Calibrating multibody ulno-humeral joint cartilage using a validated finite element model

Renani, Mohsen Sharifi; Rahman, Munsur; Çil, Akın; Stylianou, Antonis P.

doi:10.1007/s11044-018-9622-y

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…In this scenario, in silico modeling can support comparisons among surgical techniques. Specifically, multibody analysis represents an effective computational approach to predict the biomechanical behavior of articular joints in terms of, for instance, range of motion (Zanetti et al, 2018) as well as intra-articular loads (Renani et al, 2018). When these models include also soft tissue, such as muscles and ligaments, both active and passive forces, respectively, can be estimated under some simplified hypothesis (Guess et al, 2016; Zanetti et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Surgical Treatments for Canine Anterior Cruciate Ligament Rupture: Assessing Functional Recovery Through Multibody Comparative Analysis

Putame

Terzini

Bignardi

et al. 2019

Front. Bioeng. Biotechnol.

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Anterior cruciate ligament (ACL) deficiency can result in serious degenerative stifle injuries. Although tibial plateau leveling osteotomy (TPLO) is a common method for the surgical treatment of ACL deficiency, alternative osteotomies, such as a leveling osteotomy based on the center of rotation of angulation (CBLO) are described in the literature. However, whether a CBLO could represent a viable alternative to a TPLO remains to be established. The aim of this study is to compare TPLO and CBLO effectiveness in treating ACL rupture. First, a computational multibody model of a physiological stifle was created using three-dimensional surfaces of a medium-sized canine femur, tibia, fibula and patella. Articular contacts were modeled by means of a formulation describing the contact force as function of the interpenetration between surfaces. Moreover, ligaments were represented by vector forces connecting origin and insertion points. The lengths of the ligaments at rest were optimized simulating the drawer test. The ACL-deficient model was obtained by deactivating the ACL related forces in the optimized physiological one. Then, TPLO and CBLO treatments were virtually performed on the pathological stifle. Finally, the drawer test and a weight-bearing squat movement were performed to compare the treatments effectiveness in terms of tibial anteroposterior translation, patellar ligament force, intra-articular compressive force and quadriceps force. Results from drawer test simulations showed that ACL-deficiency causes an increase of the anterior tibial translation by up to 5.2 mm, while no remarkable differences between CBLO and TPLO were recorded. Overall, squat simulations have demonstrated that both treatments lead to an increase of all considered forces compared to the physiological model. Specifically, CBLO and TPLO produce an increase in compressive forces of 54% and 37%, respectively, at 90° flexion. However, TPLO produces higher compressive forces (up to 16%) with respect to CBLO for wider flexion angles ranging from 135° to 117°. Conversely, TPLO generates lower forces in patellar ligament and quadriceps muscle, compared to CBLO. In light of the higher intra-articular compressive force over the physiological walking range of flexion, which was observed to result from TPLO in the current study, the use of this technique should be carefully considered.

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Section: Introductionmentioning

confidence: 99%

Surgical Treatments for Canine Anterior Cruciate Ligament Rupture: Assessing Functional Recovery Through Multibody Comparative Analysis

Putame

Terzini

Bignardi

et al. 2019

Front. Bioeng. Biotechnol.

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“…Our models were based on rigid body dynamics. However, future studies will also optimize contact parameters and discretized cartilage size by matching a multibody cartilage model with a finite element model of the same cartilage [35,36]. Even with this limitation, our maximum contact pressures were close in range (0.5–5 Mpa) to the values reported in the literature [17,37].…”

Section: Discussionmentioning

confidence: 83%

Medial Collateral Ligament Deficiency of the Elbow Joint: A Computational Approach

