Lazaros Vlachopoulos1,2, Marcel Lüthi3, Fabio Carrillo1, Christian Gerber1, Gábor Székely2, Philipp Fürnstahl1. 1. Computer Assisted Research and Development Group (L.V., F.C., and P.F.) and Department of Orthopaedics (C.G.), Balgrist University Hospital, University of Zurich, Zurich, Switzerland. 2. Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland. 3. Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland.
Abstract
BACKGROUND: In computer-assisted reconstructive surgeries, the contralateral anatomy is established as the best available reconstruction template. However, existing intra-individual bilateral differences or a pathological, contralateral humerus may limit the applicability of the method. The aim of the study was to evaluate whether a statistical shape model (SSM) has the potential to predict accurately the pretraumatic anatomy of the humerus from the posttraumatic condition. METHODS: Three-dimensional (3D) triangular surface models were extracted from the computed tomographic data of 100 paired cadaveric humeri without a pathological condition. An SSM was constructed, encoding the characteristic shape variations among the individuals. To predict the patient-specific anatomy of the proximal (or distal) part of the humerus with the SSM, we generated segments of the humerus of predefined length excluding the part to predict. The proximal and distal humeral prediction (p-HP and d-HP) errors, defined as the deviation of the predicted (bone) model from the original (bone) model, were evaluated. For comparison with the state-of-the-art technique, i.e., the contralateral registration method, we used the same segments of the humerus to evaluate whether the SSM or the contralateral anatomy yields a more accurate reconstruction template. RESULTS: The p-HP error (mean and standard deviation, 3.8° ± 1.9°) using 85% of the distal end of the humerus to predict the proximal humeral anatomy was significantly smaller (p = 0.001) compared with the contralateral registration method. The difference between the d-HP error (mean, 5.5° ± 2.9°), using 85% of the proximal part of the humerus to predict the distal humeral anatomy, and the contralateral registration method was not significant (p = 0.61). The restoration of the humeral length was not significantly different between the SSM and the contralateral registration method. CONCLUSIONS: SSMs accurately predict the patient-specific anatomy of the proximal and distal aspects of the humerus. The prediction errors of the SSM depend on the size of the healthy part of the humerus. CLINICAL RELEVANCE: The prediction of the patient-specific anatomy of the humerus is of fundamental importance for computer-assisted reconstructive surgeries.
BACKGROUND: In computer-assisted reconstructive surgeries, the contralateral anatomy is established as the best available reconstruction template. However, existing intra-individual bilateral differences or a pathological, contralateral humerus may limit the applicability of the method. The aim of the study was to evaluate whether a statistical shape model (SSM) has the potential to predict accurately the pretraumatic anatomy of the humerus from the posttraumatic condition. METHODS: Three-dimensional (3D) triangular surface models were extracted from the computed tomographic data of 100 paired cadaveric humeri without a pathological condition. An SSM was constructed, encoding the characteristic shape variations among the individuals. To predict the patient-specific anatomy of the proximal (or distal) part of the humerus with the SSM, we generated segments of the humerus of predefined length excluding the part to predict. The proximal and distal humeral prediction (p-HP and d-HP) errors, defined as the deviation of the predicted (bone) model from the original (bone) model, were evaluated. For comparison with the state-of-the-art technique, i.e., the contralateral registration method, we used the same segments of the humerus to evaluate whether the SSM or the contralateral anatomy yields a more accurate reconstruction template. RESULTS: The p-HP error (mean and standard deviation, 3.8° ± 1.9°) using 85% of the distal end of the humerus to predict the proximal humeral anatomy was significantly smaller (p = 0.001) compared with the contralateral registration method. The difference between the d-HP error (mean, 5.5° ± 2.9°), using 85% of the proximal part of the humerus to predict the distal humeral anatomy, and the contralateral registration method was not significant (p = 0.61). The restoration of the humeral length was not significantly different between the SSM and the contralateral registration method. CONCLUSIONS: SSMs accurately predict the patient-specific anatomy of the proximal and distal aspects of the humerus. The prediction errors of the SSM depend on the size of the healthy part of the humerus. CLINICAL RELEVANCE: The prediction of the patient-specific anatomy of the humerus is of fundamental importance for computer-assisted reconstructive surgeries.
Authors: Matthijs Jacxsens; Shireen Y Elhabian; Sarah E Brady; Peter N Chalmers; Andreas M Mueller; Robert Z Tashjian; Heath B Henninger Journal: J Orthop Res Date: 2020-01-24 Impact factor: 3.494
Authors: Tatjana Pastor; Ladislav Nagy; Philipp Fürnstahl; Simon Roner; Torsten Pastor; Andreas Schweizer Journal: Medicina (Kaunas) Date: 2022-05-30 Impact factor: 2.948