Personalised statistical modelling of soft tissue structures in the ankle

Peiffer, Matthias; Burssens, Arne; Duquesne, Kate; Last, Mark; Mits, Sophie De; Victor, Jan; Audenaert, Emmanuel

doi:10.1016/j.cmpb.2022.106701

Cited by 16 publications

(16 citation statements)

References 39 publications

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“…Tube formation, as described by Audenaert and colleagues, was based on a generalized cylinder model. Over the path of a spline, connecting the anterior to the posterior meniscal root, the point-dependent offset was imposed to avoid osseous and cartilage penetration and thus force the tube to wrap around the femoral condyle ( Audenaert et al, 2009 ; Audenaert et al, 2019b ; Peiffer et al, 2022a ). Second, according to the calculated path and at equidistant interval, triangles were defined with height and width derived from the above described polynomial estimates, with the width projected towards the meniscal center and the height orthogonal to this.…”

Section: Methodsmentioning

confidence: 99%

“…Ligament geometry and path prediction was based on a custom-made mesh wrapping algorithm as previously described by ( Audenaert et al, 2009 ; Audenaert et al, 2019b ; Peiffer et al, 2022a ). This mesh wrapping algorithm was based on path prediction of the psoas anatomy as described by Audenaert et al A finite number of springs formed an elastic membrane that was iteratively released to progressively minimize the potential energy while not permitting penetration of underlying structures ( Audenaert et al, 2019b ).…”

Section: Methodsmentioning

confidence: 99%

“…The combined methodology to describe bone, cartilage and soft tissue at a population wide level was recently adopted for the ankle joint by ( Peiffer et al, 2022a ). In this study, the techniques described by Van Houcke et al and Audenaert et al were combined for estimation of cartilage topography of the tibiotalar joint and inclusion of the main ankle ligament paths ( Audenaert et al, 2019b ; van Houcke et al, 2020a ; Peiffer et al, 2022a ). However, no similar advancements have been made for the knee joint.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this study is to develop a generic computational model that can easily be scaled, morphed and fitted to patient-specific knee joint anatomy, avoiding laborious segmentation tasks and improving accuracy of patient-specific geometry predictions. This study builds further on in-house available expertise regarding statistical shape modeling and soft tissue wrapping methodology ( Audenaert et al, 2019b ; van Houcke et al, 2020a ; Peiffer et al, 2022a ). The objectives of the current study are: 1.…”

Section: Introductionmentioning

confidence: 99%

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Personalized statistical modeling of soft tissue structures in the knee

Oevelen

Duquesne²,

Peiffer³

et al. 2023

Front. Bioeng. Biotechnol.

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Background and Objective: As in vivo measurements of knee joint contact forces remain challenging, computational musculoskeletal modeling has been popularized as an encouraging solution for non-invasive estimation of joint mechanical loading. Computational musculoskeletal modeling typically relies on laborious manual segmentation as it requires reliable osseous and soft tissue geometry. To improve on feasibility and accuracy of patient-specific geometry predictions, a generic computational approach that can easily be scaled, morphed and fitted to patient-specific knee joint anatomy is presented.Methods: A personalized prediction algorithm was established to derive soft tissue geometry of the knee, originating solely from skeletal anatomy. Based on a MRI dataset (n = 53), manual identification of soft-tissue anatomy and landmarks served as input for our model by use of geometric morphometrics. Topographic distance maps were generated for cartilage thickness predictions. Meniscal modeling relied on wrapping a triangular geometry with varying height and width from the anterior to the posterior root. Elastic mesh wrapping was applied for ligamentous and patellar tendon path modeling. Leave-one-out validation experiments were conducted for accuracy assessment.Results: The Root Mean Square Error (RMSE) for the cartilage layers of the medial tibial plateau, the lateral tibial plateau, the femur and the patella equaled respectively 0.32 mm (range 0.14–0.48), 0.35 mm (range 0.16–0.53), 0.39 mm (range 0.15–0.80) and 0.75 mm (range 0.16–1.11). Similarly, the RMSE equaled respectively 1.16 mm (range 0.99–1.59), 0.91 mm (0.75–1.33), 2.93 mm (range 1.85–4.66) and 2.04 mm (1.88–3.29), calculated over the course of the anterior cruciate ligament, posterior cruciate ligament, the medial and the lateral meniscus.Conclusion: A methodological workflow is presented for patient-specific, morphological knee joint modeling that avoids laborious segmentation. By allowing to accurately predict personalized geometry this method has the potential for generating large (virtual) sample sizes applicable for biomechanical research and improving personalized, computer-assisted medicine.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Personalized statistical modeling of soft tissue structures in the knee

Oevelen

Duquesne²,

Peiffer³

et al. 2023

Front. Bioeng. Biotechnol.

