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Orthopaedic Proceedings
Vol. 96-B, Issue SUPP_16 | Pages 6 - 6
1 Oct 2014
Charbonnier C Chagué S Kolo F Lädermann A
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Shoulder instability and impingement are common in tennis players. During tennis, several impingements could occur: subcoracoid and anterosuperior impingements at the follow-through phase of forehand and the backhand preparation phase; subacromial and postero-superior impingements at the cocking phase of serve. The precise causes for these impingements remain unclear, but it is believed that repetitive contact, glenohumeral instability may play a role.

Impingement and glenohumeral instability at critical tennis positions have never been dynamically evaluated in-vivo. The purpose of this study was to develop a patient-specific measurement technique based on motion capture and MRI to accurately determine glenohumeral kinematics (rotations and translations). The second objective was to evaluate impingements and stability in tennis.

Shoulder MR arthrography and motion capture were performed in 10 tennis players. Motion data were recorded during tennis movements. Glenohumeral kinematics was computed from the markers trajectories using a global optimisation algorithm with loose constraints on joint translations (accuracy: translational error ≈3mm, rotational error <4°). The translations patterns computed with the model were in good agreement with previous works. The resulting computed motions were applied to the subject's shoulder 3D bony models reconstructed from MRI data.

While simulating the shoulder joint, minimum humero-acromial, humero-coracoid and humero-glenoid distances were measured at critical tennis positions. Given the thickness of the potential impinged tissues, impingement was considered when the computed distance was <5 mm (<6 mm for the humero-acromial distance). During serve, glenohumeral stability was assessed at the cocking, deceleration and finish phases. Glenohumeral translation was defined as the anterior-posterior and superior-inferior motion of the humeral head centre relative to a glenoid coordinate system. Subluxation was defined as the ratio (in %) between the humeral head centre translation and the radius of the width (antero-posterior subluxation) or height (supero-inferior subluxation) of the glenoid surface. Instability was thus considered when the subluxation was >50%, corresponding to a loss of congruence superior to half the radius of the width (or height) of the glenoid.

No subcoracoid impingement was detected. Antero-superior impingements were observed in two subjects (29%) during forehand. Anterior and lateral subacromial impingements occurred during the cocking phase of serve in three (29%) and four subjects (42%), respectively. Postero-superior impingements during the cocking phase of serve were the most frequent (7 subjects, 75%). In this position, glenohumeral translation was anterior (mean: 34%) and superior (mean: 13%). During the deceleration phase, anterior and superior translation varied between 8–57% and between 5–34%, respectively. During the finish phase, anterior translation was slightly more intense (mean: 44%), while superior translation remained low (mean: 1%). MRI revealed eleven rotator cuff lesions in five subjects, and six labral lesions in five subjects.

Postero-superior impingement was frequent when serving. No instability could be noted. Tennis players presented frequent radiographic signs of structural lesions which seem to be mainly related to postero-superior impingement due to repetitive abnormal motion contacts. Our findings are consistent with this hypothesis. To our knowledge, this is the first study demonstrating that a dynamic and precise motion analysis of the shoulder is feasible using an external measurement system, such as motion capture.


Orthopaedic Proceedings
Vol. 95-B, Issue SUPP_34 | Pages 27 - 27
1 Dec 2013
Charbonnier C Chague S Ponzoni M Bernardoni M Hoffmeyer P Christofilopoulos P
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Introduction

Conventional pre-operative planning for total hip arthroplasty mostly relies on the patient radiologic anatomy for the positioning and choice of implants. This kind of planning essentially remains a static approach since dynamic aspects such as the joint kinematics are not taken into account. Hence, clinicians are not able to fully consider the evolving behavior of the prosthetic joint that may lead to implant failures. In fact, kinematics plays an important role since some movement may create conflicts within the prosthetic joint and even provoke dislocations. The goal of our study was to assess the relationship between acetabular implant positioning variations and resultant impingements and loss of joint congruence during daily activities. In order to obtain accurate hip joint kinematics for simulation, we performed an in-vivo study using optical motion capture and magnetic resonance imaging (MRI).

Methods

Motion capture and MRI was carried out on 4 healthy volunteers (mean age, 28 years). Motion from the subjects was acquired during routine (stand-to-sit, lie down) and specific activities (lace the shoes while seated, pick an object on the floor while seated or standing) known to be prone to implant dislocation and impingement. The hip joint kinematics was computed from the recorded markers trajectories using a validated optimized fitting algorithm (accuracy: translational error ≍ 0.5 mm, rotational error < 3°) which accounted for skin motion artifactsand patient-specific anatomical constraints (e.g. bone geometry reconstructed from MRI, hip joint center) (Fig. 1).

3D models of prosthetic hip joints (pelvis, proximal femur, cup, stem, head) were developed based on variations of acetabular cup's inclination (40°, 45°, 60°) and anteversion (0°, 15°, 30°) parameters, resulting in a total of 9 different implant configurations. Femoral anteversion remained fixed and determined as “neutral” with the stem being parallel to the posterior cortex of the femoral neck. Motion capture data of daily tasks were applied to all implant configurations.

While visualizing the prosthetic models in motion, a collision detection algorithm was used to locate abnormal contacts between both bony and prosthetic components (Fig. 2). Moreover, femoral head translations (subluxation) were computed to evaluate the joint congruence.


Orthopaedic Proceedings
Vol. 95-B, Issue SUPP_34 | Pages 29 - 29
1 Dec 2013
Charbonnier C Christofilopoulos P Chague S Schmid J Bartolone P Hoffmeyer P
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Introduction

Today, there is no clear consensus as to the amplitude of movement of the “normal hip”. Knowing the necessary joint mobility for everyday life is important to understand different pathologies and to better plan their treatments. Moreover, determining the hip range of motion (ROM) is one of the key points of its clinical examination. Unfortunately this process may lack precision because of movement of other joints around the pelvis. Our goal was to perform a preliminary study based on the coupling of MRI and optical motion capture to define precisely the necessary hip joint mobility for everyday tasks and to assess the accuracy of the hip ROM clinical exam.

Methods

MRI was carried out on 4 healthy volunteers (mean age, 28 years). A morphological analysis was performed to assess any bony abnormalities. Two motion capture sessions were conducted: one aimed at recording routine activities (stand-to-sit, lie down, lace the shoes while seated, pick an object on the floor while seated or standing) known to be painful or prone to implant failures. During the second session, a hip clinical exam was performed successively by 2 orthopedists (2 and 12 years' experience), while the motion of the subjects was simultaneously recorded (Fig.1). These sequences were captured: 1) supine: maximal flexion, maximal IR/ER with hip flexed 90°, maximal abduction; 2) seated: maximal IR/ER with hip and knee flexed 90°. A hand held goniometer was used by clinicians to measure hip angles in those different positions.

Hip joint kinematics was computed from the markers trajectories using a validated optimized fitting algorithm which accounted for skin motion artifacts (accuracy: translational error≍0.5 mm, rotational error <3°). The resulting computed motions were applied to patient-specific hip joint 3D models reconstructed from their MRI data (Fig. 2). Hip angles were determined at each point of the motion thanks to two bone coordinate systems (pelvis and femur). The orthopedist's results were compared.