Data fusion for modelling in paediatric oncology F. Taddei 1 , S. Martelli 1 , G. Valente 1 , A. Leardini 2 , M. G. Benedetti 2 , M. Manfrini 3 , M. Viceconti 1 1 Istituto Ortopedico Rizzoli, Medical Technology Laboratory, Bologna, Italy 2 Istituto Ortopedico Rizzoli, Movement Analysis Laboratory, Bologna, Italy 3 Istituto Ortopedico Rizzoli, 5th Ward of Oncologic Orthopaedic-Trauma Surgery, Bologna, Italy Introduction Limb-salvage surgery is increasingly adopted in the treatment of primary bone tumours, affecting principally growing subjects at the long bones of the lower limbs [1]. Biological reconstructions, using intercalary massive allograft to recover the excised bone, present some acknowledged advantages especially when the patient’s joint can be spared. Those reconstructions present a high long term survival rates (75-89% at 10 years) but still allograft fractures are one of the major complications. To limit the risk of fracture, rehabilitation therapy is managed preventing a complete weight bearing for long periods, which is considered discouraging for the adoption of these surgical techniques. It would be important to deeply understand the biomechanics of reconstructed limbs either to improve the surgical technique, with respect to the mechanical failure of the implants, and to verify if shorter rehabilitation protocols may be adopted, without increasing the fracture risk. The first step for the evaluation of the fracture risk of the reconstructed bone segments is the evaluation of the loads acting on the skeleton during daily motor tasks. Direct and non-invasive measurements in-vivo of muscle forces is impossible, hence it is necessary to estimate them using computational models of the musculoskeletal system. Most recent studies have demonstrated the need for relying on a subject-specific musculoskeletal models especially when abnormalities of the skeleton geometry, and/or on the muscular system, are present. The development of such personalised musculoskeletal models requires the fusion of data coming from different sources (e.g. medical imaging data, gait analysis data, etc.). Aim of the present study is to propose a method for developing subject-specific musculoskeletal models of the lower limbs and apply them on a real clinical case to predict the joint and muscles’ forces during normal walking in a young male subject with a massive skeletal reconstruction. Materials and Methods The selected patient was operated at the age of 10 for an Osteosarcoma at the distal part of his left femur. An intercalary reconstruction was performed by means of a massive bone allograft in conjunction with a vascularized fibula autograft. Fixation was provided by a titanium plate with screws. Fig. 1On the left: the CT dataset with the visible skin markers used for the gait analysis. On the right the musculoskeletal model of the patient lower limb At month 42 of follow-up the patient underwent a Computed Tomography (CT) exam at the lower limbs for clinical reasons. In that occasion, a gait analysis session was performed, and a specific protocol was adopted to allow the registration of the patient’s skeleton with the kinematics recorded during the gait session. Thirty-four reflective markers were applied to the patient’s skin prior to CT scanning, so their positions relative to the patient skeleton could be estimated (Fig. 1). A subject-specific musculoskeletal model was build, registered with the recorded kinematics, and used to understand the biomechanics of the reconstructed bone as explained below. The musculoskeletal model. The skeletal anatomy was segmented from the CT dataset (Amira® v. 4.1, Mercury Computer System, Inc., USA). The biomechanical model of the musculoskeletal system of the lower-limb was defined as a 7-segment (pelvis, femurs, tibiae, foot), 10 degree-of-freedom (DOF) articulated system actuated by 82 Hill-type muscle-tendon units (Fig. 1). Each leg was articulated by three ideal joints: a ball and socket at the hip (3 DOF) and a hinge (1 DOF) at both the knee and the ankle [2]. The identification of joint parameters was based on relevant skeletal landmarks identified on the skeletal surface with the virtual palpation procedure [3] (LHPBuilder©,SCS, Italy). All anatomical landmarks defined by ISB standards were identified and the local coordinate system was computed for each segment. A generic muscular model of the lower extremity including 82 muscles [4] was manually registered on the skeletal anatomy. The muscular system of the operated leg accounted for modification induced by surgery. Each muscle was modelled as a Hill-type actuator for which the muscle tetanic force, the tendon slack length and the optimal muscle length were personalised fitting individual data. All the other necessary parameters were taken form literature. Inertial parameters of each segment were derived from CT data assuming homogeneous density properties for both the hard and the soft tissues. Gait simulations were run using the OpenSim software [4]. A least-square algorithm was used in a preliminary inverse kinematics analysis to predict the joints kinematics from marker trajectories. Musculoskeletal dynamics simulations were then run by input these kinematics and the recordings of the ground reaction forces. All five gait repetitions were analysed. Model validation. The models were validated comparing predicted activations of some of the major muscles of the operated leg with EMG recordings. Results All gaits were successfully analysed. Validation Consistent muscle activation patterns were predicted throughout the five simulated repetitions. Comparing the predicted muscle excitations with the EMG recordings (available for a single trial), a general agreement was found