Research Article: Quantitative Computerized Assessment of the Degree of Acetabular Bone Deficiency: Total radial Acetabular Bone Loss (TrABL)

Date Published: October 17, 2011

Publisher: SAGE-Hindawi Access to Research

Author(s): Frederik Gelaude, Tim Clijmans, Hendrik Delport.

http://doi.org/10.4061/2011/494382

Abstract

A novel quantitative, computerized, and, therefore, highly objective method is presented to assess the degree of total radical acetabular bone loss. The method, which is abbreviated to “TrABL”, makes use of advanced 3D CT-based image processing and effective 3D anatomical reconstruction methodology. The output data consist of a ratio and a graph, which can both be used for direct comparison between specimens. A first dataset of twelve highly deficient hemipelves, mainly Paprosky types IIIB, is used as illustration. Although generalization of the findings will require further investigation on a larger population, it can be assumed that the presented method has the potential to facilitate the preoperative use of existing classifications and related decision schemes for treatment selection in complex revision cases.

Partial Text

Classification SystemsNumerous classification systems have been applied to describe bone deficiencies associated with failed acetabular prostheses, each differing somewhat in purpose and detail. The aim of using a classification system is to predict the nature of the bone deficiency in advance of surgery to allow adequate treatment selection and reconstruction planning. Another important role of these classifications is the promotion of uniform surgical results measurement and reporting [1, 2].Commonly used acetabular classification systems are for example those developed by Letournel [3], Paprosky et al. [4], and D’Antonio et al. [5]. Throughout the years, the validity and reliability of classification systems have been studied, and altered—possibly improved—systems have been introduced [6–8].The class definitions are generally spoken qualitative in nature. No direct quantitative input or output measure is used, and, therefore, intraobserver and interobserver reliability is low. Some studies even suggest that, in particular for the acetabulum, bone stock loss classification systems are simply inconsistent and unreliable [6, 7, 9, 10]. Classifications mainly rely on preoperative X-ray images, complemented with intraoperative findings. In case of doubt, also preoperative computed tomography (CT) scan information can be involved, either by direct inspection of the planar slice images or from an overall reconstructed three-dimensional (3D) visualisation which has been directly generated in the CT scanner software.Current use of the aforementioned imaging modalities can be problematic. X-rays for example merely present a scaled projection of the three-dimensional reality; while the aforementioned 3D CT visualisation mainly highlight the failed metallic implant components and do not provide a clear view of the remaining bony situation.In contrast, classification systems are based upon and even illustrated by 3D images of hemi-pelves, in total absence of failed components. Up till today, while using X-rays and direct 3D scanner visualisations, the only occasion in which the classification could truly be applied occurs during surgery, after making the incision and effectively removing the failed components. This leaves little time to set the actual diagnosis. And; furthermore, the choice of implant solution is inherently restricted to readily available off-the-shelf standard implants, as really custom-made implant solutions are implicitly excluded.After all, really customized implant solutions, such as a three-flanged implant for acetabular reconstruction [11, 12], possibly combined with structural and/or morselised allografts and possibly used in combination with bone quality analysis and screw guidance jigs, requires a precise 3D-image-based preoperative planning, and, thus, entail a lead time—ranging from just a couple of weeks to numerous months depending on the supplier.In response, modular systems with standard defect filling augmentation components presently gain popularity [13]. However, especially for the extremely wide defects such as Paprosky’s types III, the question remains if the optimal solution is selected. This in view of long-term stability and restoration of functionality whereas the assembled construct, which consists out of multiple separate components, should fill and span the wide and often uncontained defect and engage closely to the specific surrounding bony situation, and also in consideration of preserving the few remaining but extremely valuable bone stock, whereas modular systems are standard in shape and thus may require reaming.A precise preoperative classification of bone loss would enable the clinician to make a proper selection from a wide and unlimited range of implant solutions.

First, a CT scan is acquired and processed in a dedicated image processing software (Mimics, Materialise NV, Leuven, Belgium) (Figure 1(a)). A three-dimensional bone surface model of the defective hemipelvis is then calculated (Figure 1(b)). (STL mesh, Standard Triangulation Language, Marching Cubes algorithm [17], accuracy settings in accordance to Gelaude et al. [15]).

The 3D bone models of the deficient hemi-pelves are displayed in Figure 8 (in red). The corresponding 3D reconstructed bone models are shown in overlay in the same figure (in green, transparently).

A novel quantitative and computerized method was presented to assess the degree of total bone loss, measured in radial direction from the centre of the reconstructed acetabulum (abbreviated “TrABL”). Ingredients for this method are advanced 3D CT-based image processing and effective 3D anatomical reconstruction methodology which have previously been validated and published elsewhere in the literature. The method was implemented and applied on a first dataset of twelve hemi-pelves, mainly the Paprosky type IIIB. The quantitative output parameters are the TrABL ratio and graph, which can be used directly for comparison between specimens.

 

Source:

http://doi.org/10.4061/2011/494382

 

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