On the Design and Experiments of a Fluoro-Robotic Navigation System for Closed Intramedullary Nailing of Femur

Nakdhamabhorn, Sakol; Suthakorn, Jackrit

doi:10.1007/978-3-642-25789-6_29

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Then, the static homogeneous transformation ( ) can be determined by (4). The completed solution result of projection equation was shown in (8) and the result of ( ) homogeneous transformation was represented in (9).…”

Section: Step Ii: Fluoroscopic Image Projection Geometry Calibrationmentioning

confidence: 99%

“…Many Computer Integrated Surgery Systems have been proposed to assisted operations, especially in distal interlocking holes targeting. The approach for recover a position and orientation of distal-hole axis uses only the area of projected image to recover the nails rotation angle with respect to fitting curve technique [8,9]. Thierry Leloup et.…”

Section: Introductionmentioning

confidence: 99%

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System integration of a fluoroscopic image calibration using robot assisted surgical guidance for distal locking process in closed intramedullary nailing of femur

Nakdhamabhorn

Suthakorn

2019

IJECE

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<span>Distal locking procedure is one of the most complex tasks in close intramedullary nailing operation which requires fluoroscopic image to interpret 2-D distal locking position on image related to 3-D distal locking position on the patient site. Hence the surgeon has to perform the distal locking process by using multiple fluoroscopic images which causes a lot of x-ray exposure to the patient and surgeon and is a time consuming task. This paper presents the system integration of a fluoroscopic image calibration using robot assisted surgical guidance. The system integration consists of three parts; distal locking recovery, fluoroscopic calibration and tracking, and robot assisted surgical guidance. The distal locking-hole recovery algorithm is based on characteristic information of the major and minor axes of distal locking hole. The fluoroscopic calibration and tracking is modeled as pin-hole projection model to estimate a projection equation based on optical tracking system. The robot-assisted surgical guidance is developed to overlay a trajectory path using a laser beam for reducing the problem of hand – eye coordination on most surgical navigation system. We integrate each part to complete a surgical navigation system for distal locking process. The experiment of system integration is conducted to validate the accuracy of distal locking axis position and orientation. The results of the system integration shows a mean angular error of 1.10 and mean Euclidean distance in X-Y plane error of 3.65 mm.</span>

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Section: Step Ii: Fluoroscopic Image Projection Geometry Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

System integration of a fluoroscopic image calibration using robot assisted surgical guidance for distal locking process in closed intramedullary nailing of femur

Nakdhamabhorn

Suthakorn

2019

IJECE

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A Deep-Learning Approach for Locating the Intramedullary Nail’s Holes Based on 2d Calibrated Fluoroscopic Images

Jun-feng¹,

Deng²,

Chen³

et al. 2023

J. Mech. Med. Biol.

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Background: Minimally invasive surgery is widely used for managing fractures. When using the intramedullary nailing for bone fixation, surgeons must insert interlocking screws to prevent rotation of the bone fragment; however, it is difficult to determine the exact screwing position of intramedullary nails inserted into the bone. Conventionally, the distal interlocking nail surgery is performed under intermittent X-ray shooting. Nevertheless, this freehand fluoroscopic technique is technically demanding and time-consuming. Currently, the failure rate of this surgery is more than [Formula: see text], and the location error requires to be controlled within 2[Formula: see text]mm. Purpose: To develop a deep-learning approach for locating the intramedullary nail’s holes based on 2D calibrated fluoroscopic images. Methods: The projection of the hole’s axis is deeply regressed in the first step. Then, the hole’s 3D axis is derived by computing the intersection line of two planes determined by the projection of the axis and the X-ray source, respectively. The benefit of the data-driven manner is that our method can be applied to the arbitrary shape of the hole’s contour. Besides, we extract hole’s contour as the distinctive feature, so as to reduce the space of the training data in a large scale. Results: Our approach is proved to be efficient and easy to be implemented, and it has been compared with traditional location method in phantom experiments. The location accuracy error of the traditional method is [Formula: see text][Formula: see text]mm, [Formula: see text], and the location error of this method is [Formula: see text][Formula: see text]mm, [Formula: see text]. Furthermore, the traditional method takes an average of 10[Formula: see text]min to complete the location, while our method takes only 4[Formula: see text]min. In addition, to further verify the robustness of our method, we carried out a preclinical study involving different neural networks for locating the hole’s axis. Conclusion: Whether in terms of time consumption or accuracy error, our method is significantly better than traditional method, and the efficiency has been significantly improved. Therefore, our method has great clinical value. In addition, our approach has potential advantages over the X-ray guided freehand solution in terms of radiation exposure, and it has tremendous application prospects.

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On the Design and Experiments of a Fluoro-Robotic Navigation System for Closed Intramedullary Nailing of Femur

Cited by 2 publications

References 7 publications

System integration of a fluoroscopic image calibration using robot assisted surgical guidance for distal locking process in closed intramedullary nailing of femur

System integration of a fluoroscopic image calibration using robot assisted surgical guidance for distal locking process in closed intramedullary nailing of femur

A Deep-Learning Approach for Locating the Intramedullary Nail’s Holes Based on 2d Calibrated Fluoroscopic Images

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