1. Incorporating Haptics EGFR Molecular Dynamics
Three Dimensional Projection Environment for Molecular Design and Surgical Simulation Matthew Wampolea, Eric Wickstroma,d, Chang-Po Chena, Devakumar Devadhasb, Yuan-Yuan Jina, Jeffrey M. Sandersa, John C. Kairysc, Martha L. Ankenye, Rui Huf, Kenneth E. Barnerf, Karl V. Steinerf and Mathew L. Thakurb,d aBiochemistry and Molecular Biology, bRadiology, cSurgery, dKimmel Cancer Center, eAcademic and Instructional Support and Resources, Thomas Jefferson University, Philadelphia, PA fElectrical and Computer Engineering, University of Delaware, Newark DE 19716
Introduction
Method
Surgery involves palpating and manipulating tissues in the operating room environment. However, sophisticated radiographic systems present only visual images. The actual assembly of organs of a particular patient must now be imagined by the surgeon before the operation. Complications that were not anticipated, such as bleeding from unusually placed arteries or veins, or unusual lesion geometry, lengthen the procedure, placing extra stress on the patient and the surgeons.
We are developing agents for positron emission tomography (PET) imaging of cancer gene mRNA expression to positively identify malignant tissues. We will fuse mRNA PET images with anatomical computerized tomography (CT) images to enable volumetric (3D) haptic (touch-and-feel) simulation of pancreatic cancer and surrounding organs prior to surgery in a particular patient.
We hypothesize that our fusion of genetic, visual, and tactile information will improve demarcation of clear margins, and will ultimately permit surgeons to better plan operations and to prepare for the actual pathology found.
Patient Data (CT/PET)
Collect image data
Locate tumor masses
Segment images
Render segmented organs in 3D
Create surface and tetrahedral meshes
Amira®
3D Image Data
Simulation Open Framework Architecture (SOFA)
Convert meshes to accepted formats
Characterize mechanics of organs
Combine visual and physical properties
Include haptic device into simulation
Surgical Simulation
Patient Data
Incorporating Haptics
Patient specific images provide anatomical positioning of normal and cancerous tissue in two dimensional image slices. Many modalities are available; in this study we use de-identified CT and PET images. The 2D images are valuable for diagnosis, but the lack of depth limits their usefulness. PET images of [18F]deoxyglucose accumulation assists in locating cancerous regions in the CT images that might otherwise be hidden.
Palpation is an important for locating the cancerous tumor and determining surgical margins. Using the Phantom Omni to provide haptic feedback, the simulation will present surgeons with a chance to practice what margins would be expected before going into surgery. Surgeons will be interviewed on the 'feel' of the tumor and organs to fine tune the material properties of the models.
Tumor CT
CT with
FDG-PET
EGFR Molecular Dynamics
Amira®
EGF binding to EGFR enables cell entry. We will identify a fragment of EGF to serve as a hook for internalization of reporter-PNA-peptide hybridization probes for imaging of cancer gene mRNA. At the current stage, we show the result of a Langevin dynamics simulation in explicit water 40 nsec after EGF binding. EGF behaves similarly.
Amira® is a powerful platform for visualizing bio-medical images. The patent's data was segmented manually with assistance from pre-installed tools. Automated segmentation for the entire patient was complicated by noise and nearly indistinguishable differences in the greyscale indexes of various organs. Typically the image stacks are reviewed for days or weeks before surgery to identify small features. Manually segmenting takes about the same time while improving the spatial recognition. Amira can also be used to build meshes of the segmented images for use in other programs.
Conclusions
Tumor
Turning 2D CT/PET slices into 3D objects assists in understanding the topology surrounding tumor masses. Incorporating the visual and physical characteristics of a patient’s anatomy will provide surgeons with an informative pre-operative tool to plan and practice the operation before the first incision. Including haptic feedback provides a familiar 'feel' to surgeons as they palpate the target organ, trying to locate the tumor and determine how large a margin of resection will be needed. The development of genetic PET imaging and contrast CT into a combined visual will further improve the surgeons’ knowledge by more accurately pinpointing malignant tissue and any hidden blood vessels.
SOFA Framework
SOFA is an open source simulation framework being developed by researchers at INRIA and its collaborators. A 'node' based architecture makes the simulated scenes highly customizable. Each 'object' consists of a behavior, collision, and visual node. The typical simulation uses ordinary differential equation solvers and Euler solvers to compute mathematical equations, but others can be easily implemented.
Future Work
Include contrast-enhanced CT images into the model for improved vascular modeling.
Improve simulation performance with multi-threading and CUDA.
Improve collision detection and model interaction algorithms.
Incorporate genetic PET imaging of cancer gene expression of tumors.
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