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“…Progress in computer vision and image analysis now offers efficient methods to establish anatomical correspondence between knee shapes and to describe the joint space to perform statistical comparison and pattern analysis of sets of joint space narrowing maps (i.e., convolutional neural networks following conformal mesh parameterization) (Audenaert et al, 2019a;Nauwelaers et al, 2021). Whereas Favre and colleagues aimed for anatomical correspondence relying on standardized two-dimensional pixel-maps, anatomical correspondence in 3D can be obtained by nonrigid surface registration of a reference template thereby providing a dense set of homologous landmarks amenable to statistical analysis (Williams et al, 2010;Favre et al, 2013;Audenaert et al, 2019a;van Houcke et al, 2020;Peiffer et al, 2022). This permits statistical comparison between and within populations of skeletal shape and related joint space geometry (Williams et al, 2010;Audenaert et al, 2019a;Nauwelaers et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Wear patterns in knee OA correlate with native limb geometry

Oevelen

Borre

Duquesne

et al. 2022

Front. Bioeng. Biotechnol.

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Background: To date, the amount of cartilage loss is graded by means of discrete scoring systems on artificially divided regions of interest (ROI). However, optimal statistical comparison between and within populations requires anatomically standardized cartilage thickness assessment. Providing anatomical standardization relying on non-rigid registration, we aim to compare morphotypes of a healthy control cohort and virtual reconstructed twins of end-stage knee OA subjects to assess the shape-related knee OA risk and to evaluate possible correlations between phenotype and location of cartilage loss.Methods: Out of an anonymized dataset provided by the Medacta company (Medacta International SA, Castel S. Pietro, CH), 798 end-stage knee OA cases were extracted. Cartilage wear patterns were observed by computing joint space width. The three-dimensional joint space width data was translated into a two-dimensional pixel image, which served as the input for a principal polynomial autoencoder developed for non-linear encoding of wear patterns. Virtual healthy twin reconstruction enabled the investigation of the morphology-related risk for OA requiring joint arthroplasty.Results: The polynomial autoencoder revealed 4 dominant, orthogonal components, accounting for 94% of variance in the latent feature space. This could be interpreted as medial (54.8%), bicompartmental (25.2%) and lateral (9.1%) wear. Medial wear was subdivided into anteromedial (11.3%) and posteromedial (10.4%) wear. Pre-diseased limb geometry had a positive predictive value of 0.80 in the prediction of OA incidence (r 0.58, p < 0.001).Conclusion: An innovative methodological workflow is presented to correlate cartilage wear patterns with knee joint phenotype and to assess the distinct knee OA risk based on pre-diseased lower limb morphology. Confirming previous research, both alignment and joint geometry are of importance in knee OA disease onset and progression.

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Application of external torque enhances the detection of subtle syndesmotic ankle instability in a weight‐bearing CT

Peiffer,

Dhont,

Cuigniez

et al. 2023

Knee surg. sports traumatol. arthrosc.

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Purpose Acute syndesmotic ankle injuries continue to impose a diagnostic dilemma and it remains unclear whether weightbearing and/or external rotation should be added during the imaging process. Therefore, the aim of this study was to assess if combined weightbearing and external rotation increases the diagnostic sensitivity of syndesmotic ankle instability using weightbearing CT (WBCT) imaging, compared to isolated weightbearing. Methods In this retrospective study, patients with an acute syndesmotic ankle injury were analysed using a WBCT (N = 21; Age = 31.6 ± 14.1 years old). Inclusion criteria were an MRI conirmed syndesmotic ligament injury imaged by a WBCT of the ankle during weightbearing and combined weightbearing-external rotation. Exclusion criteria consisted of fracture associated syndesmotic injuries. Three-dimensional (3D) models were generated from the CT slices. Tibioibular displacement and talar rotation were quantiied using automated 3D measurements (anterior tibioibular distance (ATFD), Alpha angle, posterior Tibioibular distance (PTFD) and Talar rotation (TR) angle in comparison to the contralateral non-injured ankle. ResultsThe diference in neutral-stressed Alpha angle and ATFD showed a signiicant diference between patients with a syndesmotic ankle lesion and contralateral control (P = 0.046 and P = 0.039, respectively). The diference in neutral-stressed PTFD and TR angle did not show a signiicant diference between patients with a syndesmotic ankle lesion and healthy ankles (n.s.). Conclusion Application of combined weightbearing-external rotation reveals an increased ATFD in patients with syndesmotic ligament injuries. This study provides the irst insights based on 3D measurements to support the potential relevance of applying external rotation during WBCT imaging. In clinical practice, this could enhance the current diagnostic accuracy of subtle syndesmotic instability in a non-invasive manner. However, to what extent certain displacement patterns require operative treatment strategies has yet to be determined in future studies. Level of evidence Level III.

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Personalised statistical modelling of soft tissue structures in the ankle

Cited by 16 publications

References 39 publications

Personalized statistical modeling of soft tissue structures in the knee

Personalized statistical modeling of soft tissue structures in the knee

Wear patterns in knee OA correlate with native limb geometry

Application of external torque enhances the detection of subtle syndesmotic ankle instability in a weight‐bearing CT

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