2018

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Computational elbow joint models, capable of simulating medial collateral ligament deficiency, can be extremely valuable tools for surgical planning and refinement of therapeutic strategies. The objective of this study was to investigate the effects of varying levels of medial collateral ligament deficiency on elbow joint stability using subject-specific computational models. Two elbow joint models were placed at the pronated forearm position and passively flexed by applying a vertical downward motion on humeral head. The models included three-dimensional bone geometries, multiple ligament bundles wrapped around the joint, and the discretized cartilage representation. Four different ligament conditions were simulated: All intact ligaments, isolated medial collateral ligament (MCL) anterior bundle deficiency, isolated MCL posterior bundle deficiency, and complete MCL deficiency. Minimal kinematic differences were observed for isolated anterior and posterior bundle deficient elbows. However, sectioning the entire MCL resulted in significant kinematic differences and induced substantial elbow instability. Joint contact areas were nearly similar for the intact and isolated posterior bundle deficiency. Minor differences were observed for the isolated anterior bundle deficiency, and major differences were observed for the entire MCL deficiency. Complete elbow dislocations were not observed for any ligament deficiency level. As expected, during isolated anterior bundle deficiency, the remaining posterior bundle experiences higher load and vice versa. Overall, the results indicate that either MCL anterior or posterior bundle can provide anterior elbow stability, but the anterior bundle has a somewhat bigger influence on joint kinematics and contact characteristics than posterior one. A study with a larger sample size could help to strengthen the conclusion and statistical significant.

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“…A custom macro was written in ADAMS to automatically divide the humerus cartilage into discrete hexahedral elements. The macro performance was successfully tested in many of our previous lab studies [ 15 , 24 , 25 , 26 , 27 , 28 , 29 ]. Each cartilage element had an approximate 3 × 3 mm cross-sectional area.…”

Section: Methodsmentioning

confidence: 99%

“…Optimization and design of the experiment approach were used to determine the contact parameters and the size of discretized cartilage elements from a cadaver study ( Table 1 ). The optimization was done in such a way that the maximum contact pressure and contact area errors were minimized between a multibody model and an identically loaded finite element model [ 27 , 30 ].…”

Section: Methodsmentioning

confidence: 99%

“…Local coordinate systems for each bone segment were created to measure the ulna and radius motions relative to the humerus [ 27 ]. The translations were represented as medial–lateral (M–L), anterior–posterior (A–P), and superior–inferior (S–I) directions and the rotations were represented as flexion–extension (F–E), varus–valgus (VR–VL), and internal–external (I–E) rotation.…”

Section: Methodsmentioning

confidence: 99%

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Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

et al. 2018

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A dynamic musculoskeletal model of the elbow joint in which muscle, ligament, and articular surface contact forces are predicted concurrently would be an ideal tool for patient-specific preoperative planning, computer-aided surgery, and rehabilitation. Existing musculoskeletal elbow joint models have limited clinical applicability because of idealizing the elbow as a mechanical hinge joint or ignoring important soft tissue (e.g., cartilage) contributions. The purpose of this study was to develop a subject-specific anatomically correct musculoskeletal elbow joint model and evaluate it based on experimental kinematics and muscle electromyography measurements. The model included three-dimensional bone geometries, a joint constrained by multiple ligament bundles, deformable contacts, and the natural oblique wrapping of ligaments. The musculoskeletal model predicted the bone kinematics reasonably accurately in three different velocity conditions. The model predicted timing and number of muscle excitations, and the normalized muscle forces were also in agreement with the experiment. The model was able to predict important in vivo parameters that are not possible to measure experimentally, such as muscle and ligament forces, and cartilage contact pressure. In addition, the developed musculoskeletal model was computationally efficient for body-level dynamic simulation. The maximum computation time was less than 30 min for our 35 s simulation. As a predictive clinical tool, the potential medical applications for this model and modeling approach are significant.

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Calibrating multibody ulno-humeral joint cartilage using a validated finite element model

Cited by 6 publications

References 30 publications

Surgical Treatments for Canine Anterior Cruciate Ligament Rupture: Assessing Functional Recovery Through Multibody Comparative Analysis

Surgical Treatments for Canine Anterior Cruciate Ligament Rupture: Assessing Functional Recovery Through Multibody Comparative Analysis

Medial Collateral Ligament Deficiency of the Elbow Joint: A Computational Approach

Musculoskeletal Model Development of the Elbow Joint with an Experimental Evaluation

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