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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

CSG pipeline for knee replacement surgery. The  are specific to the CSG. The CSG takes as input the planned implant alignment and uses it to make the CSG patient specific and to optimize its configuration. When the CSG is optimized, its configuration protocol is used intra-operatively to adjust the CSG and to dock it on the patients’ bone. Next, holes are drilled for k-wires. Once the k-wires are inserted, a cutting block can be attached to the k-wires and conventional surgery can take over
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Fig2: CSG pipeline for knee replacement surgery. The are specific to the CSG. The CSG takes as input the planned implant alignment and uses it to make the CSG patient specific and to optimize its configuration. When the CSG is optimized, its configuration protocol is used intra-operatively to adjust the CSG and to dock it on the patients’ bone. Next, holes are drilled for k-wires. Once the k-wires are inserted, a cutting block can be attached to the k-wires and conventional surgery can take over

Mentions: The pin-based CSG studied in this paper is inspired by [6] and uses a sparse point-surface contact set (a selected number of strategically placed adjustable pins) to achieve a stable configuration between the device and the bone. The pin-based CSG consists of a regular grid of holes, through which pins can be inserted (see Fig. 1). To give a more precise impression of how our method should be integrated in the surgical pipeline, we give an overview for a CSG-assisted total knee replacement in Fig. 2.Fig. 2


Numerical optimization of alignment reproducibility for customizable surgical guides.

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

CSG pipeline for knee replacement surgery. The  are specific to the CSG. The CSG takes as input the planned implant alignment and uses it to make the CSG patient specific and to optimize its configuration. When the CSG is optimized, its configuration protocol is used intra-operatively to adjust the CSG and to dock it on the patients’ bone. Next, holes are drilled for k-wires. Once the k-wires are inserted, a cutting block can be attached to the k-wires and conventional surgery can take over
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: CSG pipeline for knee replacement surgery. The are specific to the CSG. The CSG takes as input the planned implant alignment and uses it to make the CSG patient specific and to optimize its configuration. When the CSG is optimized, its configuration protocol is used intra-operatively to adjust the CSG and to dock it on the patients’ bone. Next, holes are drilled for k-wires. Once the k-wires are inserted, a cutting block can be attached to the k-wires and conventional surgery can take over
Mentions: The pin-based CSG studied in this paper is inspired by [6] and uses a sparse point-surface contact set (a selected number of strategically placed adjustable pins) to achieve a stable configuration between the device and the bone. The pin-based CSG consists of a regular grid of holes, through which pins can be inserted (see Fig. 1). To give a more precise impression of how our method should be integrated in the surgical pipeline, we give an overview for a CSG-assisted total knee replacement in Fig. 2.Fig. 2

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