Computer-based 3d Puzzle Solving For Pre-operative Planning Of Articular Fracture Reductions In The Ankle, Knee, And Hip

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1 Computer-based 3d Puzzle Solving For Pre-operative Planning Of Articular Fracture Reductions In The Ankle, Knee, And Hip Andrew M. Kern, MS, Donald Anderson. University of Iowa, Iowa City, IA, USA. Disclosures: A.M. Kern: None. D. Anderson: 4; CoOwner-FxRedux Solutions. Introduction: Operative management of comminuted articular fractures is a difficult task requiring high levels of pre-operative planning, anatomical knowledge, and surgical technique. Typically, pre-operative planning is performed using CT volumetric renderings to identify critical fragments and to consider the steps required to achieve an anatomical reduction. In practice it can be a very difficult perceptual task to identify the necessary translations/rotations for each fragment required to achieve an accurate reduction, especially when factors such as limb shortening are taken into account. The constellation of bone fragments can be thought of as a 3D puzzle that the surgeon solves, first in his head and second in the OR. Computer-based preoperative fracture reduction can potentially be used as a tool to better inform clinicians as to the challenges of an upcoming fracture reduction [1]. In previous work a computer-based 3D puzzle solving algorithm had been demonstrated in a cohort of tibial plafond fracture patients [2]. This algorithm demonstrated excellent virtual reduction accuracy (< 0.5 mm), within a reasonable time window (< 2.5 hours). It is however limited to fractures where a segmentation of the intact contralateral limb is attainable. Increased concern with radiation exposure and changing clinical practices have precluded the inclusion of healthy, intact limbs in imaging procedures for newly enrolled fracture patients. The goal of this study was to develop a new puzzle solving methodology that does not require an intact contralateral limb as a template, and to compare this methodology to the previously presented template-based fracture reductions. The new template-free methodology was tested on 3 tibial plafond, 3 acetabular and 2 tibial plateau fractures. Methods: A new algorithm for virtual fracture reduction was created in MATLAB 2014a to accommodate cases with bi-lateral fractures, or where a CT image of the intact contralateral limb is unavailable. This template-free algorithm attempts to reduce the fracture by identifying and matching the fractured edges of a fragment. Four main steps are involved in this algorithm. First each individual bone fragment is segmented from a clinical CT scan using a watershed-based segmentation algorithm. Next each vertex on the fragment surface models is classified as either inter-fragmentary or native bone surface, using a previously trained ADABoost classifier [3]. Fracture edges on each fragment are found by computing a minimum graph cut between using surface classification as in-region costs and the mean curvature of the surface as the edge weights. This serves to separate inter-fragmentary surfaces from native cortical and subchondral bone surfaces along the edges of highest curvature on the surface models. Once these edges are identified, a semi-automated user guided fracture reduction process is started. A trained user selects two fragments which share a common fracture edge, from this an automated algorithm finds an optimal transform to match the two fragments such that the distances between the common points on their curves are minimized. The user verifies the transform and the fragments are merged into a compound fragment with a new fragment edge curve. The process of merging fragments into

2 compound fragments and then matching these compound fragments is continued until the fracture consists of only one compound fragment. At this point the fracture solution is considered complete and the solution is saved to a format for report back to the clinician (Fig 1). The previous puzzle-solving algorithm was developed in MATLAB 2012a (Mathworks, Natick MA) and involved 4 major steps: First a custom watershed-based segmentation algorithm was used to create individual surface models of each fragment involved in the fracture. The surface of each fragment is then segmented into discrete regions using a spanning tree algorithm over the mean curvature of the surface. Each surface region is then classified based on local image intensity, gradient, curvature, and neighborhood using a bagging ensemble classifier [4]. Finally these classified regions are aligned to the intact contralateral template using an iterative closest point algorithm, starting with the largest fragment and ending with the smallest fragment (Fig 2).

3 Template-based fracture reduction was performed on 3 tibial plafond and 3 acetabular fractures for use as a gold standard. The template-free puzzle solutions were computed on 3 tibial plafond, and 3 acetabular fractures and on two tibial plateau fractures for which no intact contralateral is available. Completed virtual fracture reconstructions were visually inspected to ensure that all fragments were reasonably positioned and that no obvious angulation or shortening of the bone was present. Results: Virtual fracture reductions were successfully computed for all of the 6 fractures attempted with template-based puzzle solving within the 2.5 hour time window required for clinical turn-around. The template-free fracture reduction was successful in 2 of 3 tibial plafond fractures, 3 acetabular fractures and 1 of 2 tibial plateau fractures, again each of these reductions were computed within a 2.5 hour window (Fig 3).

4 Discussion: This study has extended computer fracture reduction to one new joint as well as introducing a new methodology for template-free computer fracture reduction. While the template-free fracture reduction produced good results on 6 of the 8 tested fractures it does have several situations where it routinely fails. The two fractures that the algorithm failed to properly reconstruct were the two most comminuted fractures tested, with five and nine fragments. Failures in the template-free fracture reduction can be explained by two major factors. Since fragments are matched based on the congruence of their fracture edges, pairings are very sensitive to segmentation accuracy as well as fragment shape. Additionally, fractures with high comminution and crushing tend to have poor reduction due to the alteration of adjacent fracture curves. Cases where the template-free fracture reduction was successful it consistently had a higher error than template-based fracture reduction. This is attributable to the fact that the template-based reductions are directly minimizing the deviation to the intact template, whereas template-free reduction is prone to small alignment errors propagating through the solution. The level of error seen in both methods is acceptable for providing purely qualitative feedback to the surgeon however quantitative work following a computational fracture reduction will require additional steps to evaluate and mitigate this error. In the future current methods will be refined to reduce the error seen in template-free puzzle solving and improve the rate of solution success.

5 Significance: Although template-free fracture reduction technique is less robust than template-based solutions it is an important step forward in computer based fracture reduction. It enables the inclusion of subjects where scans of the intact contralateral limb are unavailable and provides a general framework for which is agnostic of the joint or limb being reconstructed. ORS 2015 Annual Meeting Paper No: 0244