined to effectively compensate for deformation during image-guided brain tumor surgery.
The specific aims to investigate this hypothesis involve the development of: (1) a comprehensive digitization platform that can be positioned in the sterile field, (2) a computer algorithm to compensate for deformations based on data collected with digitization platform, (3) an OR compute node that can process data and present the data visually to the surgeon for feedback, and (4) a series of clinical studies to validate the approach. The work in this application will be the first fully realized correction system for image-guided surgery. The strategy is particularly innovative by using both pre-computation and direct simulation to generate a robust, accurate, and fast approach to compensation. Given the difficulty in validation, we have also generated a 3-component approach to validation, which when take together, will provide a good assessment of the techniques. With respect to the importance of this work, in large part, the use of image-guided surgery for surgical resection in soft-tissue organs has been confined primarily to the cranial environment. With the resolution of deformations as proposed herein, the ability to translate image-guided surgery to other soft-tissue organs becomes possible. Furthermore, the approaches herein are also inexpensive when compared to intra-operative imaging methods (e.g. MR), and scalable, i.e. capable of widespread adoption. This application is focused at producing the next evolution in image guidance.

Public Health Relevance

Of the 20,500 people diagnosed, and the 12,740 deaths from cancer of the central nervous system this year, approximately 85-90% of these patients are afflicted with brain tumors. According to the National Cancer Institute surgical removal is the recommended treatment for most brain tumors with the goal of the most complete resection possible while preserving neurological function. More complete resection restores function, extends life, and improves the quality of that life. The goal of this project is to assist the surgeon in producing a more complete resection of brain tumors. The other important aspect is the technology we are introducing is relatively inexpensive and is amenable to widespread adoption by medical centers all across the country.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS049251-07
Application #
8206763
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Babcock, Debra J
Project Start
2004-08-01
Project End
2014-12-31
Budget Start
2012-01-01
Budget End
2014-12-31
Support Year
7
Fiscal Year
2012
Total Cost
$539,001
Indirect Cost
$182,934
Name
Vanderbilt University Medical Center
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
Sastry, Rahul; Bi, Wenya Linda; Pieper, Steve et al. (2017) Applications of Ultrasound in the Resection of Brain Tumors. J Neuroimaging 27:5-15
Yang, Xiaochen; Clements, Logan W; Luo, Ma et al. (2017) Stereovision-based integrated system for point cloud reconstruction and simulated brain shift validation. J Med Imaging (Bellingham) 4:035002
Clements, Logan W; Collins, Jarrod A; Weis, Jared A et al. (2017) Deformation correction for image guided liver surgery: An intraoperative fidelity assessment. Surgery 162:537-547
Luo, Ma; Frisken, Sarah F; Weis, Jared A et al. (2017) Retrospective study comparing model-based deformation correction to intraoperative magnetic resonance imaging for image-guided neurosurgery. J Med Imaging (Bellingham) 4:035003
Hormuth 2nd, David A; Weis, Jared A; Barnes, Stephanie L et al. (2017) A mechanically coupled reaction-diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth. J R Soc Interface 14:
Miga, Michael I; Sun, Kay; Chen, Ishita et al. (2016) Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases. Int J Comput Assist Radiol Surg 11:1467-74
Ong, Rowena; Glisson, Courtenay L; Burgner-Kahrs, Jessica et al. (2016) A novel method for texture-mapping conoscopic surfaces for minimally invasive image-guided kidney surgery. Int J Comput Assist Radiol Surg 11:1515-26
Kassab, Ghassan S; An, Gary; Sander, Edward A et al. (2016) Augmenting Surgery via Multi-scale Modeling and Translational Systems Biology in the Era of Precision Medicine: A Multidisciplinary Perspective. Ann Biomed Eng 44:2611-25
Miga, Michael I (2016) Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery. Ann Biomed Eng 44:128-38
Weis, Jared A; Miga, Michael I; Arlinghaus, Lori R et al. (2015) Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model. Cancer Res 75:4697-707

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