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Proceedings Paper

Multi-body registration for fracture reduction in orthopaedic trauma surgery
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Paper 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.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150F (16 March 2020); doi: 10.1117/12.2549708
Show Author Affiliations
R. Han, Johns Hopkins Univ. (United States)
A. Uneri, Johns Hopkins Univ. (United States)
P. Wu, Johns Hopkins Univ. (United States)
R. Vijayan, Johns Hopkins Univ. (United States)
P. Vagdargi, Johns Hopkins Univ. (United States)
M. Ketcha, Johns Hopkins Univ. (United States)
N. Sheth, Johns Hopkins Univ. (United States)
S. Vogt, Siemens Healthineers (Germany)
G. Kleinszig, Siemens Healthineers (Germany)
G. M. Osgood, The Johns Hopkins Hospital (United States)
J. H. Siewerdsen, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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