@inproceedings{3afed4dc13964a4ea984675409fbe94b,
title = "Multi-body registration for fracture reduction in orthopaedic trauma surgery",
abstract = "Purpose. Fracture reduction is a challenging part of orthopaedic pelvic trauma procedures, resulting in poor long-term prognosis if reduction does not accurately restore natural morphology. Manual preoperative planning is performed to obtain target transformations of target bones - a process that is challenging and time-consuming even to experts within the rapid workflow of emergent care and fluoroscopically guided surgery. We report a method for fracture reduction planning using a novel image-based registration framework. Method. An objective function is designed to simultaneously register multi-body bone fragments that are preoperatively segmented via a graph-cut method to a pelvic statistical shape model (SSM) with inter-body collision constraints. An alternating optimization strategy switches between fragments alignment and SSM adaptation to solve for the fragment transformations for fracture reduction planning. The method was examined in a leave-one-out study performed over a pelvic atlas with 40 members with two-body and three-body fractures simulated in the left innominate bone with displacements ranging 0-20 mm and 0°-15°. Result. Experiments showed the feasibility of the registration method in both two-body and three-body fracture cases. The segmentations achieved Dice coefficient of median 0.94 (0.01 interquartile range [IQR]) and root mean square error (RMSE) of 2.93 mm (0.56 mm IQR). In two-body fracture cases, fracture reduction planning yielded 3.8 mm (1.6 mm IQR) translational and 2.9° (1.8° IQR) rotational error. Conclusion. The method demonstrated accurate fracture reduction planning within 5 mm and shows promise for future generalization to more complicated fracture cases. The algorithm provides a novel means of planning from preoperative CT images that are already acquired in standard workflow.",
keywords = "Image registration, Image segmentation, Image-guided surgery, Orthopaedic trauma, Statistical shape model",
author = "R. Han and A. Uneri and P. Wu and R. Vijayan and P. Vagdargi and M. Ketcha and N. Sheth and S. Vogt and G. Kleinszig and Osgood, {G. M.} and Siewerdsen, {J. H.}",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 16-02-2020 Through 19-02-2020",
year = "2020",
doi = "10.1117/12.2549708",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Baowei Fei and Linte, {Cristian A.}",
booktitle = "Medical Imaging 2020",
}