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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

The pin-based version of the CSG applied to the distal femur. The surgical plan is transferred to the operating theater by encapsulating the shape of the bone in the guide using a collection of strategically distributed pins (which collide with the surface of the bone). The CSG has predefined holes for the k-wires that are compatible with standard instrumentation for performing the principal bone cut
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Fig1: The pin-based version of the CSG applied to the distal femur. The surgical plan is transferred to the operating theater by encapsulating the shape of the bone in the guide using a collection of strategically distributed pins (which collide with the surface of the bone). The CSG has predefined holes for the k-wires that are compatible with standard instrumentation for performing the principal bone cut

Mentions: The objective of this article is to investigate how to automate the CSG configuration process for an arbitrary CSG. We illustrate our method by applying it to knee replacement surgery. Via a semiautomated planning step, the CSG becomes patient specific and ensures that the planned alignment can be accurately reproduced and the device snaps into the intended position and orientation (see Fig. 1). To this extent, we created a novel and generic computer-assisted planning method that predicts the CSG trajectory to the bone and its stability and guides the configuration process. The method is designed to allow users to indicate particular regions on the bone to be avoided (for instance due to bone spurs). We validate our optimization method via a simulation, as well as a real-world setting with a pin-based CSG applied to a rapid prototyped bone model.


Numerical optimization of alignment reproducibility for customizable surgical guides.

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

The pin-based version of the CSG applied to the distal femur. The surgical plan is transferred to the operating theater by encapsulating the shape of the bone in the guide using a collection of strategically distributed pins (which collide with the surface of the bone). The CSG has predefined holes for the k-wires that are compatible with standard instrumentation for performing the principal bone cut
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: The pin-based version of the CSG applied to the distal femur. The surgical plan is transferred to the operating theater by encapsulating the shape of the bone in the guide using a collection of strategically distributed pins (which collide with the surface of the bone). The CSG has predefined holes for the k-wires that are compatible with standard instrumentation for performing the principal bone cut
Mentions: The objective of this article is to investigate how to automate the CSG configuration process for an arbitrary CSG. We illustrate our method by applying it to knee replacement surgery. Via a semiautomated planning step, the CSG becomes patient specific and ensures that the planned alignment can be accurately reproduced and the device snaps into the intended position and orientation (see Fig. 1). To this extent, we created a novel and generic computer-assisted planning method that predicts the CSG trajectory to the bone and its stability and guides the configuration process. The method is designed to allow users to indicate particular regions on the bone to be avoided (for instance due to bone spurs). We validate our optimization method via a simulation, as well as a real-world setting with a pin-based CSG applied to a rapid prototyped bone model.

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