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Numerical optimization of alignment reproducibility for customizable surgical guides.

Kroes T, Valstar E, Eisemann E - Int J Comput Assist Radiol Surg (2015)

Bottom Line: The proposed optimization technique has been compared to manual optimization by experts, as well as participants with domain experience.Manually optimizing CSG parameters turns out to be a counterintuitive task.Even after training, subjects with and without anatomical background fail in choosing appropriate CSG configurations.

View Article: PubMed Central - PubMed

Affiliation: Computer Graphics and Visualization Group, Department of Intelligent Systems, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands. t.kroes@tudelft.nl.

ABSTRACT

Purpose: Computer-assisted orthopedic surgery aims at minimizing invasiveness, postoperative pain, and morbidity with computer-assisted preoperative planning and intra-operative guidance techniques, of which camera-based navigation and patient-specific templates (PST) are the most common. PSTs are one-time templates that guide the surgeon initially in cutting slits or drilling holes. This method can be extended to reusable and customizable surgical guides (CSG), which can be adapted to the patients' bone. Determining the right set of CSG input parameters by hand is a challenging task, given the vast amount of input parameter combinations and the complex physical interaction between the PST/CSG and the bone.

Methods: This paper introduces a novel algorithm to solve the problem of choosing the right set of input parameters. Our approach predicts how well a CSG instance is able to reproduce the planned alignment based on a physical simulation and uses a genetic optimization algorithm to determine optimal configurations. We validate our technique with a prototype of a pin-based CSG and nine rapid prototyped distal femora.

Results: The proposed optimization technique has been compared to manual optimization by experts, as well as participants with domain experience. Using the optimization technique, the alignment errors remained within practical boundaries of 1.2 mm translation and [Formula: see text] rotation error. In all cases, the proposed method outperformed manual optimization.

Conclusions: Manually optimizing CSG parameters turns out to be a counterintuitive task. Even after training, subjects with and without anatomical background fail in choosing appropriate CSG configurations. Our optimization algorithm ensures that the CSG is configured correctly, and we could demonstrate that the intended alignment of the CSG is accurately reproduced on all tested bone geometries.

No MeSH data available.


Related in: MedlinePlus

Visualization of the CSG and the external forces applied during the physical simulation. The moment magnitude varies periodically with a sine function, the parallel force rotates around the center of the CSG
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Fig6: Visualization of the CSG and the external forces applied during the physical simulation. The moment magnitude varies periodically with a sine function, the parallel force rotates around the center of the CSG

Mentions: To determine the CSG equilibrium state, we employ a physical simulation that predicts how the device will behave. During the simulation, we subject the CSG to external forces to mimic the real behavior of the docking process. We observed that apart from the principal pressing force along , the user will exert moments and parallel forces on the CSG in an attempt to assess its stability using the haptic feedback that it provides (if the CSG wanders under these external forces, it is not securely docked in the right position). Taking the pressing force into account is useful because the morphology of the host bone might make particular pressing directions more suitable. For instance, in the case of the distal femur, we observed that when applying the pressing force under a slight angle, the CSG behavior improves (see Fig. 6). In most cases, the CSG will reach an equilibrium state in which the CSG error can be determined. However, in some cases, the CSG will simply fall off and the physical simulation will be aborted prematurely. Here, we consider the error to be infinite, indicating that it is not useful.


Numerical optimization of alignment reproducibility for customizable surgical guides.

Kroes T, Valstar E, Eisemann E - Int J Comput Assist Radiol Surg (2015)

Visualization of the CSG and the external forces applied during the physical simulation. The moment magnitude varies periodically with a sine function, the parallel force rotates around the center of the CSG
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4591200&req=5

Fig6: Visualization of the CSG and the external forces applied during the physical simulation. The moment magnitude varies periodically with a sine function, the parallel force rotates around the center of the CSG
Mentions: To determine the CSG equilibrium state, we employ a physical simulation that predicts how the device will behave. During the simulation, we subject the CSG to external forces to mimic the real behavior of the docking process. We observed that apart from the principal pressing force along , the user will exert moments and parallel forces on the CSG in an attempt to assess its stability using the haptic feedback that it provides (if the CSG wanders under these external forces, it is not securely docked in the right position). Taking the pressing force into account is useful because the morphology of the host bone might make particular pressing directions more suitable. For instance, in the case of the distal femur, we observed that when applying the pressing force under a slight angle, the CSG behavior improves (see Fig. 6). In most cases, the CSG will reach an equilibrium state in which the CSG error can be determined. However, in some cases, the CSG will simply fall off and the physical simulation will be aborted prematurely. Here, we consider the error to be infinite, indicating that it is not useful.

Bottom Line: The proposed optimization technique has been compared to manual optimization by experts, as well as participants with domain experience.Manually optimizing CSG parameters turns out to be a counterintuitive task.Even after training, subjects with and without anatomical background fail in choosing appropriate CSG configurations.

View Article: PubMed Central - PubMed

Affiliation: Computer Graphics and Visualization Group, Department of Intelligent Systems, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands. t.kroes@tudelft.nl.

ABSTRACT

Purpose: Computer-assisted orthopedic surgery aims at minimizing invasiveness, postoperative pain, and morbidity with computer-assisted preoperative planning and intra-operative guidance techniques, of which camera-based navigation and patient-specific templates (PST) are the most common. PSTs are one-time templates that guide the surgeon initially in cutting slits or drilling holes. This method can be extended to reusable and customizable surgical guides (CSG), which can be adapted to the patients' bone. Determining the right set of CSG input parameters by hand is a challenging task, given the vast amount of input parameter combinations and the complex physical interaction between the PST/CSG and the bone.

Methods: This paper introduces a novel algorithm to solve the problem of choosing the right set of input parameters. Our approach predicts how well a CSG instance is able to reproduce the planned alignment based on a physical simulation and uses a genetic optimization algorithm to determine optimal configurations. We validate our technique with a prototype of a pin-based CSG and nine rapid prototyped distal femora.

Results: The proposed optimization technique has been compared to manual optimization by experts, as well as participants with domain experience. Using the optimization technique, the alignment errors remained within practical boundaries of 1.2 mm translation and [Formula: see text] rotation error. In all cases, the proposed method outperformed manual optimization.

Conclusions: Manually optimizing CSG parameters turns out to be a counterintuitive task. Even after training, subjects with and without anatomical background fail in choosing appropriate CSG configurations. Our optimization algorithm ensures that the CSG is configured correctly, and we could demonstrate that the intended alignment of the CSG is accurately reproduced on all tested bone geometries.

No MeSH data available.


Related in: MedlinePlus