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). 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.Introduction
Methods
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. 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.Introduction
Methods
