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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

Alignment errors as a result of placing manually configured as well as computer-optimized CSGs (full exposure) on the mean distal femur from our ASM
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Fig15: Alignment errors as a result of placing manually configured as well as computer-optimized CSGs (full exposure) on the mean distal femur from our ASM

Mentions: Assuming for the moment only a single direction-origin pair defining a translational movement toward the bone surface, we then define for a given CSG configuration (csg) the CSG error for as the maximum deviation over all pins. In other words, we compute , as the maximum Euclidean distance between the intended and actual pin location (see Fig. 4). Given , we can derive a bound on global drift and orientation deviation and vice versa. In our work, we impose a maximally acceptable drift of 1.5 mm, which implies a rotational error of 1 (see Figs. 15, 16). The surgeon can also modify this value prior to surgery.Fig. 4


Numerical optimization of alignment reproducibility for customizable surgical guides.

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

Alignment errors as a result of placing manually configured as well as computer-optimized CSGs (full exposure) on the mean distal femur from our ASM
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig15: Alignment errors as a result of placing manually configured as well as computer-optimized CSGs (full exposure) on the mean distal femur from our ASM
Mentions: Assuming for the moment only a single direction-origin pair defining a translational movement toward the bone surface, we then define for a given CSG configuration (csg) the CSG error for as the maximum deviation over all pins. In other words, we compute , as the maximum Euclidean distance between the intended and actual pin location (see Fig. 4). Given , we can derive a bound on global drift and orientation deviation and vice versa. In our work, we impose a maximally acceptable drift of 1.5 mm, which implies a rotational error of 1 (see Figs. 15, 16). The surgeon can also modify this value prior to surgery.Fig. 4

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