Benjamin Gilles

Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations

The automatic segmentation of the musculoskeletal system from medical images is a particularly challenging task, due to its morphological complexity, its large variability in the population and its potentially large deformations. In this paper we propose a novel approach for musculoskeletal segmentation and registration based on simplex meshes. Such discrete models have already proven to be efficient and versatile for medical image segmentation. We extend the current framework by introducing a multi-resolution approach and a reversible medial representation, in order to reduce the complexity of geometric and non-penetration constraints computation. Our framework allows both inter and intra-patient registration (involving both rigid and elastic matching). We also show that the introduced representations facilitate morphological analysis. As a case study, we demonstrate that muscles, bones, ligaments and cartilages of the hip and the thigh can be registered at an interactive frame rate, in a time-efficient way (<30min), with a satisfactory accuracy ( approximately 1.5mm), and with a minimal amount of manual tasks.

Anatomical modelling of the musculoskeletal system from MRI

This paper presents a novel approach for multi-organ (musculoskeletal system) automatic registration and segmentation from clinical MRI datasets, based on discrete deformable models (simplex meshes). We reduce the computational complexity using multi-resolution forces, multi-resolution hierarchical collision handling and large simulation time steps (implicit integration scheme), allowing real-time user control and cost-efficient segmentation. Radial forces and topological constraints (attachments) are applied to regularize the segmentation process. Based on a medial axis constrained approximation, we efficiently characterize shapes and deformations. We validate our methods for the hip joint and the thigh (20 muscles, 4 bones) on 4 datasets: average error = 1.5 mm, computation time = 15 min.

MRI-based Assessment of Hip Joint Translations

To better understand movement limitations and, to some extent, the pathogenesis of osteoarthritis, it is important to quantitatively measure femoroacetabular translations to assess if any joint subluxation occurs. In this paper, we aim at measuring hip joint displacements from magnetic resonance images (MRI) based on a surface registration technique. Because this measurement is related to the location of the hip joint center (HJC), we investigate and compare different HJC estimation approaches based on patient-specific 3D bone models. We estimate the HJC based on a simulated circumduction while minimizing inter-articular distance changes. Measurements of femoroacetabular translations during low amplitude abductions (80 samples) and extreme flexions (60 samples) in female professional dancers, which is a population potentially exposed to femoroacetabular impingements, do not show any significant subluxation.

Motion capture and visualization of the hip joint with dynamic MRI and optical systems

We present a methodology for motion tracking and visualization of the hip joint by combining MR images and optical motion capture systems. MRI is typically used to capture the subjects anatomy while optical systems are used to capture and analyse the relative movement between adjacent bones of the joint. Reflective markers are attached to the subjects skin and their trajectories are tracked and processed. However, the skin surface deforms while in motion due to muscle contraction leading to significant errors in the estimation of trajectories. In order to reduce these errors, we use MR images to capture both the anatomy and the trajectories of the bone. Prior to the scanning, we attach skin markers to the subject in order to analyse the markers displacements relative to the bone. We reconstruct the anatomical models of the subject and we compute the markers trajectories from the images. Using these calculated trajectories, we select the best markers configuration based on the criteria of markers displacements. The optimized configuration is used for recording external movements with the optical motion capture system. The resulting animation is mapped onto the virtual body of the subject including internal bones and the joint motion is visualized. Copyright

Bone Motion Analysis from Dynamic MRI: Acquisition and Tracking

RATIONALE AND OBJECTIVES For diagnosis, preoperative planning and postoperative guides, an accurate estimate of joint kinematics is required. It is important to acquire joint motion actively with real-time protocols. MATERIALS AND METHODS We bring together MRI developments and new image processing methods in order to automatically extract active bone kinematics from multi-slice real-time dynamic MRI. We introduce a tracking algorithm based on 2D/3D registration and a procedure to validate the technique by using both dynamic and sequential MRI, providing a gold standard bone position measurement. RESULTS We present our technique for optimizing jointly the tracking method and the acquisition protocol to overcome the trade-off in acquisition time and tracking accuracy. As a case study, we apply this methodology on a human hip joint. CONCLUSION The final protocol (bFFE, TR/TE 3.5/1.1 ms, Flip angle 80 degrees , pixel size 4.7 x 2.6 mm, partial Fourier reduction factor of 0.65 in read direction, SENSE acceleration factor of 2, frame rate = 6.7 frames/s) provides sufficient morphological data for bone tracking to be carried out with an accuracy of 3 degrees in terms of joint angle.

HIP JOINT RECONSTRUCTION AND MOTION VISUALIZATION USING MRI AND OPTICAL MOTION CAPTURE

Patients’ anatomical models are increasingly used for both preoperative planning and postoperative guides. The joints motion visualization opens a new level of understanding and can be a valuable diagnosis tool. We propose here to use optical motion capture to simulate internal motions. The ability to image the articulation dynamically and non-invasively in vivo opens the way to the efficient and accurate design of patient-specific musculoskeletal functional models. The long term of our current project in CO-ME is to improve the success rate of orthopaedic surgeries by providing a new set of tools for anatomical and functional simulation of the full leg. This will help orthopaedists in diagnosing pathologies and in surgical planning.