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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

Docking directions are generated inside a truncated cone by picking a random point on Disk 1 and 2, these two points ( and ) are then connected and form the docking direction . The default radius for Disk 2 is 5 mm, and the cone angle is . The  represents the placement uncertainty, and does not dependent on the size of the patient. However, this parameter can be changed by the user prior to optimization
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Fig5: Docking directions are generated inside a truncated cone by picking a random point on Disk 1 and 2, these two points ( and ) are then connected and form the docking direction . The default radius for Disk 2 is 5 mm, and the cone angle is . The represents the placement uncertainty, and does not dependent on the size of the patient. However, this parameter can be changed by the user prior to optimization

Mentions: One important observation is that the equilibrium state of the CSG depends on the bone surface and a surgeon would not be able to move a device perfectly along a single direction. Consequently, several docking movements , in the form of a starting position and direction, should be tested. In practice, we restrict to a truncated cone (see Fig. 5). The directions and origins inside the truncated cone are tested; the final objective function is then . In practice, we use 40 directions because the maximum drift parameter changed only marginally (drift 0.05 mm) hereafter and the computational overhead of adding more directions does not pay off in this case.


Numerical optimization of alignment reproducibility for customizable surgical guides.

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

Docking directions are generated inside a truncated cone by picking a random point on Disk 1 and 2, these two points ( and ) are then connected and form the docking direction . The default radius for Disk 2 is 5 mm, and the cone angle is . The  represents the placement uncertainty, and does not dependent on the size of the patient. However, this parameter can be changed by the user prior to optimization
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig5: Docking directions are generated inside a truncated cone by picking a random point on Disk 1 and 2, these two points ( and ) are then connected and form the docking direction . The default radius for Disk 2 is 5 mm, and the cone angle is . The represents the placement uncertainty, and does not dependent on the size of the patient. However, this parameter can be changed by the user prior to optimization
Mentions: One important observation is that the equilibrium state of the CSG depends on the bone surface and a surgeon would not be able to move a device perfectly along a single direction. Consequently, several docking movements , in the form of a starting position and direction, should be tested. In practice, we restrict to a truncated cone (see Fig. 5). The directions and origins inside the truncated cone are tested; the final objective function is then . In practice, we use 40 directions because the maximum drift parameter changed only marginally (drift 0.05 mm) hereafter and the computational overhead of adding more directions does not pay off in this case.

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