Lea Sjurine Starck^{1,2}, Erling Andersen^{2,3}, Ondřej Macíček^{4}, Oskar Angenete^{5,6}, Thomas Augdal^{7}, Erlend Hodneland^{2,8}, Radovan Jiřík^{4}, Karen Rosendahl^{9,10}, and Renate Grüner^{1,2,7}

The effect of elastic and affine motion correction in dynamic contrast enhanced MRI ofthe temporomandibular joints in children is investigated. Imaging in children is particularly difficultdue to motion. This hampers DCE-MRI and pharmacokinetic estimations for their potentialdiagnostic value in these children with Juvenile Idiopathic Arthritis with possible TMJ involvement.The relative enhancement curves obtained with different motion correction approaches arecompared with the curves calculated with the Gamma Capillary Transit Time model. It is found thatwhen image registration is applied, a greater number of participants can be analysed. The elasticmotion correction approach outperforms the affine approach.

The DCE-MRI data from the TMJ of 48 children with JIA are included, aged 6-15 years. The data is part of a larger set of data from a still ongoing study. It is part of an extensive imaging protocol acquired at three centres using Siemens Skyra 3T and a 64-channel head coil. A 3D-FLASHsequence was used (TR/TE/FA = 4ms/1ms/9°, image matrix 160x160x16, at 60 points in time with a temporal resolution of about 4s). Using a power injector, the contrast agent Gd-DOTA (Dotarem®) was injected 10s after acquisition start (injection speed 5 mL/s). Regions-of-interest (ROIs), i.e. masks of the left and right TMJ, were manually selected in consensus by three expert paediatric radiologists allowing the extraction of relative enhancement curves from the DCE-MRI data.

Parmacokinetic parameters were estimated in the right and left TMJ in each participant comparing three different pre-processing schemes: i) no motion correction,(non-registered), ii) affine motion correction of the whole image volume using the Elastix software^{ 2,3} (affine), iii) elastic registration over the TMJs using an inhouse developed implementation of elastic registration originally developed for DCE-MRI of the kidneys^{ 4} (elastic). The pharmacokinetic parameters blood plasma flow (Fp ), initial extraction fraction(E), volume of extravascular extracellular space (ve), capillary mean transit time (Tc), width of transit time distribution (α^{-1} ) and bolus arrival time (BAT) were computed using the Gamma Capillary Transit Time (GCTT) model.^{5}

Image registration is of importance in the evaluation of DCE-MRI in the TMJ in children, as seen by the increased smoothness of the time curves post registration and by the better model fit of the model to the data. Elastic registration of the TMJs, outperformed affine registration in that its fit to the GCTT model is better. Image registration will facilitate DCE-MRI analysis also in participants where analysis otherwise is not possible due to large motion artefacts and hence increase the potential diagnostic value of DCE-MRI in Juvenile Idiopathic Arthritis affecting the TMJs.

1. Macíček O. Andersen E, Angenete O, Augdal T, Rosendal K, Jiřík R, Grüner R, ISMRM 2018, Paris.

2. S. Klein, M. Staring, K. Murphy, M.A. Viergever, J.P.W. Pluim, " elastix: a toolbox for intensity based medical image registration," IEEE Transactions on Medical Imaging 2010; 29: 196 - 205.

3. D.P. Shamonin, E.E. Bron, B.P.F. Lelieveldt, M. Smits, S. Klein and M. Staring, & quot; Fast Parallel Image Registration on CPU and GPU for Diagnostic Classification of Alzheimer’s Disease & quot;, Frontiers in Neuroinformatics 2014; 50: 1-15.

4. Hodneland E. et al. Normalized gradient fields for nonlinear motion correction of DCE-MRI time series. Computerized Medical Imaging and Graphics 2014; 38: 202-210.

5. Schabel M. C. A unified impulse response model for DCE-MRI. Magnetic Resonance in Medicine 2012; 68:1632–1646.

Fig.1 Relative enhancement curves (red for left TMJ, green for right TMJ) and GCTT model (blue) for subject TMJDE00010, obtained from non-registered data, as well as data registered with the affine parameter file and the elastic ROI approach.

Fig. 2 A sum of squares was performed to the left and right TMJ to evaluate how the registration methods contribute to a better fit to the GCTT model.

Table 1 The sum of squares for each registration method summed across participants (N=48).

Fig. 4 Boxplot of respective ratio of primary pharmacokinetic parameters from the GCTT. In each plot; non-registered (left), affine registration (right) and elastic registration (middle).