To a degree, the use of soft-tissue modeling for updating image-guided navigational systems has not been embraced by the mainstream scientific community. It has only just recently found application within the neuronavigation community (although no commercial systems are available yet) and is still under investigation. Much of this frustration is not due to the growing number of methodologies but rather to a misunderstanding of the goals of model-updating, and an inability to test and validate. With respect to the former, it is na?ve to believe that modeling can account for all fine-scale deformations. However, the question to be answered is, within the confines of surgical margin, can model-updating significantly impact surgical resection? This is a central research hypothesis to be investigated within this application. What sets this application apart is that if the milestones are achieved, the outcome could result in a soft-tissue deformation correction system for image- guided liver surgery systems that could be immediately commercially available for patient care. More specifically, at the conclusion of this work, an image-guided liver surgical system capable of deformation correction will be generated, a preliminary experience with the fidelity of those corrections will be established, and the technology will be commercially available to early adopters that secure approval from their Institutional Review Board (IRB). This is possible because this application will leverage an ongoing clinical trial being performed by Pathfinder Therapeutics Incorporated (PTI) that is in the process of testing their image-guided liver surgery (IGLS) system to start in the latter half of 2007. PTI has agreed to share their clinical data to support the novel tissue deformation correction strategy proposed herein. The hypothesis that models can be used to correct for deformation within IGLS will be supported by three specific aims which involve: (1) the development of deformation compensation strategy that involves a combined registration and shape correction technique that will reside on an adaptable deformation correction compute node, (2) the retrospective testing of this approach on data from three separate clinical trials, and (3) an investigation to improve the computer model for the updating process. Phase II for this application would involve upgrading the systems of the early adopters to include our deformation correction compute node and then prospectively test its fidelity clinically. Primary and metastatic cancer within the liver is becoming increasingly common. There is significant evidence that intra-abdominal liver surgery improves survival times for patients afflicted with metastatic disease. Currently the patient population is limited largely due to the complexity of this procedure. Better visualization and guidance would provide surgeons more confidence and would increase the number of surgical candidates and outcome for these patients. If this application is successful, it would lead to the first commercially available image-guided liver surgery system capable of soft-tissue deformation correction. The proposed """"""""deformation correction compute node"""""""" would have more widespread impact by being readily adaptable to other surgical systems with similar data. In addition, the strategy would also be compatible with minimally invasive surgeries provided that information regarding organ shape can be acquired. Currently, the only commercial means to correct for soft-tissue deformation is to use intraoperative magnetic resonance (iMR) and computed tomography (iCT). These systems are of considerable expense, require staff, and can be costly to maintain. Due to their cumbersome nature, the patient through-put is also considerably less than a conventional operating room. iCT has been available since the mid-1980's and iMR has been available since the mid- 1990's, yet there are still only a handful of systems being used throughout the world. While these are disadvantages, it should be noted that these systems will not be dispensed with and will continue to be developed. However, it is highly probable that these facilities will become referral centers for the most critical cases rather than available as a mainstream technology. The strategy of augmenting an existing image- guidance system with a """"""""deformation correction compute node"""""""" is very low cost, may be as effective as the iMR/iCT solution, and is translatable to any medical center with an image-guided surgery system. This application will play an important role in remedying a disconnection between these sparse referral centers and the vast assortment of local medical centers available to the general population. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21EB007694-01
Application #
7303713
Study Section
Special Emphasis Panel (ZEB1-OSR-B (M1))
Program Officer
Haller, John W
Project Start
2007-08-01
Project End
2010-07-31
Budget Start
2007-08-01
Budget End
2008-07-31
Support Year
1
Fiscal Year
2007
Total Cost
$346,535
Indirect Cost
Name
Vanderbilt University Medical Center
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
Simpson, Amber; Miga, Michael (2018) Special Section Guest Editorial: Technology Platforms for Treatment and Discovery in Human Systems: Novel Work in Image-Guided Procedures, Robotic Interventions, and Modeling. J Med Imaging (Bellingham) 5:021201
McKenna, Matthew T; Weis, Jared A; Barnes, Stephanie L et al. (2017) A Predictive Mathematical Modeling Approach for the Study of Doxorubicin Treatment in Triple Negative Breast Cancer. Sci Rep 7:5725
Collins, Jarrod A; Weis, Jared A; Heiselman, Jon S et al. (2017) Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework. IEEE Trans Med Imaging 36:1502-1510
Miga, Michael I (2016) Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery. Ann Biomed Eng 44:128-38
Galloway, Robert L; Herrell, S Duke; Miga, Michael I (2012) Image-Guided Abdominal Surgery and Therapy Delivery. J Healthc Eng 3:203-228
Clements, Logan W; Dumpuri, Prashanth; Chapman, William C et al. (2011) Organ surface deformation measurement and analysis in open hepatic surgery: method and preliminary results from 12 clinical cases. IEEE Trans Biomed Eng 58:
Lathrop, Ray A; Hackworth, Douglas M; Webster 3rd, Robert J (2010) Minimally invasive holographic surface scanning for soft-tissue image registration. IEEE Trans Biomed Eng 57:1497-506
Dumpuri, Prashanth; Clements, Logan W; Dawant, Benoit M et al. (2010) Model-updated image-guided liver surgery: preliminary results using surface characterization. Prog Biophys Mol Biol 103:197-207
Clements, Logan W; Chapman, William C; Dawant, Benoit M et al. (2008) Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation. Med Phys 35:2528-40
Barnes, Stephanie L; Lyshchik, Andrej; Washington, Mary K et al. (2007) Development of a mechanical testing assay for fibrotic murine liver. Med Phys 34:4439-50