Image-based Analysis of in vivo Knee Dynamics
The aim of this project is the development of MRI-based methods and technologies for the non-invasive in-vivo assessment of mechanical loads in soft tissue structures of the knee joint. Such an in-vivo assessment of soft tissue kinematics in 3D together with their incident loads would pave the way for an understanding of the dynamics of elastic deformations of tendons, ligaments, and menisci in healthy subjects as well as their changes due to pathologies (e.g. after ruptures of the anterior cruciate ligament in young vs. elderly patients). Given the short acquisition times available in the dynamic imaging setup only partial information (low resolution, 2D slices instead of 3D volume) can be acquired. Therefore, specialized reconstruction methods have to be developed to extract the motion of soft tissues from the incomplete data, e.g. by correlating information from high-resolution static images to the partial data. The obtained time-dependent configurations of the structures can then be interpreted as shape trajectories allowing morphometric analysis employing manifold-valued statistical tools.
MRI data
Ultra-short echo-time (UTE) MRI sequences have been developed for imaging of the human knee joint [1, 2]. Based on these techniques and a device developed at Charitè for guided motion of the legs, MRI data have been acquired (see Fig. 1).
Fig. 1: Six time frames of a dynamic MRI sequence.
Image-based analysis of the in vivo knee dynamics
We employ techniques of image registration to assess the movement of the bones and the soft tissue of knee joints in MRI data. Non-linear B-Spline registration (see Fig. 2) is employed using the registration framework Elastix. Additionally, a poly-affine log-euclidean framework is investigated.
Fig. 2: Illustration of B-Spline-based transformation of an image.
B-Spline-based registration of dynamic MR images
We employ B-Spline-based registration to register all adjacent time points of the dynamic MRI sequence (see Fig. 3). Mutual Information is utilized in combination with a local rigidity penalty term which is included in the registration function in order to penalize deformations of rigid objects.
Fig. 3: B-Spline-based transformation of two time points of a dynamic MRI sequence.
Poly-rigid registration of dynamic MR images
As an alternative to B-Spline-based non-linear registration, a poly-rigid registration framework is investigated. In this framework each bone is registered rigidly to the respective time point. In the second step, the rigid transformations will be combined using a Log-Euclidean framework.
Fig. 4: Poly-rigid transformation of each bone individually for two time points of a dynamic MRI sequence.
References
[1] M Krämer, M Maggioni, N Brisson, S Zachow, U Teichgräber, G Duda, J Reichenbach: T1 and T2* mapping of the human quadriceps and patellar tendons using ultra-short echo-time (UTE) imaging and bivariate relaxation parameter-based volumetric visualization. Magnetic Resonance Imaging, 63(11), pp. 29-36, 2019
[2] M Krämer, M Maggioni, C von Tycowicz, N Brisson, S Zachow, G Duda, J Reichenbach: Ultra-short echo-time (UTE) imaging of the knee with curved surface reconstruction-based extraction of the patellar tendon. ISMRM (International Society for Magnetic Resonance in Medicine) 26th Annual Meeting, 2018
