Fracture reduction planning and guidance in orthopaedic trauma surgery via multi-body image registration

Han, Runze; Uneri, Ali; Vijayan, Rohan C.; Wu, Pu‐Wei; Vagdargi, Prasad; Sheth, Niral; Vogt, Sebastian; Kleinszig, Gerhard; Osgood, Greg; Siewerdsen, Jeffrey H.

doi:10.1016/j.media.2020.101917

Cited by 30 publications

(21 citation statements)

References 53 publications

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“…However, these techniques are difficult to apply directly in orthopedic trauma surgery because of the additional costs, equipment, time, and lack of support for changes due to surgery. 2D fluoroscopic images are still the mainstream for orthopedic trauma surgery [ 28 ]. In the treatment of fractures, the affected area is largely deformed and moved by the surgical procedure, and a method to track this has not yet been established.…”

Section: Discussionmentioning

confidence: 99%

An Experimental Study of a 3D Bone Position Estimation System Based on Fluoroscopic Images

Yoshii

Iwahashi²,

Sashida³

et al. 2022

Diagnostics

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To compare a 3D preoperative planning image and fluoroscopic image, a 3D bone position estimation system that displays 3D images in response to changes in the position of fluoroscopic images was developed. The objective of the present study was to evaluate the accuracy of the estimated position of 3D bone images with reference to fluoroscopic images. Bone positions were estimated from reference points on a fluoroscopic image compared with those on a 3D image. The four reference markers positional relationships on the fluoroscopic image were compared with those on the 3D image to evaluate whether a 3D image may be drawn by tracking positional changes in the radius model. Intra-class correlations coefficients for reference marker distances between the fluoroscopic image and 3D image were 0.98–0.99. Average differences between measured values on the fluoroscopic image and 3D bone image for each marker corresponding to the direction of the bone model were 1.1 ± 0.7 mm, 2.4 ± 1.8 mm, 1.4 ± 0.8 mm, and 2.0 ± 1.6 mm in the anterior-posterior view, ulnar side lateral view, posterior-anterior view, and radial side lateral view, respectively. Marker positions were more accurate in the anterior-posterior and posterior-anterior views than in the radial and ulnar side lateral views. This system helps in real-time comparison of dynamic changes in preoperative 3D and intraoperative fluoroscopy images.

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Section: Discussionmentioning

confidence: 99%

An Experimental Study of a 3D Bone Position Estimation System Based on Fluoroscopic Images

Yoshii

Iwahashi²,

Sashida³

et al. 2022

Diagnostics

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“…This mirroring method relies on the assumption that the contralateral bones are symmetric, which is often not true in case of natural shape differences or bilateral trauma (Han et al, 2021). Therefore, another approach has been proposed which is based on the use of a SSM to serve as a template for registration of the fracture fragments.…”

Section: Virtual Bone Fracture Reduction Planningmentioning

confidence: 99%

Computer-Assisted Preoperative Planning of Bone Fracture Fixation Surgery: A State-Of-The-Art Review

