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Title: Predictive surgical simulation for preoperative planning of complex cardioviscular surgeries
Authors: LI HAO
Keywords: predictive simulation, cardiovascular surgery, preoperative surgical planning, blood vessel modeling, tubular object deformation, Cosserat tree
Issue Date: 20-Aug-2010
Citation: LI HAO (2010-08-20). Predictive surgical simulation for preoperative planning of complex cardioviscular surgeries. ScholarBank@NUS Repository.
Abstract: Many cardiovascular surgeries involve very complex operations on the heart, the cardiac blood vessels, and other soft tissues. At present, cardiac surgeons rely mostly on echocardiography, cardiac catheterization and CT/MR images to understand the specific anatomical structures of a patient. They often resort to manual drawing to visualize the surgical procedures and the expected results. This approach is not precise and is impossible to provide detailed information about the possible outcome of the surgical procedures. To improve the precision and effectiveness of cardiovascular surgery planning, novel computer simulation systems are needed. Among the existing surgery simulation systems, reactive systems attempt to simulate real-time response of body tissues according to user inputs that emulate surgical operations. They are useful for medical training and preoperative planning of simple surgical operations. However, to use a reactive system for predicting the results of complex surgeries, the surgeon would need to go through all the detailed surgical steps, which is very tedious and timeconsuming. Predictive systems, on the other hand, produce the results of surgical procedures given a small amount of user inputs. They allow the surgeon to easily explore various surgical options and determine the best ones. At present, few predictive simulation systems have been developed for open surgeries, and none for complex cardiovascular surgeries. This thesis presents a predictive simulation system for preoperative planning of complex cardiovascular surgeries. To develop this simulation system, surgical requirements from the surgeon?s perspective are first analyzed. These requirements are translated into system requirements that define the simulation system. Predictive simulation algorithms are then developed to accomplish the requirements of simulating the surgical procedures and predicting possible surgical outcomes based on a small amount of user inputs. In predictive simulation of complex cardiovascular surgeries, the deformation of blood vessels needs to be simulated accurately and efficiently. This thesis presents a novel hybrid approach to achieve this task. It binds a reference Cosserat tree to the surface mesh of the blood vessels. The reference Cosserat tree is a tree structure of Cosserat rods that explicitly model the stretching, bending, and twisting of the blood vessel branches, while the surface mesh models the surface details. The deformation of the hybrid model is achieved by first deforming the Cosserat tree based on Cosserat theory, then deforming the surface mesh according to the binding to the Cosserat tree, and the surface?s local bending and stretching energies. In this way, the deformation of blood vessels can be simulated accurately and efficiently. In addition, explicit modeling of the important characteristics of the blood vessels, such as the bending and twisting strains, facilitates the evaluation of the surgical results. The predictive simulation system is demonstrated on various surgeries including neo-aorta reconstruction, bidirectional Glenn shunt, arterial switch operations, extracardiac total cavo-pulmonary connection, and Norwood procedure. For bidirectional Glenn shunt simulation, qualitative and quantitative comparison of the simulation results and postoperative CT images of the patients are also presented to validate the accuracy of the proposed hybrid blood vessel model and the predictive simulation system. The test results show that the predicted blood vessels are similar to the postoperative blood vessels.
Appears in Collections:Ph.D Theses (Open)

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