Anastomosis is a necessary and critical part of all reconstructive surgery involving any luminal structure from cardiovascular to gastrointestinal (GI) surgery. Well over a million anastomoses are performed in the USA each year for visceral indications alone (gastrointestinal, urologic and gynecologic surgery). However, up to 30% of GI anastomoses are complicated by leakage, strictures, and stenosis, in part attributable to technical and technologic issues. An anastomotic complication significantly increases patient mortality from three times up to ten times, and diminishes the function and quality of life for affected patients. Although the minimally invasive surgical approach has transformed surgery with significantly reduced collateral tissue damage associated with access to operative sites, recent advances in surgical tools and vision technology have not addressed the critical factors influencing anastomotic outcome. This is evidenced by the lack of improvements in complication rates. To the contrary, the current minimally invasive surgery (MIS) or robot assisted surgery (RAS) pose additional new challenges for anastomosis stemming from visual and spatial limitations. The long-term goal of this research is to reduce complications and improve functional outcomes of anastomosis by robotically executing best anastomosis techniques. The following specific aims will enable the development of this technology and demonstrate feasibility, as a path to clinical adoption:
Aim 1 : Identify optimal suture placements using multispectral imaging. We will compare suture placements and anastomotic outcome between those guided by our novel algorithm for suture location optimization incorporating subsurface anatomic and physiologic information and those performed by expert surgeons in pre-clinical studies.
Aim 2 : Accurately track mobile and deformable soft tissue targets in an unstructured surgical environment. We will demonstrate how our innovative fused 3D tracking based on plenoptic imaging and NIR marker technology allows real-time, accurate identification and tracking of tissue targets during the task of anastomosis in contrast to current tracking methods in phantom and in-vivo studies.
Aim 3 : Compare supervised autonomous robotic control to manual anastomosis. We will compare the algorithm of automated suture planning controlled by supervised autonomous robotics to current standard master-slave robotic and manual laparoscopic technology in performing in-vivo anastomosis in preclinical studies. This research has the potential to significantly improve the function and outcome of anastomosis, independent of surgeon experience. Beyond anastomosis, adoption of this approach could be beneficial in all soft tissue MIS and RAS tasks requiring precision and maneuverability due to small working space, including pediatric and complex cardiac surgery.

Public Health Relevance

We propose to develop and test the next generation of surgical imaging and robotics for supervised- autonomous soft tissue surgery. The goal of this research is to improve surgical imaging to reveal subsurface tissue parameters and track tissue targets to robotically execute best anastomosis techniques independently of surgeon's experience to ultimately reduce complications and improve functional outcomes of anastomosis.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB020610-01A1
Application #
9105602
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Krosnick, Steven
Project Start
2016-04-01
Project End
2019-12-31
Budget Start
2016-04-01
Budget End
2016-12-31
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Children's Research Institute
Department
Type
DUNS #
143983562
City
Washington
State
DC
Country
United States
Zip Code
20010
Krieger, Axel; Opfermann, Justin; Kim, Peter C W (2018) Development and Feasibility of a Robotic Laparoscopic Clipping Tool for Wound Closure and Anastomosis. J Med Device 12:0110051-110056
Le, Hanh N D; Nguyen, Hieu; Wang, Zhaoyang et al. (2018) Demonstration of a laparoscopic structured-illumination three-dimensional imaging system for guiding reconstructive bowel anastomosis. J Biomed Opt 23:1-10
Decker, Ryan S; Shademan, Azad; Opfermann, Justin D et al. (2017) Biocompatible Near-Infrared Three-Dimensional Tracking System. IEEE Trans Biomed Eng 64:549-556
Le, Hanh N D; Nguyen, Hieu; Wang, Zhaoyang et al. (2017) A demonstration of structured-illumination-based technique using commercial surgical endoscope. Conf Lasers Electro Optics 2017:
Opfermann, Justin D; Leonard, Simon; Decker, Ryan S et al. (2017) Semi-Autonomous Electrosurgery for Tumor Resection Using a Multi-Degree of Freedom Electrosurgical Tool and Visual Servoing. Rep U S 2017:3653-3659
Frank, Tobias; Krieger, Axel; Leonard, Simon et al. (2017) ROS-IGTL-Bridge: an open network interface for image-guided therapy using the ROS environment. Int J Comput Assist Radiol Surg 12:1451-1460
Le, Hanh N D; Decker, Ryan; Opferman, Justin et al. (2016) 3-D endoscopic imaging using plenoptic camera. Conf Lasers Electro Optics 2016:
Cha, Jaepyeong; Shademan, Azad; Le, Hanh N D et al. (2015) Multispectral tissue characterization for intestinal anastomosis optimization. J Biomed Opt 20:106001