Moolenaar¹,

Tümer²,

Checa³

2022

Preprint

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Background Bone fracture fixation surgery is one of the most commonly performed surgical procedures in the orthopedic field. However, fracture healing complications occur frequently, and the choice of the most optimal surgical approach often remains challenging. In the last years, computational tools have been developed with the aim to assist preoperative planning procedures of bone fracture fixation surgery. Objectives The aims of this review are (1) to provide a comprehensive overview of the state-of-the-art in computer-assisted preoperative planning of bone fracture fixation surgery, (2) to assess the clinical feasibility of the existing virtual planning approaches, and (3) to assess their clinical efficacy in terms of clinical outcomes as compared to conventional planning methods. Methods A literature search was performed in the MEDLINE-PubMed, Ovid-EMBASE, Ovid-EMCARE, Web of Science, and Cochrane libraries to identify articles reporting on the clinical use of computer-assisted preoperative planning of bone fracture fixation. Results 79 articles were included to provide an overview of the state-of-the art in virtual planning. While patient-specific geometrical model construction, virtual bone fracture reduction, and virtual fixation planning are routinely applied in virtual planning, biomechanical analysis is rarely included in the planning framework. 21 of the included studies were used to assess the feasibility and efficacy of computer-assisted planning methods. The reported total mean planning duration ranged from 22 to 258 minutes in different studies. Computer-assisted planning resulted in reduced operation time (Standardized Mean Difference (SMD): -2.19; 95% Confidence Interval (CI): -2.87, -1.50), less blood loss (SMD: -1.99; 95% CI: -2.75, -1.24), decreased frequency of fluoroscopy (SMD: -2.18; 95% CI: -2.74, -1.61), shortened fracture healing times (SMD:-0.51; 95% CI: -0.97, -0.05) and less postoperative complications (Risk Ratio (RR): 0.64, 95% CI: 0.46, 0.90). No significant differences were found in hospitalization duration. Some studies reported improvements in reduction quality and functional outcomes but these results were not pooled for meta-analysis, since the reported outcome measures were too heterogeneous. Conclusions Current computer-assisted planning approaches are feasible to be used in clinical practice and have been shown to improve clinical outcomes. Including biomechanical analysis into the framework has the potential to further improve clinical outcome.

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“…Despite the small number of learning-based multiple object registration strategies, traditional intensity-based registration methods are now routinely employed to solve compound, multiple object, registration problems across broad applications, such as: kinematic measurements of bone ( Chen et al, 2012 ; Otake et al, 2016 ; Abe et al, 2019 ), rib motion and respiratory analysis ( Hiasa et al, 2019 ), intra-operative assessment of adjusted bone fragments ( Grupp et al, 2019 , 2020b ; Han et al, 2021 ), confirmation of screw implant placement during spine surgery ( Uneri et al, 2017 ) and the positioning of a surgical robot with respect to target anatomy ( Gao et al, 2020a ). We believe that the lack of new multiple object registration learning-based strategies is indicative of the substantial challenges involved with their development, rather than any perceived lack of the problem’s importance by the community.…”

Section: Perspectivementioning

confidence: 99%

The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective

Unberath

Gao

et al. 2021

Front. Robot. AI

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Image-based navigation is widely considered the next frontier of minimally invasive surgery. It is believed that image-based navigation will increase the access to reproducible, safe, and high-precision surgery as it may then be performed at acceptable costs and effort. This is because image-based techniques avoid the need of specialized equipment and seamlessly integrate with contemporary workflows. Furthermore, it is expected that image-based navigation techniques will play a major role in enabling mixed reality environments, as well as autonomous and robot-assisted workflows. A critical component of image guidance is 2D/3D registration, a technique to estimate the spatial relationships between 3D structures, e.g., preoperative volumetric imagery or models of surgical instruments, and 2D images thereof, such as intraoperative X-ray fluoroscopy or endoscopy. While image-based 2D/3D registration is a mature technique, its transition from the bench to the bedside has been restrained by well-known challenges, including brittleness with respect to optimization objective, hyperparameter selection, and initialization, difficulties in dealing with inconsistencies or multiple objects, and limited single-view performance. One reason these challenges persist today is that analytical solutions are likely inadequate considering the complexity, variability, and high-dimensionality of generic 2D/3D registration problems. The recent advent of machine learning-based approaches to imaging problems that, rather than specifying the desired functional mapping, approximate it using highly expressive parametric models holds promise for solving some of the notorious challenges in 2D/3D registration. In this manuscript, we review the impact of machine learning on 2D/3D registration to systematically summarize the recent advances made by introduction of this novel technology. Grounded in these insights, we then offer our perspective on the most pressing needs, significant open problems, and possible next steps.

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Fracture reduction planning and guidance in orthopaedic trauma surgery via multi-body image registration

Cited by 30 publications

References 53 publications

An Experimental Study of a 3D Bone Position Estimation System Based on Fluoroscopic Images

An Experimental Study of a 3D Bone Position Estimation System Based on Fluoroscopic Images

Computer-Assisted Preoperative Planning of Bone Fracture Fixation Surgery: A State-Of-The-Art Review

The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective

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