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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

In the crossover stage, the configuration of two random CSGs (parent A and B) is combined to spawn a new CSG. A new CSG is formed by combining the pin IDs from two shuffled pin ID lists
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Fig9: In the crossover stage, the configuration of two random CSGs (parent A and B) is combined to spawn a new CSG. A new CSG is formed by combining the pin IDs from two shuffled pin ID lists

Mentions: To evolve the set of current individuals, we apply elitism, crossover, and mutation. Elitism keeps the best individuals (elite) in the population to maintain their good properties. For crossovers, properties of two randomly chosen individuals are exchanged (see Fig. 9). Mutation means copying elite individuals and applying a slight configuration change. Precisely, a randomly chosen active pin is moved to a new location, (see Fig. 10). In order to reduce the probability of getting stuck in a local extremum, random CSGs are added to the population with a small probability.


Numerical optimization of alignment reproducibility for customizable surgical guides.

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

In the crossover stage, the configuration of two random CSGs (parent A and B) is combined to spawn a new CSG. A new CSG is formed by combining the pin IDs from two shuffled pin ID lists
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4591200&req=5

Fig9: In the crossover stage, the configuration of two random CSGs (parent A and B) is combined to spawn a new CSG. A new CSG is formed by combining the pin IDs from two shuffled pin ID lists
Mentions: To evolve the set of current individuals, we apply elitism, crossover, and mutation. Elitism keeps the best individuals (elite) in the population to maintain their good properties. For crossovers, properties of two randomly chosen individuals are exchanged (see Fig. 9). Mutation means copying elite individuals and applying a slight configuration change. Precisely, a randomly chosen active pin is moved to a new location, (see Fig. 10). In order to reduce the probability of getting stuck in a local extremum, random CSGs are added to the population with a small probability.

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